2018 School of science - RMIT University · 2018-06-14 · Applied Physics . Professor Gary Bryant . Mimicking atomic potentials with colloids: ... 3D atomic structure of amorphous

Additional Projects

— 2018 School of science HDR Research Projects

Contents

Click on the project links for more information Applied Chemistry and Environmental Science

Professor Rachel A. Caruso

Hybrid photocatalytic nanomaterials for water purification

Professor Adam Lee

Spatially Orthogonal Nanomaterials for Catalytic Cascades

Professor Ewan Blanch

Investigation of carbohydrate-lectin binding mechanisms as a strategy for treating multi-drug resistant (MDR) bacterial infection

Professor Karen Wilson

Catalyst design for tandem bio-chemo transformations

Dr Ravichandar Babarao

Intelligent Design of Porous Materials for Capture and Release of Oxygen

Dr Samantha Grover

Indonesian Peatland Pestoration Dr Jampaiah Deshetti

Cost-effective porous materials for controlling engine emissions

Applied Physics Professor Gary Bryant

Mimicking atomic potentials with colloids: charged particles with a depletion interaction Rapid measurement of the effects of antimicrobial drug candidates on bacterial motility Effects of vitrification (glass formation) on biological membranes

Associate Professor Jeffrey Crosbie

Synchrotron Microbeam Radiation Therapy

Dr Andrew Martin

3D atomic structure of amorphous materials via electron

diffraction Probing nanoscale disorder in 3D with x-rays

Dr Qiming Zhang

Direct laser writing of neural inspired photonic chips for optical deep learning

Biosciences and Food Technology

Professor Harshan Gill

Transforming waste/low-value herbal trimmings into high value products

Professor Dayanthi Nugegoda

Effects of pollutants on health of seabirds

Professor Ian Macreadie Overcoming reduced proteostatis in ageing with a systems biology approach Production of worm proteins for testing as novel immunosuppressants

Professor Peter Smooker Identifying colonisation factors in Campylobacter jejuni Detection and Identification Procedures for Foodborne Pathogens

Associate Professor Donald Wlodkowic

Environmental cellomics: High-content analysis of programmed cell death and perturbations in cell cycle upon exposure to pollutants

Neurotoxicology: Uncovering mechanisms of developmental neurotoxicity using high- content neurobehavioural phenomics

PhysiOmics-on-a-chip: integrated fluidic and imaging systems for high-throughout phenotypic screening in drug discovery and predictive toxicology

Behavioural Ecotoxicology: Discovering intertwined effects of contaminants on aquatic animal behaviour and metabolism

Dr Kylie Boyce Defining Genetic Causes of Antifungal Drug Resistance

Dr Ravi Shukla

The PRINT Cell: Progressing to technology for cell therapy in diabetes

Computer Science

Professor Mark Sanderson

Predicting user satisfaction on next generation search engine interfaces

Recommender Systems: ensemble learning to mitigate user preference change over time

Professor Zahir Tari

Effective Countering cyber threats over Smart-Grid Infrastructures Low-Latency High-Throughput Computational Models for Heavily Data-Driven Applications in Hybrid Data Centres

Preventing Exfiltration of Sensitive Data by Malicious Insiders or Malwares

Associate Professor Tao Gu

Indoor Positioning with Multiple Antennas on Wi-Fi Human Activity Sensing with Wireless Signals

Characterization of Australian Bioluminescent Dinoflagellates

Associate Professor Sebastian Sardina

Behavior Composition for Smart Manufacturing

Goal Recognition and Deception in Intelligent Agents

Associate Professor Xiuzhen Jenny Zhang

Text mining for recommendation

Dr Zhifeng Bao

Towards an Optimal Facility Deployment in Urban Computing

Dr Jeffrey Chan

Solving Hybrid Learning and Optimisation Problems Next Generation Machine Learning

Learning to optimise

Dr Shane Culpepper

Conversational Information Retrieval Dr Yongli Ren

Dynamic Evaluation of Recommender Systems

Dr Caspar Ryan Reducing Energy Consumption in Computational and Cyber Physical Systems

Dr Flora Salim

Scalable and Transferrable Occupant Behaviour Learning with Multi-Sensor Data from

Multi-region Living Labs

Dr. Xiangmin Zhou Abnormal Event Detection in Big Video Data

Situation-aware Social Recommendation over Big Media Streams

Mathematical Sciences

Professor Andrew Eberhard

Application of Variational Analysis to the Stability\Sensitivity of Optimisation problems

Stochastic Variational Inequalities Methods for Characterizing and Computing Equilibria in Bayesian Games

Professor John Hearne

Optimal routing and scheduling -

heuristics Optimal routing and scheduling

Professor Asha Rao

Dynamics of connections in online campaigning

Associate Professor Melih Ozlen

Emergency facility location and vehicle routing problem

Algorithms and tools for multi-objective mixed integer programming

Associate Professor Marc Demange

Frictional Pebble Games in Graphs and Applications

Colouring circle graphs: interval representation, algorithms and applications Associate Professor John J Shepherd

Modelling of anaerobic digester reactor incorporating delays

Dr Mali Abdollahian

Monitoring and Modelling Neonatal and Maternal Mortality using Multivariate quality control and quality assurance models

Improving the process performance through Fault diagnosis in Multivariate process monitoring and performance analysis for high dimensional data

Dr Arathi Arakala Dissimilarity Vectors as a Paradigm for Biometric Template Protection

Dr Haydar Demirhan

Statistical Approaches to Solar Radiation Modelling

Dr Yan Ding

The influence of blood flow disturbances in the cardiovascular system on atherosclerosis

Modelling the pulsatile transport of blood-borne species in the cardiovascular system

Investigating the influence of arterial wall compliance on the transport of blood-borne species

Modelling the biochemical inflammatory process governing atherosclerosis

Dr Anil Dolgun

A normality test for short-tailed and long-tailed symmetric distributions

Assessing inter-rater agreement based on arcsine transformation

Dr Michael Anthony Nyblom

Counting all self-avoiding walks on a finite lattice strip of varying widths

Dr Yan Wang

Mining tropical cyclones using Market Basket Analysis Techniques Integrated model for joint species distributions

DR229 – PhD (Applied Chemistry ) MR229 – Master of Science (Applied Chemistry)

School of Science HDR Project 2018


Applied Chemistry and Environmental Science – City Campus

Project Description – Photocatalysis makes use of a photoactive material that absorbs light to produce an electron-hole pair and hence can initiate oxidative and reductive reactions with species adsorbed on the photocatalyst surface. This can lead to the breakdown of pollutants in aqueous or gas phases, the generation of fuel (through hydrogen generation from splitting water or the production of hydrocarbons through the reduction of CO2), or antimicrobial reactions killing viruses, bacteria and fungi. In this project, porous, hybrid photocatalytic nanomaterials will be synthesized to maximize the interfacial interaction of the two materials and hence the benefits of the heterojunction. The materials will be characterized to understand the composition and structural effects on the properties of the materials and then assessed for their photocatalytic performance by the breakdown of pollutants in water.

This project will contribute to an Australian Research Council Discovery Project focused on the development of hybrid photocatalysts for water purification and the application of high-throughput approaches to speed up the determination of the composition/structure-property-performance relationships. The candidate will be working with a team of academics from the School of Science and the School of Engineering, as well as collaborators at CSIRO and in Japan. In addition to the wet chemical synthesis used to prepare the materials, the candidate will be trained to make use of a number of characterisation methods including both scanning and transmission electron microscopy, powder X-ray diffraction, gas sorption and spectroscopy techniques. High-throughput approaches to characterisation of the materials and testing the photocatalytic properties will be developed and applied.

It is expected that the successful candidate will have majored in one of the following disciplines: chemistry, chemical engineering, materials science or materials engineering.

Contact Details: To discuss this project further please contact: Prof. Rachel A. Caruso ([email protected]) Office 03.03.13




Applied Chemistry and Environmental Science Discipline - RMIT City

Project Description – Cascade reactions are sequential chemical transformations in which the starting substrate undergoes a reaction whose product becomes the substrate for the next step, and so on, until a stable product is reached. Cascades offer great advantages in respect of process efficiency and waste minimisation. In addition to their intrinsic elegance, cascade reactions thus represent a key green chemical technology by which to reduce the number of manipulations required for the manufacture of pharmaceuticals and fine chemicals.[1] However, catalytic cascades, in which the product of a reaction catalysed by species A undergoes a subsequent, distinct transformation catalysed by a second species B, are hindered by undesired interactions between the initial substrate and the second active species, or indeed between the two catalytic species (e.g. an acid and base).

We recently discovered a revolutionary yet versatile solution to the above problems that enables control over the position of chemically and/or physically different components within three-dimensional architectures.[2] This solution, by which chemically distinct functionalities can be introduced in a spatially orthogonal (mutually exclusive) fashion, offers unparalleled control over sequences of chemical reactions in porous solid catalysts (Fig. 1).

Fig. 1. (left) Spatially orthogonal chemically functionalised Pd macroporous-Pd mesoporous SBA-15 (HAADF-STEM image inset), and (right) catalytic advantage for the aerobic selective oxidation of cinnamyl alcohol.

This PhD project will focus on the creation of new hierarchically ordered, porous architectures [3] from diverse and commercially significant metal oxides for shape/size-selective catalytic cascades including: oxidative amination of allylic alcohols to unsaturated amides; and the selective hydrogenation of furfural, both industrially important reactions for the production of sustainable fine chemicals, agrochemicals and pharmaceutical intermediates from biomass feedstocks. The student will learn a range of synthesis and characterization skills, including electron microscopy, XPS, XRD, DRIFTS, and N2 porosimetry, and have the opportunity to conduct cross-disciplinary research at international synchrotron light sources and during visits to collaborators at the University of Cambridge and Manchester University.

References [1] M.J. Climent, A. Corma, S. Iborra, M.J. Sabater, ACS Catal. 2014, 4, 870. (Review) [2] C.M.A. Parlett, M.A. Isaacs, S.K. Beaumont, L.M. Bingham, N.S. Hondow, K. Wilson, A.F. Lee - Nature

Mater. 2016, 15, 178. [3] C.M.A. Parlett, K. Wilson, A.F. Lee - Chem. Soc. Rev. 2013,42, 3876.

Contact Details: To discuss this project further please contact: Professor Adam Lee ([email protected]) Office +61 03 99252623; Room 3.2.06 Professor Karen Wilson ([email protected]) Office +61 03 99252122; Room 3.2.18.





Project Description – The quest for sustainable resources to meet the demands of a rapidly rising global population while mitigating the risks of rising CO2 emissions and associated climate change, represents a grand challenge for humanity. Biomass offers the most readily implemented and low cost solution for sustainable transportation fuels, and the only non-petroleum route to organic molecules for the manufacture of bulk, fine and speciality chemicals and polymers. [1] Bio-derived chemicals can be employed as ‘drop-in’ replacements of existing fossil derived chemicals or as new products that may offer enhanced properties for use in high performance materials polymers and coatings. Tandem synthetic pathways, inspired by natural metabolic pathways of organisms can allow multi-step conversions to take place in one reaction vessel, and are viewed as a potentially step-changing approach for the selective production of new platform chemicals from biomass. Multi-enzyme ‘one-pot’ bio-catalytic processes are attracting growing interest for their superior process efficiency over whole cell fermentation processes which suffer from product inhibition and/or cell death at substrate concentrations required to make processes industrially viable. [2] Recently a 5 enzyme cascade has been proposed as a potential

route to produce malic acid (a valuable succinic acid derivative) from glucose in a “one-pot” route outlined in Scheme 1. Malic acid is used widely used in the food industry, but its potential as a versatile platform chemical has been largely overlooked. [3] The potential of a malic acid platform could be unlocked via the development of tailored heterogeneous catalysts for its transformation, which requires advances in materials chemistry, nanoscience, surface chemistry and process engineering. The production of 2,4 dihydroxybutyric acid (2,4 DHBA) from malic acid is one such example, which is proposed as an advanced additive for curing coatings that offers superior hydrogen bonding characteristics over currently utilised petroleum derived additives.

This PhD project will focus on the development of a heterogeneous catalyst for the selective hydrogenation of malic acid, derived from an enzymatic cascade, to 2,4 dihydroxybutyric acid (and its associated lactone). Control over selectivity of the hydrogenation process will require careful tuning of the metal nanoparticle size, shape and composition and morphology of the support. Pore dimensions of the nanoporous support will also be investigated to exploit confinement effects [4] that will direct the orientation (and thus malic acid coordination), thereby avoiding undesirable side reactions. By tuning the support surface polarity and ensuring selective in-pore deposition of nanoparticles we anticipate fine control over the binding of malic acid and thus chemo-selective hydrogenation.

References 1. Wilson, K. & Lee, A.F. Phil. Trans. R. Soc. A, 374, 20150081, (2016). 2. Filice, M. & Palomo, J.M. ACS Catal. 1 88 1 8 (201 ) 3. Kumar, R.V., Pakshirajan, K. & Pugazhenthi, G., Platform Chemical Biorefinery, Ch 9, 159–179, (2016). 4. Durndell, L.J., Wilson, K. & Lee, A.F., RSC Adv., 5, 80022-80026, (2015).


Scheme 1: Tandem process for the production of 2,4 dihydroxybutyric acid from glucose.




Applied Chemistry and Applied Biology & Biotechnology –City/Bundoora Campuses

Project Description – Emergence of multi drug resistant (MDR) bacteria is an enormous and growing threat facing the world. Particularly vulnerable are immunocompromised patients exposed to opportunistic bacteria such as Pseudomonas aeruginosa and Burkholderia cenocepacia. New approaches to combat this global problem involve exploitation of host-pathogen interaction processes not targeted by current drugs. Carbohydrate- recognizing proteins (lectins) of many pathogens, including P. aeruginosa and B. cenocepacia spp., use host cell surface carbohydrates (glycans) for adhesion. Thus, lectins represent novel targets for interference in the infection process. Our hypothesis is that the consideration of an ensemble of multiple binding modes will reveal detailed structural mechanisms of lectin-carbohydrate recognition not achievable by other methods. The main goal of this project is to delineate the structural requirements of carbohydrate recognition by lectins from opportunistic bacteria: PA-IL and PA-IIL (P. aeruginosa), BC2L-C (B. cenocepacia), and BambL (Burkholderia ambifaria). Translation of the fundamental information on dynamic carbohydrate recognition will be in the development of a carbohydrate-centric virtual screening (VS) approach that will be applied to identify small molecules with anti-adhesive properties. The specific aims of this project are to: (1) examine conformational properties of free and bound carbohydrate ligands using advanced spectroscopic techniques, to determine the basis for recognition by lectins, and (2) use this information to develop a carbohydrate-centric VS approach to screen large available chemical libraries to identify novel

References 1. Dingjan T, Spendlove I, Durrant LG, Scott AM, Yuriev E, Ramsland PA (2015): Structural biology of

antibody recognition of carbohydrate epitopes and potential uses for targeted cancer immunotherapies. Mol. Immunol. 67, 75.

2, Mutter ST, Zielinski F, Cheeseman JR, Johannessen C, Popelier PLA, Blanch EW (2015): Conformational dynamics of carbohydrates: Raman optical activity of D-glucuronic acid and N-acetyl-D-glucosamine using a combined molecular dynamics and quantum chemical approach. Physical Chemistry Chemical Physics 17, 6016.

3. Soliman C, Walduck AK, Yuriev E, Richards JS, Cywes-Bentley C, Pier GB, Ramsland PA. (2018) Structural basis for antibody targeting of the broadly expressed microbial polysaccharide poly-N-acetyl glucosamine. J. Biol. Chem. in press. DOI:10.1074/jbc.RA117.001170.

Contact Details: To discuss this project further please contact:

Prof Ewan Blanch ([email protected]) Associate Professor Paul Ramsland ([email protected])

mailto:[email protected]


DR229 – PhD (Applied Chemistry) MR2229 – Master of Science (Applied Chemistry)



Applied Chemistry and Environmental Science / Applied Porous Materials Team (CSIRO)

Project Description – An exciting opportunity is available for an outstanding PhD candidate to work on an ARC funded project on design of hybrid porous materials for capture and release of oxygen. The past decade has seen the emergence of metal organic frameworks (MOFs) or porous coordination polymers (PCPs) as potential candidate materials across a plethora of applications [1, 2]. MOFs are crystalline materials built from metal ions or clusters bridged by organic linkers to form one-, two- or three-dimensional structures. MOFs are porous materials that hold the world record for specific surface area and storage of gases. MOF research has become one of the fastest growing fields in materials science. More than 20,000 different MOFs have been reported and studied within the past decade and nearly 130,000 hypothetical MOFs have been designed based on different metal clusters and ligands [3]. Recent evidence has indicated that the MOF can be tuned to capture oxygen over nitrogen. However, significant new chemistry is needed to develop new MOF materials with enhanced performance for selectively capturing oxygen over nitrogen in designing portable oxygen concentrators [4]. Understanding these issues is critical for MOF manufacturing, processing and performance. This project will focus on fundamental understanding of what makes MOFs stable in realistic conditions using state of the art computational techniques [5] and to exploit this new understanding to guide development of robust porous materials for capturing oxygen. We seek chemistry/physics/engineering graduates who have some background in any of the following: atomistic modelling, in silico design of materials, programming, high-throughput screening, Monte Carlo methods and first principles calculations. The project involves close collaboration with experimentalist in the field of nanomaterials and device fabrication. Specifically, the project involves: (i) Develop in silico screening tool for pre-screening thousands of materials for capturing oxygen. (ii) Develop new descriptors based on the structure–property relationship to identify materials that merit experimental characterisation. (iii) Use a combinatorial approach integrating modelling and chemical experiments to speed up the design cycle from laboratory testing to industrial use. (iv) Develop understanding of the stability of MOF materials in realistic conditions to find the best, robust materials for large-scale synthesis.

References [1] J. Jiang et al. Chemical Society Reviews 40 (2011) 3599. [2] M. Rubio-Martinez et al. Chemical Society Reviews 46 (2017) 3453. [3] D. J. Xiao et al. J. Am. Chem. Soc. 138 (2016) 7161. [4] A. J. Howarth et al. Nature Reviews, Materials 1 (2016). [5] J. D. Evans et al. Chemistry of Materials 29 (2017) 199.


Dr Ravichandar Babarao ([email protected]) – Office 3.2.03 A/Prof Matthew Hill ([email protected]) – New Horizon Building, Clayton



Indonesian Peatland Pestoration

Applied Chemistry – City Campus Project Description –

Smoke haze has become a major issue in South East Asia in recent decades, negatively impacting on public health and the economy of several countries in the region. The majority of smoke haze is derived from intentional and accidental anthropogenic burning of forest and peatlands. To address the smoke haze crisis, the Government of Indonesia created the Badan Restorasi Gambut (BRG, or Peatland Restoration Agency) in 2016 with the mandate to restore 2.4 M Ha of peat by 2020. Peatland restoration also reduces greenhouse gas emissions, as drained tropical peatland is a globally significant source of CO2 through oxidation processes and burning.

Peatland restoration, which involves rewetting, revegetation and revitalisation of livelihoods1, has not been attempted in tropical regions on a large scale. This project aims to support the biophysical transition that will need to occur for effective and equitable restoration of this peatland, and ensure the continued wellbeing of the communities that are dependent on it.

References 1. Anil, J, Nandakumar, K & Nagar, A 2007, ‘Biometric Template Security’, Department of Computer

Science and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI 48824, USA.

2. E. Pekalska[ and R. P. W. Duin, The Dissimilarity Representation for Pattern Recognition- Foundations and Applications, ser. Series in Machine Perception and Artificial Intelligence. World Scientific, 2005.

3. K. Riesen and H. Bunke, “Approximate graph edit distance computation by means of bipartite graph matching,” Image and Vision Computing, vol. 27, no. 7, pp. 950–959, 2009..


Dr Samantha Grover ([email protected])




Cost-effective porous materials for controlling engine emissions Applied Chemistry and Environmental Science – City

Project Description Advanced combustion engines are being developed to meet higher standards of fuel efficiency and lower greenhouse gas emissions. However, the commercial potential is depending on meeting emission standards for the control of serious pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons, and particulate matter. The pollutants are serious to the atmosphere, and most concern is due to their negative impact on cardiovascular health [1]. With increasing demands from the environmental organizations, the control of these pollutants is the urgent task today. Several exhaust gas after treatment technologies were employed to control the emissions using filters in automobile engines [2, 3]. Three way catalysis (TWC) namely, catalytic CO oxidation, NOx reduction, and hydrocarbons oxidation has been extensively used to control pollutants during combustion. Various catalysts such as precious metals, alkali metal oxides, spinel oxides, and transition metal oxides have been intensively investigated for TWC application. However, these oxides are relatively unstable, require high temperatures (>350 °C), and possess ill-defined particle structure. Therefore, the lower exhaust temperatures of advanced engines require catalysts that are active at <150 °C to meet future emission regulations.

A porous network of nanomaterial is an interesting phenomenon in gas adsorption and catalytic oxidation reactions as it provides a facile pathway for the reactants to be actively involved in the oxidation reaction. Surface area is also a crucial issue, and it needs to be considered when metal oxides used in catalysis. In this project, the PhD student will work on designing nanostructured porous metal oxides by wet chemistry approaches such as co-precipitation, hydrothermal, sol-gel, and solvothermal synthetic methods. The various other parameters related to the synthetic schemes would also be taken into account e.g. factors determining nanoparticle monodispersity, size tunability, yield, effect of solvent, pH, temperature etc. Structural, surface, redox properties of these materials will then be recorded and the effect of changing of different parameters will be evaluated. A characterization scheme using the state of the art techniques, namely, XRD, N2 BET Surface area, SEM, TEM, HR-TEM, TPR, TPD, FT-IR, RAMAN and XPS will be employed to fully characterize the structural, surface, and redox properties of the as-prepared catalysts. Using TGA instrument and GC analysis, the student will study the catalytic activities of porous materials. References 1. S. Chen, L. Luo, Z. Jiang, W. Huang, , ACS Catalysis, 5 (2015) 1653-1662

2. D. Jampaiah, V.K. Velisoju, P. Venkataswamy, V.E. Coyle, A. Nafady, B.M. Reddy, S.K. Bhargava, ACS Applied Materials & Interfaces, 9 (2017) 32652-32666

3. James E. Parks Science 327 (5973), 1584-1585.


Dr Jampaiah Deshetti (Email: [email protected]) Professor Suresh Bhargava (Email: [email protected]) Dr Yan Wang (Email: [email protected])

DR230 – PhD (Applied Physics) MR230 – Master of Science (Applied Physics)


Mimicking atomic potentials with colloids: charged particles with a depletion interaction

Physics, School of Science, City Campus

PhD or Masters Project Project Description – Colloidal suspensions provide model for understanding the fundamentals of crystallization and glass formation in atomic systems. As the inter-particle distance is ~ 100s of nm, light scattering can be used to study such systems. As the motions are Brownian rather than ballistic, kinetics and dynamics can be studied in real time [1, 2].

To date, there have been extensive studies of 3 model systems: repulsive hard spheres [3]; repulsive charged particles [4]; and repulsive hard spheres with a depletion attraction [5]. However, to date there have been no studies of systems with a short range attraction combined with a long range repulsion – ie something that mimics real atomic systems, such as the Lenard-Jones potential. This project aims to develop such a model system using PMMA-based nanoparticles suspended in water or water/DMSO mixtures. The project will have two aspects:

1) Masters project: preparing and characterizing a novel sterically stabilized charged PMMA nanoparticle in the size range 100-150 nm, and developing a suitable polymer system to provide the depletion effect, then characterizing the phase behaviour of the system. This will be done using light and X-ray scattering. Would result in 2 high quality publications.

2) PhD project: as above, plus a detailed study pf particle dynamics using light scattering, and detailed structural characterisation of the crystalline phases using Small Angle X-ray and Neutron Scattering. This would result in an additional 2-3 high quality publications.

The primary techniques include Dynamic and Static Light Scattering and Differential Dynamic Light Scattering. For the PhD project, Small angle X-ray and Neutron scattering (SAXS & SANS) will be conducted at the Australian Synchrotron and at Lucas Heights in Sydney).

References 1. Schope, H.J., G. Bryant, and W. van Megen, Two-step crystallization kinetics in colloidal hard-sphere

systems. PHYSICAL REVIEW LETTERS, 2006. 96: p. 175701. 2. Martinez, V.A., G. Bryant, and W. van Megen, Slow dynamics and aging of a colloidal hard sphere

glass. PHYSICAL REVIEW LETTERS, 2008. 101(13): p. 135702. 3. van Megen, W., V.A. Martinez, and G. Bryant, Scaling of the Space-Time Correlation Function of

Particle Currents in a Suspension of Hard-Sphere-Like Particles: Exposing When the Motion of Particles is Brownian. PHYSICAL REVIEW LETTERS, 2009. 103(25): p. 258302.

4. Palberg, T., Crystallization Kinetics of Repulsive Colloidal Spheres. Journal of Physics: Condensed Matter, 1999. 11(28): p. R323-R360.

5. Pham, K.N., et al., Glasses in hard spheres with short-range attraction. Physical Review E, 2004. 69(1).

Contact Details: To discuss this project further please contact: Prof Gary Bryant ([email protected]) Supervisor – Office 14.7.04A Bill van Megen ([email protected]) Supervisor – Office 14.7.04A



Rapid measurement of the effects of antimicrobial drug candidates on bacterial motility


PhD or Masters Project Project Description – Antimicrobial drug resistance is a global health emergency and there is an urgent need for new clinical antimicrobial agents that work in different ways to current drugs, and are therefore more likely to be effective (1). One mechanism of particular interest is motility, the ability of bacteria to explore their environment and spread, as this is how bacteria spread and grow. Despite their enormous potential, antimicrobial drugs that prevent motility have received little attention because of the limitations of traditional techniques to measure bacterial movement (2)). These measurements are critical for assessing the effectiveness of potential drugs.

This project we will apply the new technique of Differential Dynamic Microscopy (DDM) to assess the effectiveness of potential candidate antimicrobial molecules, and understand how such molecules affect motility. DDM (3) was first used to study bacterial motility by the Soft Matter group at Edinburgh University, in collaboration with CI Bryant at RMIT (4). Since then, the Edinburgh group have proved the utility of the technique using E. Coli (5), the bacteria responsible for maladies such as gastroenteritis, meningitis in newborns, pneumonia and urinary tract infections. Bryant’s group at RMIT has recently applied the technique to understand the role of motility in the effects of carbohydrate-based surfactants on bacterial growth, in collaboration with PIs Bansal and Wilkinson (6).

This project will extend this work to look at motility in a range of organisms, and the effects of both existing and novel antibiotics on bacterial motility.

The project could be at either Masters of PhD level, with the Masters level project focussing on one bacterial species.

References 1. Carlet J, Collignon P, Goldmann D, Goossens H, Gyssens IC, Harbarth S, et al. Society's failure to

protect a precious resource: antibiotics. The Lancet.378(9788):369-71. 2. Berg HC. The rotary motor of bacterial flagella. Annual Review of Biochemistry. 2003;72:19-54. 3. Cerbino R, Trappe V. Differential dynamic microscopy: Probing wave vector dependent dynamics with

a microscope. Physical Review Letters. 2008;100(18). 4. Wilson L, Martinez V, Schwarz-Linek J, Tailleur J, Bryant G, Pusey P, et al. Differential Dynamic

Microscopy of Bacterial Motility. Physical Review Letters. 2011;106(1). 5. Schwarz-Linek J, Arlt J, Jepson A, Dawson A, Vissers T, Miroli D, et al. Escherichia coli as a model

active colloid: A practical introduction. Colloids and surfaces B, Biointerfaces. 2016;137:2-16. 6. Hu, Y, Zou, W, Julita, V, Ramanathan, R, Tabor, RF, Nixon-Luke, R, Bryant, G, Bansal, V and

Wilkinson, BL. Photomodulation of bacterial growth and biofilm formation using carbohydrate-based surfactants. Chemical Science. 2016;7:6579 (Cover Image).

Contact Details: To discuss this project further please contact: Prof Gary Bryant ([email protected]) Supervisor – Office 14.7.04A



Effects of vitrification (glass formation) on biological membranes Physics, School of Science, City Campus

PhD or Masters Project Project Description – In nature, many species of plants and animals have evolved to avoid membrane damage during freezing and dehydration by vitrifying – ie forming a glass from the concentrated sugar suspensions1. Early studies of the effects of sugars concentrated on the effects of non-vitrified glasses, though there were attempts to understand and model the effects of vitrified glasses2. These studies examined the effects membrane transition temperatures, but not on the membrane structure, due to limitations in the available technology. Over the past few years we have pioneered the study of the effects of various molecules on membrane structure using advanced scattering techniques3-5.

While initial studies of the effects of glasses on membranes In this project we will study thIn this project we will apply these advanced techniques to the study of the effects of glass formation on membrane structure, as well as studying the effects of ice crystallization.

The primary lab based techniques to be used include Small angle X-ray scattering (SAXS) and Differential Scanning Calorimetry (DSC). We will also investigate the properties of the sugar glasses themselves using Powder X-ray Diffraction, DSC and Dynamic Mechanical Analysis (DMA). In addition, we will conduct synchrotron SAXS studies (at the Australian Synchrotron) and neutron scattering studies (at Lucas Heights in Sydney and at overseas facilities), using molecular deuteration to fine tune the location of molecules within the membrane-glass system. As well as studying traditional glass formers, we will study the effects of novel crystallization inhibitors and vitrification agents.

The project could be at either Masters of PhD level, with the Masters level project focussed on lab based measurements.

References 1. Koster, K. L.; Anderson, M., Interactions between vitrified sugars and lipid - mechanism by which the

fluit-to-gel phase-transition temperature is lowered. Plant Physiology 1995, 108 (2), 110-110. 2. Koster, K. L.; Lei, Y. P.; Anderson, M.; Martin, S.; Bryant, G., Effects of vitrified and nonvitrified

sugars on phosphatidylcholine fluid-to-gel phase transitions. Biophysical Journal 2000, 78 (4), 1932- 1946.

3. Garvey, C. J.; Lenne, T.; Koster, K. L.; Kent, B.; Bryant, G., Phospholipid Membrane Protection by Sugar Molecules during Dehydration-Insights into Molecular Mechanisms Using Scattering Techniques. International Journal of Molecular Sciences 2013, 14 (4), 8148-8163.

4. Kent, B.; Hunt, T.; Darwish, T. A.; Hauss, T.; Garvey, C. J.; Bryant, G., Localization of trehalose in partially hydrated DOPC bilayers: insights into cryoprotective mechanisms. Journal of the Royal Society Interface 2014, 11 (95).

5. Kent, B.; Hauss, T.; Deme, B.; Cristiglio, V.; Darwish, T.; Hunt, T.; Bryant, G.; Garvey, C. J., Direct Comparison of Disaccharide Interaction with Lipid Membranes at Reduced Hydrations. Langmuir 2015, 31 (33), 9134-9141

Contact Details: To discuss this project further please contact: Prof Gary Bryant ([email protected]) Supervisor – Office 14.7.04A



Synchrotron Microbeam Radiation Therapy

Applied Physics – City campus Project Description

The profound burden of cancer remains with an estimated 1 in 3 people facing the disease in their lifetime. Cancer remains one of the world’s leading causes of death, responsible for almost 8 million deaths in 2008, according to figures from the World Health Organisation [1]. As such, research into new anti-cancer techniques continues apace. Approximately 50% of all cancer patients receive radiotherapy [2]. Despite radiotherapy’s effectiveness, the dose delivered to a tumour is limited by the dose that can be tolerated by surrounding, normal tissues. Certain cancers represents a significant challenge to radiotherapy because some of the body’s most radiosensitive organs (e.g. the intestines/gut, lung, brain) are in close proximity to the tumour. Synchrotron microbeam radiation therapy (MRT) [1,2] is an exciting pre-clinical radiotherapy technique that involves fractionating the dose in space rather than in time. MRT has shown great efficacy in tumour-bearing rodent models with remarkable sparing of normal tissue [3]. For the MRT technique, the high flux X-ray beam from the synchrotron is segmented into a lattice, of narrow micro-planar beams, typically 25-50 μm wide and separated by 200 -400μm. Typical radiation doses are 300-800 Gray (Gy) in the beam (peak dose), and 5-20 Gy in the valley between the beams. At a critical collimated beam width and separation, on the order of tens and hundreds of microns respectively, normal tissue is able to recover from these traditionally presumed lethal levels of exposure, whilst the cancerous cells within the tumour are destroyed. The exact underlying process which drives this remarkable recovery of normal tissue is still unknown. Currently, MRT is only possible at a small number of synchrotron facilities world-wide, including the Australian Synchrotron in Melbourne. Conventional X-ray sources do not possess the necessary physical properties of very high flux rate with minimally divergent beams to make MRT a possibility. The imaging and medical beam line (IMBL) at the Australian Synchrotron (AS) in Melbourne has created an outstanding opportunity for world leading MRT research to be undertaken in Australia. The AS has prioritized MRT research as one of the areas for support, since this is in line with the AS priority of developing the medical beam line for patient use. The aim of this particular PhD project is to make scientific advances around a number of medical physics topics vital for eventual clinical trials. These topics include; X-ray dosimetry, computerized treatment planning systems, radiobiology studies, image guidance for synchrotron radiotherapy, patient positioning, and record and verify processes. References

1. Smyth, L. M., Senthi, S., Crosbie, J. C. & Rogers, P. A. The normal tissue effects of microbeam radiotherapy: What do we know, and what do we need to know to plan a human clinical trial? Int J Radiat Biol 92, 302-311, (2016).

2. Poole, C. M., Day, L. R. J., Rogers, P. A. W. & Crosbie, J. C. Synchrotron microbeam radiotherapy in a commercially available treatment planning system. Biomedical Physics & Engineering Express 3, 025001, (2017).

3. Lye, JE, Harty PD, Butler DJ, Crosbie JC et al. Absolute dosimetry on a dynamically scanned sample for synchrotron radiotherapy using graphite calorimetry and ionization chambers. Phys Med Biol 61, 4201-4222, (2016).

4. Livingstone J, Adam JF, Crosbie JC, et al. Preclinical radiotherapy at the Australian Synchrotron's Imaging and Medical Beamline: instrumentation, dosimetry and a small-animal feasibility study. J Synchrotron Radiat. 2017 Jul 1;24(Pt 4):854-865.

Contact Details

To discuss this project further please contact: Associate Professor Jeffrey Crosbie ([email protected])



3D atomic structure of amorphous materials via electron diffraction

Physics Discipline – RMIT City

Project Description – The atomic structure of an amorphous material, like a glass, is a seemingly random atomic network. However, these materials are only truly random on length scales larger than a few nanometres. The local environment around any given atom can be highly ordered by bonding. It is the local structure that ultimately determines the properties of amorphous materials. However, disordered structures are also extremely difficult to measure because the long-range randomness limits electron or x-ray characterization methods to 1D1. As a consequence, there is much we do not know about the structural origins of amorphous material properties. For example, the search for a microscopic explanation of the glass transition, whereby a supercooled liquid suddenly becomes solid without crystallizing, remains one of the biggest outstanding challenges in condensed matter physics2.

This Project is based on a new theory3 to characterise the local structure of amorphous materials in 3D with electron scattering. The new method exploits the statistics of large datasets collected by scanning a focused electron probe over the material. The goal of this Project is to develop the new 3D technique experimentally and to make some of the first demonstrations with amorphous solids using the high-resolution electron microscopes at the RMIT Microscopy and Microanalysis Facility.

The Project involves data analysis, electron microscopy and numerical simulation. It would suit a candidate who is interested in learning both simulation and experimental methods.

References 1. Eliot, S. R. Physics of Amorphous Materials. (Longman, 1983). 2. Binder, K. et al. Glassy Materials and Disordered Solids. (World Scientific Publishing, 2011). 3. Martin, A. V. Orientational order of liquids and glasses via fluctuation diffraction. IUCrJ 4, 24, (2017)

Contact Details: To discuss this project further please contact: Dr. Andrew Martin ([email protected]) Supervisor – Office 14.6.11



Probing nanoscale disorder in 3D with x-rays


Project Description – Disordered matter is ubiquitous in the world we live in, from the glass in our windows to the polymers in our plastics to living biological systems. However, disordered matter is as mysterious as it is common. For example, we lack a microscopic explanation of the glass transition, whereby a supercooled liquid suddenly becomes solid without crystallizing1. More generally, it is extremely difficult to predict the properties of disordered materials because we do not know their atomic structures accurately. The problem is that disordered materials scatter x-rays and electrons randomly, obscuring local order and producing a 1D characterisation only (atomic pair distance statistics)2.

This Project is based on a new theory3 that enables 3D statistics of disordered nanostructure to be measured. The goal of this Project is to develop x-ray scattering techniques for 3D nanostructure characterization for use at the Australian Synchrotron. In a collaboration with chemistry, biology and the Australian Synchrotron, the candidate will work on applications of the new technique to soft matter systems, such as the lipidic cubic phase which is highly topical for membrane protein crystallization and drug delivery.

There is also scope within the Project to develop new x-ray techniques for x-ray free-electron lasers. These remarkable new international facilities are a billion times brighter than synchrotron sources and produce femtosecond pulses enabling snapshot measurements of dynamic samples like liquids. There are exciting possibilities for using the new 3D methods to study supercooling and phase transitions.

The Project involves data analysis, x-ray experimentation and numerical simulation. It would suit a candidate who is interested in learning both simulation and experimental methods.

References 1. Binder, K. et al. Glassy Materials and Disordered Solids. (World Scientific Publishing, 2011). 2. Eliot, S. R. Physics of Amorphous Materials. (Longman, 1983). 3. Martin, A. V. Orientational order of liquids and glasses via fluctuation diffraction. IUCrJ 4, 24, (2017)





Laboratory of Artificial-Intelligence Nanophotonics

Project Description – Deep learning without supervision has transformed the field of artificial intelligence, but the limitations of conventional computer hardware are hindering progress [1]. Nanophotonic chips could be the answer by carrying out deep learning at the speed of light. Although photonic chips have long been used to speed data transfer, non-volatile memories is required to integrate the nanophotonic chips to achieve deep learning without supervision. This project will involve the fabrication of photonic chip with long lifetime memory for on-chip optical deep learning. This is enabled by the direct laser writing technique with nanometer resolution in three dimensions that developed by our group [2].

This project will focus on the direct writing of three dimensional photonic chips with structure inspired by neural networks. The memory at nanoscale will be induced by the femtosecond laser interaction with nanomaterials [3] such as graphene and nanodiamonds in the as formed photonic chips. This is based on our recent development of optical memories enabled by direct laser writing [3], [4]. Optical memory at nanoscale will be induced by laser at deterministic and on-demand in the photonic chips. Characterization of the memory after laser fabrication will be carried out from the optical and optoelectronic point of view.

Training on the use of femtosecond laser and imaging systems will be required via attending the Laser Safety Course and by the Supervisors.

References [1]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. [2]. Gan, Z., Cao, Y., Evans, R. A., & Gu, M. (2013). Three-dimensional deep sub-diffraction optical beam lithography with 9 nm feature size. Nature communications, 4, 2061. [3]. Gu, M., Zhang, Q., & Lamon, S. (2016). Nanomaterials for optical data storage. Nature Reviews Materials, 1, 16070. [4]. Gu, M., Li, X., & Cao, Y. (2014). Optical storage arrays: a perspective for future big data storage. Light: Science and Applications, 3(5), e177.

Contact Details: To discuss this project further please contact: Professor Min Gu ([email protected]) Senior Supervisor – Office Building 91 Dr Qiming Zhang ([email protected]) Dr Ben Cumming([email protected])

Associate Supervisor – Office Builiding 14 Associate Supervisor – Office Builiding 14

DR232 – PhD (Food Science) MR232 – Master of Science (Food Science)



Food Science – Bundoora Campus

Project Description – Culinary herbs are a highly prized cooking ingredient and are used for imparting specific aroma and colour to various dishes, as well as for preserving food. The demand for fresh and ready to use culinary herbs continue to grow worldwide due to the growing influence of cooking shows, popularity of ethnic cuisines, readiness of consumers to try new flavours and increasing knowledge about the health-promoting properties of various herbs.

The Australian culinary herb industry is relatively small but is growing each year at a consistent rate of 5- 10%. To be successful in a globalised market place, it needs to implement more efficient production and pest management systems and develop innovative premium quality products with improved shelf-life. Currently, over 20% of all herbs grown in Australia end up as trimmings or grading out due to specifications required by the retailers and customers. This leads to a significant waste disposal cost and burden on resources.

Thus, there is urgent need to develop new formulations and processing and packaging technologies for transforming waste and low value herbal trimmings into high value, high quality, and shelf-stable products, and reduce post harvest losses.

Therefore this project plans to transform waste herb trimmings and low quality product into high value food products and ingredients. This will include the development of new formulations/recipes, processing protocols, products, shelf-life and safety studies, and sensory and consumer testing.

References 1. Australian Government. National Food Waste Strategy: Halving Australia’s food waste by 2030,

Commonwealth of Australia 2017 [Internet] Canberra (Australia): Australian Government; 2017 [ cited 17/03/2018]; [54 p.] Available from: https://www.environment.gov.au/system/files/resources/4683826b- 5d9f-4e65-9344-a900060915b1/files/national-food-waste-strategy.pdf

2. McCrindle. Food Bank Hunger Report 2017, FoodBank 2017 [Internet] Australia: McCrindle; 2017 [ cited 17/03/2018]; [20 p.] Available from: https://www.foodbank.org.au/wp- content/uploads/2017/10/Foodbank-Hunger-Report-2017.pdf

3. Department of the Environment and Energy. Canberra (Australia): Tackling Australia’s food waste; 2017 [cited 19/02/2018]. Available from: http://www.environment.gov.au/protection/national-waste- policy/food-waste

4. Australian Government.Working together to reduce food waste in Australia, Commonwealth of Australia 2017


Prof Harsharn Gill ([email protected]) Dr Jessica Danaher ([email protected]) Dr Lisa Newman ([email protected])

http://www.environment.gov.au/system/files/resources/4683826b-

http://www.foodbank.org.au/wp-

http://www.environment.gov.au/protection/national-waste-


DR231 – PhD (Applied Biology/Biotechnology) MR231 – Master of Science (Applied Biology/Biotechnology)



Biotechnology and Biological Sciences – Bundoora

Project Description – There is increasing evidence that endocrine disruption from exposure to chemical pollutants impacts wildlife including birds on a global scale (Sumpter, 2003, Nordstad 2012, Tartu et al. 2014). Alterations in wildlife development, reproduction, and behaviour that coincide with the presence of endocrine disrupting chemicals (EDCs) have been widely reported (Colburn, et al. 1993, Damstra, et al. 2012). Persistent organic pollutants (POPs) and Mercury (Hg) are recognised EDCs (Kibria et al 2010) that are bioaccumulated and bioconcentrated up trophic chains with detrimental effects on top predators. There is no research linking effects of bioaccumulated POPS or Hg in Australian seabirds with their endocrinology and health. Finger et al. (2016) established that little penguins (Eudyptula minor ) in a site in Melbourne had detectable concentrations of Hg in their tissues with an increasing trend over 3 years from 2011- 2013; while resident penguins in this city location had higher blood and feather Hg concentrations than those from a remote island (Finger et al. 2015). These studies did not investigate effects on the endocrine function or health of the birds. Professor Dayanthi Nugegoda has a current collaborative SWRRFI grant to study pollutant body burdens and hormone levels in shearwaters from Lord Howe Island. The current research project will similarly evaluate pollutant body burdens in relation to health indicators including endocrinology and reproduction in Little Penguins in Phillip Island Nature Park. The project will be co-supervised and field support provided by research staff from the Park.

References Colburn, T., F. S. vom Saal and A. M. Soto (1993). "Developmental effects of endocrine-disrupting chemicals

in wildlife and humans." Environ Health Perspect 101: 378-384. Damstra, T., S. Barlow, A. Bergman, R. Kavlock and G. Van Der Kraak (2012). "Global Assessment of the

State-of-the-Science of Endocrine Disruptors." WHO/PCS/EDC.World Health Organization, Geneva, Switzerland.

Finger, A. Lavers, J. L. Orbell, J. D. Dann, P.,Nugegoda, D. and Scarpaci, C. (2016) Seasonal variation and annual trends of metals and metalloids in the blood of the Little Penguin (Eudyptula minor) Mar. Pollut. Bull., 110, 261-273.

Finger, A. Lavers, J. Dann, P. Nugegoda, D. Orbell, J. Robertson, B. and Scarpaci C.( 2015), The Little Penguin (Eudyptula minor) as an indicator of coastal trace metal pollution, in Environmental Pollution, 205, 365-377

Nordstad T, Moe B, Ove Bustnes J, Bech C., Chastel O, Goutte A, Sagerup K, Trouvé C, Herzke D T, Gabrielsen G.W (2012) Relationships between POPs and baseline corticosterone levels in black-legged kittiwakes (Rissa tridactyla) across their breeding cycle. Environmental Pollution 164, 219-226

Tartu S, Angelier F, Herzke D, Moe B, Bech C, Gabrielsen G W., Bustnes J O, Chastel O. (2014) The stress of being contaminated? Adrenocortical function and reproduction in relation to persistent organic pollutants in female black legged kittiwakes. Science of The Total Environment, 476-477, 553.

Sumpter J.P. 2003. Endocrine disruption in wildlife: The future? Pure and Applied Chemistry 75, 2355-2360.

Contact Details: To discuss this project further please contact: Professor Dayanthi Nugegoda ([email protected].) Supervisor – Office: B223 F01 R044



Overcoming reduced proteostatis in ageing with a systems biology approach

Discipline of Biosciences and Food Technology Bundoora

PhD Project Project Description – This project will utilize yeast expressing deleterious proteins, such as amyloid beta, that accumulate with ageing in humans and in yeast. The ageing in yeast will be assayed in a systems biology approach to examine ways in which proteostasis can be improved in aging cells.

A substantial contributor to ageing is a decline in proteostasis. The impact of this is particularly obvious in the brain and neuronal cells where loss of function and cell death may result from the lack of clearance of proteins. The Alzheimer's amyloid beta protein accumulates with age and its toxicity is associated with neuronal death. In yeast it can be observed that amyloid beta also accumulates with age and is toxic. Studies of GFP-labelled amyloid beta in yeast have provided a convenient opportunity to study cellular mechanisms to overcome toxicity and to study compounds that overcome toxicity [1,2]. Increasing autophagic turnover of proteins is one mechanism to reduce problematic proteins of ageing. Some compounds enable old cells to do this more effectively, and it is of interest to examine ingredients in foods that may also act in this manner. It can be inferred that improving proteostasis will delay the incidence of Alzheimer's disease, Parkinson's disease, and possibly other diseases where the accumulation of toxic proteins is highly deleterious to the brain.

References [1] Porzoor and Macreadie. Systems biology of Alzheimer’s disease: Methods and protocols. (Castrillo JI and

Oliver SG ed.), Humana Press Ch 12: 217-226 (2015). [2] Porzoor et al. Biomolecules 5: 505-527 (2015).

Contact Details: To discuss this project further please contact: 1st Supervisor –Prof Ian Macreadie, Bundoora 223.1.28C 2nd Supervisor – A/Prof Benu Adhikari, Bundoora 201.6.5



Production of worm proteins for testing as novel immunosuppressants Discipline of Biosciences and Food Technology

Bundoora

PhD Project Project Description – This project is multi-disciplinary and a requires joint effort from 3 groups for cloning and yeast expression, protein characterization and purification, and testing in an animal model.

Hookworms suppress the immune system enabling their survival in humans [1, 2]. The ways in which they do this could provide opportunities for new therapeutic interventions in diseases where chronic inflammation is a major problem .e.g. asthma, inflammatory bowel disease, psoriasis, fibromyalgia, etc. Prof Alex Loukas (James Cook University) has identified proteins secreted from hookworms that may suppress the immune system, allowing worms to persist in humans. He has also developed a mouse model for chronic inflammation, including asthma, where the hookwork proteins can be evaluated for their efficacy [3]. Prof Loukas will provide genes coding for these candidate immunosuppressing proteins. The cloning and small scale secretion of these proteins from Pichia pastoris will be performed with Prof Ian Macreadie at RMIT University. Scale up and purification of the proteins will be performed with Adj Prof George Lovrecz at CSIRO. The purified proteins will be tested in mice models of chronic inflammation, under the supervision of Prof Alex Loukas (JCU). JCU will be responsible for ethics approval.

References: [1] McSorly and Loukas Parasite Immuno (2008) 32, 549 doi: 10.1111/j.1365-3024.2010.01224.x. [2] Ruyssers et al. Clin Devel Exp Immunol (2008) 567314 doi: 10.1155/2008/567314 [3] Navaro et al. Science Translational Med. (2016) 8, 362, 362ra143 DOI: 10.1126/scitranslmed.aaf8807


1st Supervisor –Prof Ian Macreadie, Bundoora 223.1.28C 2nd Supervisor – Adj Prof George Lovrecz, CSIRO Clayton and Parkville 3rd Supervisor – Prof Alex Loukas, James Cook University, Cairns

DR232– PhD (Food Science) MR232 – Master of Science (Food Science)


Identifying colonisation factors in Campylobacter jejuni.

BFT, Bundoora West

Project Description – One of the primary organisms causing food poisoning worldwide is Campylobacter, particularly C. jejuni. This is largely due to the consumption of poultry. The bacterium colonises poultry to such high levels that there is inevitably contamination of some meat after processing. If the meat is incorrectly prepared, undercooked etc then human infection can occur.

As a commensal in poultry, Campylobacter must avoid the chicken gut immune system. How it does this is largely unknown. There is presumably a combination of tolerance and active suppression. The molecules that mediate such suppression are unknown. We have identified five molecules that are non-classically secreted from the bacterium that may play a role in this. Previous work using molecular modelling and tissue culture assays indicated one may induce pores in target cells.

This project will use a combination of genome mining, molecular modelling, recombinant protein expression, protein structure determination, and immunology to help to uncover the functions of the molecules. If they are shown to be involved in avoiding the immune system, they are prime targets for immunotherapeutic intervention, which may allow chickens to target C. jejuni, reducing colonization. This would significantly reduce carcass contamination and subsequent human infection.

Contact Details: To discuss this project further please contact: Professor Peter Smooker ([email protected]) Location: 223.1.29, tel 99257129



Detection and Identification Procedures for Foodborne Pathogens Biosciences and Food Technology Discipline – RMIT Bundoora West

Project Description This project aims to compare, contribute and address deficiencies and gaps in orthogonal measurement and confirmation methods for the detection and quantification of foodborne pathogens, including a range of bacteria and foodborne viruses. The research will improve techniques to better protect Australian consumers from foodborne illness and assist the food manufacturing industry in providing its consumers with safe food products. More specifically, this research will: • Compare current methods of detection for Foodborne viruses and develop methods or enhanced sampling plans to improve the detection of viruses, particularly Hepatitis A (HAV), from foods and publish the findings (this research will provide the content for two publications). • Compare modern methods of detection of foodborne pathogenic bacteria using Matrix Assisted Laser Desorption Ionisation - Time of Flight (MALDI-TOF) mass spectrometry and Fourier Transform Infrared microscopy (FTIR) • Establish novel techniques for the detection of foodborne viruses from different matrices (mass spectrometry, FTIR) and publish the findings. • Evaluate and design methods for the direct isolation of foodborne pathogenic bacteria such as Salmonella spp., Listeria spp. and E. coli (among others) from foods with minimal enrichment for identification with the FTIR microscope and publish the findings.

The research will be conducted in collaboration with the National Measurement Institute - Australia (NMIA) and research will jointly be undertaken at the NMI’s Port Melbourne site and RMIT University. The NMIA is the peak Australian measurement body whose vision is to ‘provide measurement policy, science and regulation that underpins the economy and well-being of Australia’.

Contact Details To discuss this project further please contact:

Professor Peter Smooker ([email protected]) Room– 223.1.29, Ph: 99257129 Mrs Prue Bramwell ([email protected]) Room - 223.1.48, Ph: 99257128





PhysiOmics-on-a-chip: integrated fluidic and imaging systems for high-throughout phenotypic screening in drug discovery and predictive

toxicology

Phenomics Laboratory – A/Prof Wlodkowic (Science, Bundoora)

Project Description – Small model organisms such as i.e. C.elegans, D.melanogaster and zebrafish offer some of the most promising alternative and cost-effective biological models for high-throughput predictive toxicology, molecular pathology and drug discovery routines.

Unlike cell lines, small metazoan model organisms offer an intact, multicellular system that models integrative biochemical and physiological processes. At the same time they are amenable to non-invasive, whole-animal in vivo imaging. The latter can be utilized for both single-cell imaging of processes such as e.g. apoptosis as well as imaging of physiological process such as cardiovascular system function, central nervous system development and even imaging of entire organism neurobehavioral responses. Combined with high fecundity, transparency, and small size, such organisms are ideal model systems for chemical screening and drug discovery efforts.

The lack of high-throughput and high-content screening systems available for rapid phenotypic profiling on living small model organisms is currently a significant impediment to discovering more sensitive toxicological endpoints and performing accelerated drug discovery routines.

Given the unmet need for novel drugs, this project seeks to develop innovative Lab-on-a-Chip technologies to non-invasively assess various physiological endpoints using small organisms such as transgenic zebrafish. The new technologies will provide the next generation high-content phenotypical based on non-invasive analysis of biological complexity in living organisms.

Skills learned in this project: - Lab-on-a-Chip technologies design including 3D Computer Assisted Design (3D CAD) - Computational Fluid Dynamics (CFD) in silico modeling - Microfabrication methods incl. photolithography, stereolithography, laser ablation - Metrology incl. optical profilometry and scanning electron microscopy (SEM) - Integration with sensors and actuators to achieve high-throughput automated bioanalysis - Biological assays for validation of chip-based technology performance - Statistical data analysis, report writing and scientific data presentation

Future career opportunities: skills gained in this project can facilitate future careers in: biomedical devices industry, Lab-on-a-Chip R&D, drug discovery, biopharmaceutical industry

References [1] Fuad et al., Sensors and Actuators B: Chemical 256 (2018), 1131-1141. [2] Fuad et al., Biomicrofluidics 11(5) (2017), 051101. [3] Zhu et al., Environmental Science & Technology 49(24) (2015), 14570-14578. [4] Zhu et al., Zebrafish 12(4) (2015), 315-318. [5] Skommer & Wlodkowic, Expert Opinion in Drug Discovery 10(3) (2015), 231-244.

Contact Details: To discuss this project further please contact: A/Prof Donald Wlodkowic ([email protected]) Supervisor Office 223.1.32A



Neurotoxicology: Uncovering mechanisms of developmental neurotoxicity using high-content neurobehavioural phenomics

Phenomics Laboratory – A/Prof Wlodkowic (Science, Bundoora) Kaslin Research Group – Dr Kaslin (ARMI, Monash University)

Jusuf Research Group – Dr Jusuf (School of BioSciences, The University of Melbourne)

Project Description – The developing nervous system is a sensitive target for pharmaceutical compounds and environmental toxicants. Developmental neurotoxicity (DNT) in early life-stage exposures can lead to temporary or permanent, long-term effects on motor activity, sensory function, and cognition. Currently, there is minimal data available on DNT for thousands of chemicals used in commerce. Moreover, many pharmaceuticals still have poorly characterized DNT side-effects.

Altered neurological functions and neurotoxicity will generally be behaviourally apparent and together with electrophysiology analysis can serve as useful end-points in the DNT profiling. DNT profiling can be very useful to develop new neuroprotective drugs, as well as elucidate neurotoxic mechanisms of pharmaceutical drug candidates or environmental contaminants.

This project will explore innovative discovery models of DNT profiling such as neurobehavioral zebrafish embryo bioassays, electroretinography, live neuron imaging and intact animal bioenergetic profiling to investigate impact and mechanisms of neurotoxic chemicals at the physiological level (PhysiOmics). Unlike cell lines, zebrafish embryo models offer an intact, multicellular system that integrates physiological processes and, at the same time, allows noninvasive, whole-animal imaging and analysis at both the morphologic- and functional-level. Such in vivo discovery system can be utilized to rapidly discover new molecular countermeasures to protect the nerve cells exposed to harmful chemicals (neuroprotective drugs).

Skills learned in this project: - fluorescent microscopy, video-microscopy and time-resolved imaging - non-invasive neurobehavioral bioassays on living zebrafish embryos - fluorescent bioassays on living zebrafish embryos and neuronal cell cultures - non-invasive bioenergetic bioassays on living neuronal cells and fish embryos - non-invasive zebrafish electroretinography - statistical data analysis, report writing and scientific data presentation

Future career opportunities: skills gained in this project can facilitate future career in: neurosciences, cell biology, drug discovery, predictive toxicology, environmental toxicology, biotechnology companies, pharmaceutical companies, agrochemical companies. References

[1] Huang et al., Science of The Total Environment 615 (2018), 107-114. [2] Campana and Wlodkowic, Environmental Science & Technology 52(3) (2018), 932-946. [3] Fuad et al., Biomicrofluidics 11(5) (2017), 051101. [4] Lindsey and Kaslin, Zebrafish 14(6) (2017), 574-577. [5] Kokel et al., Nature Chemical Biology 6(3) (2010), 231-237.


A/Prof Donald Wlodkowic ([email protected]) Supervisor – Office 223.1.32A




Phenomics Laboratory – A/Prof Wlodkowic (RMIT, Science, Bundoora)

Brander Cancer Research Institute – Prof Darzynkiewicz (NYMC, NY, USA) NCI-NIH - Dr Telford (NIH, Bethesda, USA)

Project Description – Beyond their contribution to basic metabolism, the major cellular organelles, in particular mitochondria and the endomembrane system, can determine whether cells respond to stress in an adaptive (survival) or suicidal (death) manner. The latter processes are commonly referred as programmed cell death (PCD). PCD underlies many pathological conditions arising upon exposure to toxic chemicals, side-effects or overdoses of pharmaceuticals. Many conditions such as neurotoxicity, cardiotoxicity, nephrotoxicity arise from induction of molecular processes that initiate and execute the tightly controlled molecular processes of cell suicide.

Cell death and the closely intertwined regulation of cell cycle in eukaryotic cells serve as useful end-points in the toxicological profiling of chemicals that can be harmful. The utilization of modern fluorescent assays enables researchers to perform real-time, high-throughput and multiparameter analysis on living cells exposed to toxicants or pharmaceuticals. This enables us to rapidly characterise, track and quantify the modes of cell death as well as develop new molecular countermeasures to protect the cells exposed to harmful chemicals (cytoprotective chemicals).

This project will explore induction of PCD as well as perturbations in the cell cycle in response to environmental pollutants. Results will increase our still limited understanding of the impact of toxicants on adaptive cell process, induction of the PCD and the de-regulation of cell cycle underlying mechanisms such as neurotoxicity, developmental toxicity and cardiotoxicity.

Skills learned in this project: - in vitro cell culture, fluorescence bioassays on living cells and fish embryos - real-time metabolic bioassays on living cells and fish embryos - cell proliferation assays - DNA damage: single cell Comet assay, H2AX phosphorylation assays - multiparameter flow cytometry - automated time-lapse fluorescent microscopy - multimodal microplate scanners with build-in cell culture - statistical data analysis, report writing and scientific data presentation

Future career opportunities: skills gained in this project can facilitate future career in: cell biology, drug discovery, predictive toxicology, neurosciences, experimental biology, environmental toxicology, clinical toxicology and biopharmaceutical companies. References

[1] Campana and Wlodkowic, Environmental Science & Technology 52(3) (2018), 932-946. [2] Adams et al. The Journal of Physiology 594 (12) (2016), 3245-3270. [3] Skommer et al. Biosensors and Bioelectronics 42 (2013), 586-591. [4] Wlodkowic et al. Methods in Cell Biology 103 (2011), 55-98. [5] Wlodkowic et al. Cytometry Part A 77(7) (2010), 591-606.

Contact Details: To discuss this project further please contact: A/Prof Donald Wlodkowic ([email protected]) Supervisor – Office 223.1.32A





PhD Project Project Description – Behavioural toxicology has emerged as a promising discipline to bridge the laboratory-to-field divide. Animal behaviour integrates the internal physiology of the animal and the external conditions of the environment. Toxicant-induced behavioural impairments often point to underlying physiological deficits and can be linked to bioenergetic deficits and/or impairment of major organ systems such as underlying neuro- and cardiotoxicity. Evaluating behavior and energy expenditure can be used to rapidly evaluate impact of drugs and toxicants – especially if the affected parameters relate directly to survival, growth, or reproduction. Neurobehavioral responses and metabolic profiling significantly precede mortality and are gradually gaining acceptance as more sensitive and ethically acceptable endpoints of sub-lethal stress.

The lack of high-throughput and high-content screening systems available for behavioural toxicology and metabolic profiling is currently a significant impediment to discovering more sensitive toxicological endpoints. It is also limiting fundamental physiological studies on mechanisms underlying specific life-stage responses to chemical stressors.

This project aims to develop and validate new high-content screening bioassays applicable for marine and freshwater invertebrate aquatic model species. It will utilize the video-microscopy and fluorescent real-time probes to non-invasively assess behavioural and metabolic endpoints. The data will be cross-correlated with conventional techniques and bioenergetics profiling to develop a physiological framework underlying rapid screening in environmental risk assessment.

Skills learned in this project: - video-microscopy and video imaging of small aquatic model organisms - behavioural bioassays such as analysis of swimming activity, avoidance behaviours, etc - ecotoxicological bioassays according to standard OECD/EPA guidelines - multimode high-content plate scanners - real-time bioenergetic profiling using fluorescent indicator probes - high-throughput behavioural screening and data analysis - statistical data analysis, report writing and scientific data presentation

Future career opportunities: skills gained in this project can facilitate future career in: ecotoxicology, environmental risk management, predictive toxicology, neurosciences, drug discovery, biopharmaceutical industry, agrochemical industry

References: [1] Huang et al., Sensors and Actuators B: Chemical 226 (2016), 289-298 [2] Huang et al., Scientific Reports 7(1) (2017), 17603 [3] Campana and Wlodkowic, Environmental Science & Technology 52(3) (2018), 932-946 [4] Heckmann et al., Environmental Toxicology and Chemistry 29(6) (2010), 1396-8 [5] Kokel et al., Nature Chemical Biology 6(3) (2010), 231-237.




Defining Genetic Causes of Antifungal Drug Resistance

Biosciences and Food Technology Discipline – RMIT Bundoora West

Project Description Fungi infect 1.7 billion people world-wide and 1.5 million people die from invasive fungal disease (IFD) annually (1). Deaths due to fungal infections are increasing with mortality often exceeding 50% with delayed treatment further increasing this mortality rate to 100% (1). Fungal infections represent a significant disease burden in Australia, with an estimated 62,341 hospitalizations, 4967 deaths and a cost of $583 million over a 5-year period recorded in 1995-1999 (2). A major contributor to these high mortality rates is the limited number of antifungal drugs available and emerging antifungal drug resistance. Resistance to every class of antifungal drugs has been described and for some drug classes is extremely common (3). Antifungal resistance can cause relapse of the IFD and further increase mortality rates (3, 4). Clinical strains of the fungal pathogen Cryptococcus neoformans have been isolated which possess a higher than normal mutation rate (a mutator phenotype) and also display rapid emergence of resistance to antifungal drugs (5).

This project will aim to:

• Determine if an elevated mutation rate accelerates the development of resistance to antifungal drugs in the fungal species which represent a significant disease burden both worldwide (C. neoformans) and in Australia (Aspergillus and Candida)

• Define the molecular mechanisms giving rise to drug resistance both in vivo and in vitro. This information will contribute to the development of a diagnostic assay enabling rapid on-site detection of fungal pathogens and antifungal drug resistance.

Expertise will be gained in advanced molecular biology, the genetic manipulation of fungal pathogens (generation of genetically modified organisms), bioinformatics and genomics.

References 1. Brown GD, Denning DW, Gow NA, et al. 2012. Hidden killers: human fungal infections. Sci Transl Med

4:165rv13. 2. Slavin M, Fastenau J, Sukarom I, et a.l 2004. Burden of hospitalization of patients with Candida and

Aspergillus infections in Australia. Int J Infect Dis 8:111-20. 3. Coelho C, Casadevall A. 2016. Cryptococcal therapies and drug targets: the old, the new and the promising.

Cell Microbiol 18:792-9. 4. Rhodes J, Beale MA, Vanhove M, et al 2017. A Population Genomics Approach to Assessing the Genetic

Basis of Within-Host Microevolution Underlying Recurrent Cryptococcal Meningitis Infection. G3 (Bethesda) 7:1165-1176.

5. Boyce KJ, Wang Y, Verma S, et al. 2017. Mismatch Repair of DNA Replication Errors Contributes to Microevolution in the Pathogenic Fungus Cryptococcus neoformans. MBio. 8(3). Pii: e00595-17.

Contact Details To discuss this project further please contact:

Dr Kylie Boyce email: [email protected] (03) 9925 7101 Prof Ian Macreadie email: [email protected] (03) 9925 6627






Bioscience and Food Technology/ Tissue Engineering & NanoBiotechnology Research Laboratory – RMIT Bundoora/City

Multicentre cross disciplinary Joint PhD Project

Project Description –

Current research in pancreas stem cell biology has achieved limited success in generating alternate cell types for treatment of diabetes. This project aims to deliver three-dimensional scaffolds that will not only allow for efficient differentiation of pancreatic cells but also enhance their survival and function through integration of endothelial cell microfluidic channels. This study will be executed through expert collaborators from cross disciplinary research areas viz. bio-printing, cell biology, and engineering in a milestone-driven workflow.

Current tissue engineering efforts in islet micro-encapsulation, (which provide a quasi-3D scaffold) have achieved limited success because these scaffolds i) do not support vascularisation and hence low impact on the long-term survival; or ii) are immunogenic and prone to fibrosis and immune cell attack. Such strategies have been tested since early 1990s. To develop our nanobio-printing derived scaffold, we aim to i) identify and characterize the appropriate biomaterial (already available and from the RMIT nanobio-material library) for 3D printing of islets; ii) test the biomaterial, Bioprinted-scaffold and cellularised scaffold/devices for in vitro function; and iii) assess the functional potential of these nanobioprinted scaffolds in animal models. The project will deliver bio-printed 3D scaffolds via two major approaches: i) Biomimicry, and ii) Autonomous self-assembly, along with integrated microfluidics to collectively deliver the desired nano-scale derivation of cellular microenvironment on a bioprinted tissue scaffold which will be tested for glucose responsiveness. The success of PRINT cell project will be measured by transplantable 3D printed functional Islet scaffolds, knowledge/IP generation, multidisciplinary research training and advancement of knowledge in the field.

A prospective PhD student with strong background knowledge of Cell Biology/ 3D Bioprinting with ever- lasting enthusiasm and risk taking capacity are encouraged to apply. Student will have ample opportunities to learn state-of-the-art cross disciplinary tools and techniques available in the three different school in RMIT, St Vincent Hospital and NHMRC Clinical Trails Centre (CTC) through routine mentoring and interaction. In addition, student will be nurtured for research ethics, critical thinking, time & project management, and problem-solving skills.

Meet the Team: Ravi Shukla, Anand Hardikar, Milan Brandt, Vipul Bansal, Ma Qian, Khasharyar Khoshmanseh, Sara Baratchi, Mugdha Joglekar, Wayne Howthrone, Zeyad Nasa, Suresh Bharagava,

References 1. A 3D map of the islet routes throughout the healthy human pancreas. Scientific Reports, 2015, 5, 14634 2. Epithelial-to-mesenchymal transition in pancreatic islet beta cells. Cell Cycle 2010, 9(20), 4077-4079 3. New pancreas from old: microregulators of pancreas regeneration. Trends in Endocrinology and

Metabolism, 2007, 18(10), 393-400

Contact Details: To discuss this project further please contact anyone of us: RAVI SHUKLA ([email protected]) Joint Supervisor – Office +61 3 9925 2970 ANAND HARDIKAR ([email protected]) Asso. Supervisor– Office +61 2 9562 5000 MILAN BRANDT (milan.brandt@rmit,edu.au) Joint Supervisor – Office +61 3 9925 4179 KASH KHOSMANESH ([email protected] ) Asso. Supervisor –Office +61 3 9925 2851 SARA BARATCHI ([email protected]) Asso. Supervisor –Office +61 3 9925 7036

https://scholar.google.com.au/citations?user=_p-K5eUAAAAJ&hl=en

http://sydney.edu.au/medicine/people/academics/profiles/anand.hardikar.php

https://www.youtube.com/watch?v=-5ELveQ_7tA

https://scholar.google.com.au/citations?user=MskJvAUAAAAJ&hl=en

https://www.rmit.edu.au/contact/staff-contacts/academic-staff/m/ma-professor-qian

https://scholar.google.com.au/citations?user=S1cIkzoAAAAJ&hl=en



http://sydney.edu.au/medicine/people/academics/profiles/mugdha.joglekar.php


https://scholar.google.com.au/citations?hl=en&user=B8Yr9T4AAAAJ&view_op=list_works&sortby=pubdate






DR221 – PhD (Computer Science) MR221 – Master of Science (Computer Science)



Computer Science & Information Technology – City Campus

Project Description – The design of the interfaces of search engines has been relatively fixed for the past 25 years: a user types in a query, clicks a search button and is presented with a list of results. The user scans the list and clicks on any results that look promising. A great deal of research has considered how to approximate user satisfaction from the way that users click: how long do they dwell on search results, how quickly do they reformulate their query? However, user interfaces to search engines are changing: increasingly users speak queries to search engines, search engines often return answers, not links to documents; systems like Siri or smart watches speak or display highly concise answers. In many of these situations, the user will never click on a result. The research question of the project will be:

With the evolution of search engine interfaces, how can search engine designers know when a user is satisfied with their search results?

The project has the potential to examine one or more of the following aspects

• To what extent can existing research methods be applied to these new interfaces? o Extensive research has been conducted on modeling user behavior and approximating user

satisfaction. However, the vast majority of work has assumed users work with a classic click- based search engine interface.

• Can the way a user speaks their query indicate satisfaction and/or annoyance? o Emotion detection in speech is now possible, could such a signal indicate a user struggling

with their search task? • Can the range of sensors in a user’s phone or watch be exploited to detect clues to suggest user

satisfaction? o Mobile phones and watches can detect the context of the user (e.g. at home/work, in the car).

They can also detect the way they are being held. Could such clues to exploited to better understand user satisfaction?

• Can knowledge of other activities a user undertakes be used to determine user satisfaction? o A user may not click on a search result, but by seeing an answer from a search engine, are the

activities a user does after searching affected by the answer?

References 1. Priyogi, B., Sanderson, M., Salim, F., Chan, J., Tomko, M., Ren, Y. (2018) Identifying In-App User

Actions 2. from Mobile Web Logs. In Proceedings of The 22nd Pacific-Asia Conference on Knowledge Discovery

and Data Mining, Springer. 3. Hassan, A., Shi, X., Craswell, N., & Ramsey, B. (2013, October). Beyond clicks: query reformulation as a

predictor of search satisfaction. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 2019-2028). ACM.

4. Ong, K., Järvelin, K., Sanderson, M., & Scholer, F. (2018, March). QWERTY: The Effects of Typing on Web Search Behavior. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (pp. 281-284). ACM.

Contact Details: To discuss this project further please contact: Mark Sanderson ([email protected]) – Office 14.09.17





Project Description – Recommender systems underpin the way that Netflix, Amazon – and a wide range of e-commerce sites – observe the objects and activities that users like and then recommend something new. While extensive research has been conducted in recommender systems, there are still many potential projects. The focus of this project is to enable recommender systems to better react to the continual change in user’s preferences. The research question of the project will be

How to improve the accuracy of recommender systems by mitigating the problem of time drifting inputs using ensemble learning techniques?

The project will examine the following aspects

• How will ensemble learning be adapted to manage user preference change? o Ensemble learning requires a set of single learners to build from. These learning will need to

incorporate a forgetting mechanism to discard old user preferences and be self-adaptive. This is considered a major scientific and engineering challenge. Once adapted an ensemble learner will be constructed.

• How is user preference change detected and monitored? o There are two approaches to define how the learner will adapt: informed methods that detect

the drift through triggering mechanisms and blind methods that implicitly are adapted to changes without drift detection. Both approaches will be examined in this project.

• What are the performance criteria? o Here, we will focus on the criteria to obtain an unfailing system that handles user preference

change. The requirements for such systems are autonomy (i.e. the level of human involvement), reliability, and complexity (i.e. time and memory consumption).

References 1. B. Kumar and N. Sharma, “Approaches, Issues and Challenges in Recommender Systems: A Systematic

Review,” Indian J. Sci. Technol., vol. 9, no. 47, 2016. 2. M. Taghavi, J. Bentahar, K. Bakthiyari, and C. Hanachi, “New Insights Towards Developing

Recommender Systems,” pp. 1–35, 2017. 3. J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A

survey,” Decis. Support Syst., vol. 74, pp. 12–32, 2015. 4. I. Khamassi, M. Sayed-Mouchaweh, M. Hammami, and K. Ghédira, “Discussion and review on evolving

data streams and concept drift adapting,” Evol. Syst., 2016. 5. Y. Koren, “Collaborative filtering with temporal dynamics,” Commun. ACM, vol. 53, no. 4, p. 89, 2010. 6. H. Liu, X. Kong, X. Bai, W. Wang, T. M. Bekele, and F. Xia, “Context-Based Collaborative Filtering for

Citation Recommendation,” IEEE Access, vol. 3, pp. 1695–1703, 2015. 7. Y. Kabutoya, R. Sumi, T. Iwata, T. Uchiyama, and T. Uchiyama, “A topic model for recommending

movies via linked open data,” Proc. - 2012 IEEE/WIC/ACM Int. Conf. Web Intell. WI 2012, pp. 625–630, 2012.


Mark Sanderson ([email protected]) – Office 14.09.17 Yongli Ren ([email protected]) – Office 14.09.07



Effective Countering cyber threats over Smart-Grid Infrastructures

Computer Science – Campus

Project Description – Smart-grids are the cornerstones of future energy infrastructure. Today's ageing power grids have had their inadequacies exposed by challenges such as: the rising cost of fuels, need for lower greenhouse emissions, and the rise of renewable energy [1]. Tomorrow’s Smart Grids will incorporate increased sensing, communication, and distributed control systems to accommodate renewable generation, EV (Electric Vehicle) loads, peak demand reduction and energy loss minimization. Of the various impediments affecting smart-grids, security is the foremost concern for all stake holders, estimating economic losses at $6B annually [2]. This project aims to research a distributed and multi-granular framework for effective data security that is achieved through anomaly detection over smart-grid sensing systems. This will evaluate cyber threats from a data centric point of view for effective protection, and the framework will be compatible with and cater to the dynamic and non- homogeneous data typically present in multi-faceted smart-grid environments.

Specifically this project intends to extend some of the early work carried it out by Prof Tari [3-6] to produce: • Efficient data processing framework of a hierarchical nature that identifies the most optimal nodes for analytical processing: This mitigates issues relating to the very large amount of streaming data involved, and the complexity of a largely heterogeneous population of computational nodes involved in sensing and communication. The emphasis will be on dynamic evaluation of such nodes, based on limitations such as node density, connectivity, availability, and ever-present resource constraints. • Distributed data-clustering technique based on the aggregated data over a defined node hierarchy. This will address limitations in centralised data clustering models, and factors in non-homogeneous data- domains. We will also consider cross-correlation of data across multiple nodes, allowing for optimal accuracy during classification. • Multi-granular anomaly detection technique that will consider observed correlations (if any) amongst data-clusters produced at each analytical level as well as individual data points on the defined node hierarchy. This will be an adaptive process able to clearly discern between abnormal and normal data in a non-parametric and non-probabilistic manner.

References [1] V. Gungor et al.: Smart Grid Technologies — Communication Technologies and Standards. IEEE

Transactions on Industrial Informatics, 7(4):529 –539, 2011. [2] H. Khurana et al.: Smart-Grid Security Issues. IEEE Security Privacy, 8(1), 2010. [3] A. Anwar, A. Mahmmood and Z. Tari: Ensuring Data Integrity of OPF Module and Energy Database

by Detecting Changes in Power Flow Patterns in Smart Grids. IEEE Transactions on Industrial Informatics (TII), 13(6):3299-3311, 2017.

[4] A. Almalawi, A. Alharthi, Z. Tari, M. A. Cheema, and I. Khalil: kNNVWC: An Efficient k-Nearest Neighbours Approach based on Various-Widths Clustering. IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(1):68-81, 2016.

[5] A. Almalawi, X. Yu, Z. Tari, A. Fahad, and I. Khalil: Unsupervised Anomaly-Based Detection Approach for Integrity Attack on SCADA Systems. Elsevier Journal on Computer Security, 46:94-110, 2014.

[6] C. Quieroz, A. Mahmood, and Z. Tari: A Probabilistic Model to Measure the Survivability of SCADA Systems. IEEE Transactions on Industrial Informatics, 9(4):1975-1985, 2013.


Prof Zahir Tari ([email protected]) Office B014 F11 R018



Low-Latency High-Throughput Computational Models for Heavily Data-Driven Applications in Hybrid Data Centers


Project Description – The need to process a huge volume of data during in a small amount of time is dramatically increasing especially as the size of the data moves into Exabyte in the near [1]. While use of such applications was previously confined to the finance sector, it is becoming now prevalent in almost every industry where analytical processing over massive data sets can solve business problems. To meet such low-latency requirements of data mining and machine learning applications, datacentre providers must expand the computing capacity of the underlying infrastructure by exploiting graphics processing units (GPU) and Field Programmable Gate Arrays (FPGAs) as new hardware accelerators, the so called heterogeneous datacentres. However, there is no mechanism that can appropriately project the complex characteristics of modern applications emerging in enterprise/scientific domains into the available computing capacity of a system with hundreds or thousands of heterogeneous computers. Additionally, using existing resources allocation solutions in heterogeneous datacentres result in significant resource wastage [2][3]. The general aim of this project is to investigate innovative solutions/methods to control and to make use of the capabilities of the new hardware accelerators in a heterogeneous computing systems to substantially enhance the resource efficiency when running data-driven applications. The specific aims of this project are:

• To explore inference algorithms for modelling the complex interaction of different components of

data-driven applications with the heterogeneous resources. • To design resource allocation controlling algorithms that not only maximize the system performance,

but also detect and resolve resources' bottlenecks. • To equip the designed algorithms with adaptive optimal control tools for online tuning of

corresponding parameters to effectively deal with modelling uncertainties in a datacentre as well as the sporadic rise and fall in the incoming requests.

References [1] P. Lotfi-Kamran, B. Grot, M. Ferdman, S. Volos, O. Kocberber, J. Picorel, A. Adileh, D. Jevdjic, S.

Idgunji, E. Ozer, et al., “Scale-out processors,” in ACM SIGARCH Computer Architecture News, vol. 40, pp. 500–511, IEEE Computer Society, 2012.

[2] R. Hameed,W. Qadeer, M.Wachs, O. Azizi, A. Solomatnikov, B. C. Lee, S. Richardson, C. Kozyrakis, and M. Horowitz, “Understanding sources of inefficiency in general-purpose chips,” in ACM SIGARCH Computer Architecture News, vol. 38, pp. 37–47, ACM, 2010.

[3] L. Keys, S. Rivoire, and J. D. Davis, “The search for energy-efficient building blocks for the data center,” in International Symposium on Computer Architecture, pp. 172–182, Springer, 2010.


Professor Zahir Tari ([email protected]) Office: B014 F11 R018





Project Description – Data exfiltration is the unauthorized leakage of data from computers by sophisticated malware and malicious insiders. Data exfiltration is a serious problem since it may have catastrophic effect on businesses, governments as well as individuals if such exfiltration involves sensitive data. Examples include exfiltration of data involving business inventions, national intelligence, classified research, individual’s credit card and biometric profile. Specifically, data exfiltration has resulted in huge economic losses as well as unprecedented breaches of national security. A study by the Ponemon Institute [1] reported that the average per-incident cost of reported data leakage by businesses was $4 million in 2015/2016, and the number of reported data breaches surpassed all previous years [2]. The aim of this project is to develop solutions to detect sensitive data exfiltration attempts by malwares, as well as human users, and block those attempts without affecting legitimate users’ normal usage of computers. The specific objectives of this project are to develop:

• Existing real-time identification techniques of sensitive data are not scalable for analyzing large

amount of data generated by memory resident application. This project will develop efficient data representation and summarization techniques that will enable multi-granular searching of sensitive information from memory.

• Some Malware can circumvent existing exfiltration detection mechanisms by loading small parts of sensitive data from file or by accessing dynamically generated sensitive data (e.g., from Email). This project will develop a meta-searching technique that will continuously monitor memory resident data to detect exfiltration attempts of sensitive data by malwares or insiders.

• A key limitation of existing techniques is malwares can circumvent existing detection mechanisms by partially loading sensitive data over a period of time from file or by accessing dynamically generated sensitive data (e.g., from Email). This project will develop a technique that can piece together the different parts of data (dynamically generated) accessed over a period of time by by process(es), to determine if those parts constitute sensitive data.

References

[1] L. Ponemon, “2016 Ponemon Institute Cost of a Data Breach Study,” Security Intelligence Institute. [2] DataLossDB, “Data Loss Statistics”. Retrieved 26 February 2017, from https://blog.datalossdb.org/


Professor Zahir Tari ([email protected]) Office B014 F11 R018



Indoor Positioning with Multiple Antennas on Wi-Fi

Computer Science and Information Technology Discipline – City Campus

This project offers opportunities for 2 PhD students.

Project Description – Indoor positioning is the process of obtaining a device or user location in indoor environments, which enables a wide range of applications in health, industry, and surveillance. An increasing popularity of smartphones demands accurate indoor positioning solutions to be deployed in real world. Indoor positioning also facilitates building the infrastructure for emerging Internet of Things (IoT).

Wi-Fi based indoor positioning has been the most popular suite of technologies since most of the smartphones are Wi-Fi enabled. Such systems typically utilise radio signals collected from existing Wi-Fi access points. Studies [5-7] show that the accuracy of Wi-Fi based systems depends much on Wi-Fi bandwidth. New standards are evolving to support Wi-Fi access points with multiple antennas. This provides us an exciting opportunity to break the Wi-Fi bandwidth barrier, and hence strongly motivate us to revisit the design of indoor positioning systems.

In this project, we will investigate effective ways to increase Wi-Fi bandwidth and discover a fundamental breakthrough in accurate indoor positioning. To achieve this goal, we identify the following objectives to be accomplished. 1) Investigate signal propagation and its properties under Wi-Fi access points with multiple antennas. 2) Discover key principles for practical system design, and develop advanced algorithms to push the performance limit of indoor positioning. 3) Develop demonstration applications utilising our positioning algorithms to provide real-life validation and also address practical issues for potential deployment.

References 1. Jie Xiong, Karthikeyan Sundaresan, and Kyle Jamieson. ToneTrack: Leveraging Frequency-Agile Radios

for Time-Based Indoor Wireless Localization, in Proc. of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom 2015), Paris, France, 7-11 Sept. 2015.

2. Jon Gjengset, Jie Xiong, Graeme McPhillips, and Kyle Jamieson. Phaser: Enabling Phased Array Signal Processing on Commodity WiFi Access Points, in Proc. of the 20th annual international conference on Mobile computing and networking (MobiCom 2014), Maui, Hawaii, USA, 7-11 Sept. 2014.


1st Supervisor – A/P Tao Gu Email: [email protected] Website: https://sites.google.com/site/gutao98/


https://sites.google.com/site/gutao98/



Human Activity Sensing with Wireless Signal



Project Description – Human activity sensing has drawn much attention due to the growing demands from many domain applications, such as surveillance, health care, and human-computer interaction. A traditional approach is to capture data from wearable sensors such as accelerometer and gyroscope, and process sensor data using machine learning or data mining algorithms. This approach, though proven to be effective, requires a user to wear on-body sensors which may be uncomfortable and obtrusive to the user.

In recent years, radio-based human activity sensing [1-2] has explored the possibility of using radio signals without the need of physical sensors or special devices. The underpinning principle is that the human body causes radio signal attenuation, and different body movements generate different changes in radio signals. By analysing these change patterns, we are able to discriminate different activities. Since radio-based sensing does not require a person to carry any device, it provides a contactless and privacy-preserving approach to detecting human activities. This new approach plays an important role in developing next generation applications. While the existing radio-based sensing systems look promising and encouraging, they mostly adopt a trial-and-error approach to make the system function based on tedious experiments and they lack the theoretical foundation to guide system design in practice. Understanding the fundamental theory and design principles in radio-based human activity sensing is crucial to the success of its real deployment in our society.

In this project, we will discover and reveal the underlying theory in radio-based human activity sensing. Having better understanding on the signal propagation properties, we will then develop a theoretical framework, and define the fundamental principles to guide practical system design. We will also validate our theoretical framework and demonstrate its scientific merit through building typical real-life activity sensing applications such as contactless activity detection and vital signs monitoring. We aim to achieve the following objectives. (1) Understand radio signal propagation and discover its properties in radio-based sensing systems. (2) Develop a realistic sensing model by extending our basic model, and define a set of key design principles to guide practical system design for real-life applications. (3) Develop several activity sensing applications to validate our theoretical framework in real life. These applications will enable us to assess the scientific merit of our work, and also address practical issues for potential deployment.

References 1. X. Qi, G. Zhou, Y. Li, and G. Peng. RadioSense: Exploiting Wireless Communication Patterns for Body

Sensor Network activity recognition, In Proc. of the 33rd IEEE Real-Time Systems Symposium (RTSS 2012), San Juan, Puerto Rico, USA, Dec 4-7, 2012.

2. X. Liu, J. Cao, S. Tang, and J. Wen. Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals, In Proc. of the 35th IEEE Real-Time Systems Symposium (RTSS 2014), Rome, Italy, Dec 3-5, 2014.







Characterization of Australian Bioluminescent Dinoflagellates

Computer Science – City campus

Project Description

Internet of things (IoT) is a new paradigm of connecting objects through the Internet. Devices and people will have ability to transfer data over wired and wireless networks with minimal human intervention. IoT has a great potential to facilitate domain-specific usage and to improve performances of the systems in many applications such as transportation, energy management, manufacturing, and healthcare []. In this project, we aim to develop an Internet of Things framework to facilitate efficient positioning and tracking of objects. We will focus on developing a physical IoT framework for autonomous and accurate counting and positioning of objects, leveraging on Radio-frequency identification (RFID), as well as Bluetooth Low Energy. RFID tags or Bluetooth beacon will be attached on thousands of objects to connect to a network to transmit identification information. In summary, we identify the following objectives to be accomplished within the three year project duration. 1) Design efficient protocol for tag counting, and localisation algorithm to track RFID tags. 2) Design efficient localisation algorithm to construct Bluetooth Mesh network, and track Bluetooth

Beacon. 3) Develop demonstration applications utilising our algorithms to provide real-life validation and also

address practical issues for potential deployment. References

1. Wang, S., J. Wan, D. Zhang, D. Li, and C. Zhang. 2016. “Towards Smart Factory for Industry 4.0: A Self-organized Multi-agent System with Big Data Based Feedback and Coordination.” Computer Networks 101: 158–168.

2. Leitão, P., A. W. Colombo, and S. Karnouskos. 2016. “Industrial Automation Based on Cyber-physical Systems Technologies: Prototype Implementations and Challenges.” Computers in Industry 81: 11–25.

Contact Details

To discuss this project further please contact: Associate Professor Tao Gu ([email protected]) Supervisor – Office 14.11.26




Computer Science & Software Engineering Discipline/Intelligent Systems Group – City Campus

Project Description – With computers now present in everyday devices like mobile phones, credit cards, cars and planes or places like homes, offices and factories, the trend is to build embedded complex systems from a collection of simple components. Thus a complex surveillance system for a smart house can be “realised” (i.e., implemented) by suitably coordinating the behaviours (i.e., the operational logic) of hundreds (or thousands) of simple devices and artifacts—lights, blinds, a microwave, video cameras, robotic arms, etc.—installed in the house. The Behavior Composition problem involves automatically building an embedded controller-coordinator to bring about a desired target complex system by suitably coordinating the available components.

Researchers at RMIT had contributed significantly in the last 12 years to the composition problem, in collaboration with many other internationally recognised scientists and in particular with the AI group in La Sapienza University, Rome, Italy. A short high-level description can be found in [1], and more comprehensive treatment have been published in the Artificial Intelligence Journal [2,3], the premier journal in AI.

One application of composition that has recently received attention is smart manufacturing, where existing capabilities in a production plant are coordinated to achieve a production recipe [4-6]. Our work developed over the years is in fact used in the large 2012-2018 EU project EP/K018205/1 “EVOLVABLE ASSEMBLY SYSTEMS –Towards open, adaptable and Context-awaare equipment and systems,” which aims at developing highly adaptable manufacturing systems and includes 11 large industries such as Airbus, Siemens, and GE. Dr Brian Logan (University of Nottingham), one of the project’s investigators, has visited RMIT to learn about the problem and technique and is currently a collaborator on the topic (e.g., [4]).

Nonetheless, many (most) challenges remain unanswered. The overarching aim of this project is to develop a behavior composition account that meets the needs of the manufacturing domain, by accommodating production of multiple items, probabilistic models of failure, smooth transitions between production recipes, true concurrent execution of devices in the plant, execution of multiple different production recipes in the same production plant, etc. The project involves both theoretical work in developing adequate representation models and practical work in devising effective computational techniques to solve the problem efficiently.

References [1] Giuseppe De Giacomo, Fabio Patrizi, and Sebastian Sardina. Building virtual behaviors from partially controllable available behaviors in nondeterministic environments. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages 523-526,, 2014. [2] Giuseppe De Giacomo, Fabio Patrizi, and Sebastian Sardina. Automatic behavior composition synthesis. Artificial Intelligence Journal, 196:106-142, 2013. [3] Giuseppe De Giacomo, Alfonso Gerevini, Fabio Patrizi, Alessandro Saetti, and Sebastian Sardina. Agent planning programs. Artificial Intelligence, 231:64--106, 2016. [4] Paolo Felli, Brian Logan, and Sebastian Sardina. Parallel behavior composition for manufacturing. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 272--278, 2016. [5] Lavindra de Silva, Paolo Felli, Jack C. Chaplin, Brian Logan, David Sanderson, Svetan M. Ratchev: Synthesising Industry-Standard Manufacturing Process Controllers. AAMAS 2017: 1811-1813 [6] Paolo Felli, Lavindra de Silva, Brian Logan, Svetan M. Ratchev: Process Plan Controllers for Non- Deterministic Manufacturing Systems. IJCAI 2017: 1023-1030

Contact Details: To discuss this project further please contact: A/Prof. Sebastian Sardina ([email protected]) Supervisor – Office 14.08.7D




Computer Science & Software Engineering Discipline/Intelligent Systems Group – City Campus Project Description – Plan-goal recognition (PR or GR) is the problem of identifying an agent’s intent or purpose by observing her behaviour. Its growing number of applications include language understanding and response generation, adversarial reasoning for games and the military, smart homes for the cognitively impaired, and human- machine interaction. Deception is the other side of the coin, and it involves the synthesis of behavior that while achieving the goals is able to deceive an observer as much as possible.

Traditionally, PR/GR has involved matching a sequence of observations to some plan in a given plan library, the “winning” plan being the one that best matches the observations [1]. Given that each plan presumably sets out to achieve a goal, having identified the agent’s plan, the observer has implicitly identified her goal. Recent developments in GR [2,3,4] dispense with the overhead of a plan library by treating the problem as one of “planning in reverse”. This innovation has enabled plan recognition to leverage advances made by the planning community and relies on a key insight that the probability of a plan can be linked directly to its cost, under the assumption that agents are rational.

The aim of this project is to push further the application of automated planning to the problem of goal recognition and deception. Over the last 3 years, researchers at RMIT University have studied the problem of GR [3] and deception [4] in the context of navigation. The former amounts to observing an agent navigate a space (e.g., an airport terminal or video game map) and understand its destination (e.g., specific gate or room in the game); the latter ask for the construction of trajectories that are “deceiving,” but still reach the intended destination. The work done has been welcome and [3] has received the Best Student Paper Award at the premier conference on Intelligent Agents and has been invited into the IJCAI-18 Sister Conference Track. This project aims to further extend those results in one or more of the following directions:

• Direction 1: integrate GR and deception in navigation with the more general case of task-oriented behavior (as in [2]), by mixing spatial reasoning techniques with general action reasoning.

• Direction 2: extend existing planning-based techniques with adapting cost models, that are learnt over time and change dynamically.

• Direction 3: develop deception framework in the context of an observer who may have limited observation capabilities

• Direction 4: develop techniques for designing observation capabilities to improve GR (e.g., installing a camera in particular strategic locations)

References [1] H. Kautz and J. Allen. Generalized plan recognition. In Proceedings of the National Conference on

Artificial Intelligence (AAAI), pages 32–37, 1986 [2] M. Ramirez and H. Geffner. Probabilistic plan recognition using off-the-shelf classical planners. In

Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 1121–1126, 2010. [3] Peta Masters and Sebastian Sardina. Cost-based goal recognition for path-planning. In Proceedings of the

International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pages 750--758, 2017. Best Student Paper Award.

[4] Peta Masters and Sebastian Sardina. Deceptive path-planning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 4368--4375, 2017.


http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1821

http://www.aamas2017.org/proceedings/pdfs/p750.pdf

https://www.ijcai.org/proceedings/2017/610



Text mining for recommendation

Computer Science – City campus Project Description – Recommender systems are widely used for E-commerce, social networks, academia and other domains. Collaborative filtering is a widely used technology for recommendation, and the main objective is to accurately predict ratings from ratings by like-minded people. Recently textual information has also been exploited for recommender systems. Item description and product reviews have been exploited for product recommendation whereas titles and abstracts are used for article recommendation. The focus of existing research on using textual information is on improving the accuracy of rating prediction. On the other hand, the textual explanation for the rating prediction is more useful for recommendation for end-user experience. In this project, we will develop deep learning models for text mining to enhance the effectiveness as well as utility of recommendation. There are three lines of work in this project: • Incorporating textual information for effective collaborative filtering. • Design of textual-based collaborative filtering models in the rich context of research environment for recommendation of articles. • Recommendation on Twitter via credibility analysis of tweets. • Text analysis to profile users for expert recommendation. References

1. Li, Piji, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. "Neural rating regression with abstractive tips generation for recommendation." In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 345-354. ACM, 2017.

2. Almahairi, Amjad, Kyle Kastner, Kyunghyun Cho, and Aaron Courville. "Learning distributed representations from reviews for collaborative filtering." In Proceedings of the 9th ACM Conference on Recommender Systems, pp. 147-154. ACM, 2015.

3. He, Xiangnan, Tao Chen, Min-Yen Kan, and Xiao Chen. "Trirank: Review-aware explainable recommendation by modeling aspects." In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1661-1670. ACM, 2015.

4. Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009): 4.

Contact Details: To discuss this project further please contact: Associate Professor Xiuzhen Jenny Zhang ([email protected])




Computer Science & IT - City

Project Description – This project aims to study the problem of Influence Maximization for facility deployment in urban computing. Our first goal is: given a set of facility candidates U, a database of vehicle/user trajectories T and a budget L, how to find a set of facilities within the budget L so that the deployed facilities can influence the largest number of moving trajectories. We call it as the Optimal Facility Deployment problem. In this project, we expect to solve four research questions, each of which is raised from the following challenges. The first core challenge is that multiple facilities have influence overlap on trajectories and it is critical to identify and reduce the overlap of the influence among different facilities, while keeping the budget constraint into consideration. The second challenge is that, the deployment of each facility has non-equal cost, which makes this problem NP-hard. Therefore, we would like to devise a series of algorithms that both achieve theoretically non-trivial approximation ratio while performing as efficient and feasible as possible for decision making purpose. The third challenge is how to efficiently handle the incremental version of the facility deployment problem: given an existing placement plan and more budgets, how to place more facilities in a way that further maximize the influence as much as possible. We aim to incrementally update the current placement without computing the global influence from scratch. The last challenge is how to build an appropriate model to measure the cost and benefits in multi-faceted way, to cater for different scenarios and needs.

Significance and Benefits: The solution to this problem can significantly advance almost any facility selection problem in urban area, where an influence overlap always exists. In particular, it is useful but not limited to the optimal deployment of warehouses, gas stations, electric vehicle charge stations, outdoor advertising billboards.

Required Background: Strong Algorithm design and analysis, Data Mining (with a focus on clustering), Database Index

References [1] G. Li, S. Chen, J. Feng, K.-l. Tan, and W.-s. Li. Efficient location-aware influence maximization. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pages 87-98. ACM, 2014. [2] S. Khuller, A. Moss, and J. S. Naor. The budgeted maximum coverage problem. Information Processing Letters, 70(1):39-45, 1999.

Contact Details: To discuss this project further please contact: 1st Supervisor – Dr. Zhifeng Bao ([email protected])



Solving Hybrid Learning and Optimisation Problems

CSSE discipline, Machine Learning, Big Data and Data Analytics Groups Location (City)

PhD Project Project Description Sequence based problems involve learning, predicting and recommending sequences, an order set of entities, and a subset of these have characteristics of (machine) learning and optimisation problems. Three examples are tour recommendation, journey planning and recommending routes in ride-sharing taxis. In tour recommendation [1], an itinerary of places to visit and transportation schedules are recommended to tourists based on their interests and past travels. In journey planning [4], a trip from A to B using different transportation modes are recommended to users based on their preferences and transportation schedules. In route recommendation in ride-sharing taxis, a route is suggested for taxi drivers to maximise the chances they will get a fare and minimise idle time. All these applications are sequence based problems and are simultaneously a machine learning problem, e.g., recommending locations to visit, learning preferences and predicting traffic, and an optimisation problem, e.g., scheduling under constraints. Solving this recommendation and scheduling problem requires a joint, integrated approach.

In the group, there has been work done in itinerary recommendation [3] and journey planning [4], but there is still the open problem of how to simultaneously learn from data, either for preferences or utilising previous sequences and solve sequence based optimisation that satisfies non-trivial constraints.

In this project, the student will research novel approaches that simultaneously learns and optimises. As initial examples, the project will apply this to itinerary recommendation and journey planning, but there is scope to expand to other applications.

References: [1]. J. Borràs, A. Moreno, and A. Valls. Intelligent tourism recommender systems: A survey. Expert Systems with Applications, 41, 16 (2014), 7370–7389. [2]. K. Lim, J. Chan et al. Personalized Itinerary Recommendation with Queuing Time Awareness, in Proceedings of ACM CIKM, (to appear), 2017. [3]. K. Lim, J. Chan et al. Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Duration, in Proceedings of AAAI IJCAI, pp. 1778-1784, 2015. [4]. M. Haqqani, X. Li and X. Yu. An Evolutionary Multi-criteria Journey Planning Algorithm for Multimodal Transportation Networks, in Proceedings of ACALCI, pp.144-156, 2017.


Dr Jeffrey Chan ([email protected]) 1st Supervisor – Office 14.08.15 Prof Xiaodong Li ([email protected]) 2nd Supervisor – Office 14.08.14B




Next Generation Machine Learning

CSSE Discipline, Machine Learning, Big Data and Data Analytics Groups City Campus

Project Description Machine learning is about developing and using models to solve many prediction and learning problems from data. Combined with the availability of large volumes of data, advances in large scale processing, machine learning has captured the imagination of the general public and is one of the main drivers of the recent advances in artificial intelligence and data science.

However, there remains many outstanding and open challenges. In this project, we aim to tackle such challenges and research cutting edge problems in machine learning, particularly in dimensionality reduction, hybrid learning models, deep learning and graph based learning. These include but not limited to:

• Novel approaches for dimensionality reduction, e.g., in matrix factorisation [1] and feature selection [2], which can help with improving predictive performance, identifying information biomarkers, and improve modern day recommender systems.

• Research into hybrid deep learning models, that combines deep learning with more traditional machine learning models. An example is in deep recommendation learning [3], that combines the ability of deep learning models to learn arbitrary functions with factorisation models strength in recommendation.

• New approaches in hybrid machine learning and contraint programming models, that incorporates the strengths of both in order to easily incorporate complex user feedback and guidance.

• New approaches in graph based learning, including blockmodelling [4]. This includes developing of new probabilistic matrix factorisation models that combine facets of probabilistic Bayesian models with factorisation models, allowing contextual information to be easily incorporate.

The supervisors form a larger Melbourne based machine learning cluster, which are actively engaged in studying and researching on these problems. The student will perform such research and join this larger based machine learning cluster.

References 1. D. Lee and S. Seung, “Learning the parts of objects by non-negative mattrix factorization”, Nature, 401, pp. 788-791, 1999. 2. X. Nguyen, J. Chan, S. Romano and J. Bailey, “ Effective Global Approaches for Mutual Information Based Feature Selection”, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 512-521, 2014. 3. S. Zhang, L. Yao and A. Sun, “Deep Learning based Recommendation System: A Survey and New Perspective”, arXiv: 1707.07435v5, 2017. 4. J. Chan, W. Liu, C. Leckie, J. Bailey, K. Ramamohanarao, “Discovering Latent Blockmodels in Sparse and Noisy Graphs using Non-negative Matrix Factorisation”, in Proceedings of 22nd ACM International Conference on Information and Knowledge Management, pp. 811-816, 2013.


Dr Jeffrey Chan ([email protected]) 1st Supervisor – Office 14.08.15 TBD 2nd Supervisor –



Learning to Optimise


Project Description In optimisation, typically an expert or analyst will first model the optimisaton problem as an objective function and constraints, then select an approach solver to optimise it. The modelling and solver selection can have a large effect on the quality of the final solution and how fast this solution was found. However, good modelling and selection can be difficult, and still largely a manual affair, requiring years of experience to do well. Although optimisation problems surround us, due to the high barriers of its applications, it has been limited to experts and larger organisations.

In recent years, there has been big advances in machine learning, which essentially is about designing models that learns from data to do predictions and other useful analysis. There have been advances in meta-learning [1], which tries to optimise the parameters of the model by itself as well as learning to play games [2]. This interest in what is called “learning to learn”, has been extended to the emerging field of learning to optimise [3], where machine learning models learn from past problem formations and solving experiences, to learn the optimisation model and appropriate solver.

This field is in its infancy, and there are many open challenges to solve. This project aims to solve some of these, including:

• Problem representation, essentially how to model an optimisation problem such that we can learn problem formulations across different optimisation problems. This might require research into embeddings and kernels [4].

• Learning from past solving experiences, which could involved deep reinforcement learning [3] or other artificial intelligence approaches such as evolutionary computing to learn over previous problems and solving experiences

• Studying the theoretical bounds of learning to optimise, i.e., in which problem types is this type of learning possible.

References 1. C. Lemke, M. Budka, and B. Gabrys, “Metalearning: a survey of trends and technologies”, Artificial

Intelligence Review, 44(1), pp. 117-120, 2015. 2. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wiestra and M. Riedmiller, “Playing

Atari with Deep Reinforcement Learning”, arXiv:1312.5602, 2013. 3. K. Li and J. Malik, “Learning to Optimize”, in Proceedings of 5th International Conference on Learning

Represenation, 2017 4. A. Grover and J. Leskovec, “node2vec: Scalable Feature Learning for Networks”, in Proceedings of 22nd

ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 855-864, 2016


Dr Jeffrey Chan ([email protected]) 1st Supervisor – Office 14.08.15 A/Prof Kai Qin ([email protected]) 2nd Supervisor – Swinburne University, Data Science Institute




Conversational Information Retrieval

CSIT – City Campus

Project Description – Conversations are a natural way for humans to seek information, and there is decades of study on formal dialogs and interactions of users with reference librarians. The next step is to design automated systems that are ‘virtual assistants’, eliciting information needs, correcting misconceptions, providing the right amount of information at the right time across all possible domains. Multi-turn conversations should also become more prevalent in our digital lives due to the proliferation of devices that are accessible anytime/anywhere (without screen or keyboard), the maturity of speech recognition/synthesis approaches, and recent developments in general representation learning. Today’s digital assistants are only capable of very basic “conversations”, which usually means simple user questions (“What’s the weather like today?” or “When does my flight leave tomorrow?”), followed by a single system answer. In contrast, this research will lead to multi-turn, multi-user, multi-task and multi-domain conversational information seeking systems. How best to model and manage state is an important and open problem in Information Retrieval.

Building on extensive recent progress in dialog systems, we distinguish Conversational IR Systems from traditional search system as including capabilities such as long term user state (including tasks that may be continued or repeated with or without variation), taking into account user needs beyond topical relevance (how things are presented in addition to what is presented), and allowing initiative to be taken by either the user or the system at different points of time. As information is presented, requested or clarified by either the user or the system, the narrow channel assumption also means that CIR Systems must address issues including presenting information provenance, user trust, federation between structured and unstructured data sources and summarization of potentially long or complex answers in easily consumable units.

Potential project subtasks (depending on student interests) include machine learning models for CIR, Question Answering with structured and unstructured data, end-to-end system design and evaluation, and efficiency- effectiveness trade-offs in complex CIR systems.

References

1. Radlinski, F., & Craswell: A theoretical framework for conversational search. In Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval (CHIIR 2017), Mar 2017, pp. 117-126.

2. R. Benham and J. S. Culpepper: Risk-Reward Trade-offs in Rank Fusion. Proceedings of the 22nd Annual Australasian Document Computing Symposium (ADCS 2017), Dec 2017, pp 1.1-1.8.

3. R.-C. Chen, L. Gallagher, R. Blanco, and J. S. Culpepper. Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval. Proceeding of the 40th Annual International Conference on Research and Development in Information Retrieval (SIGIR 2017) , Aug 2017, pp 445-454.

Contact Details: To discuss this project further please contact: Dr Shane Culpepper ([email protected]) Supervisor – Office 14.09.21



Dynamic Evaluation of Recommender Systems Computer Science & Information Technology – City Campus

Project Description The problem Recommender systems (RS) are very commonly used and vital in real-life applications, i.e. smart shopping carts, vacation recommender and financial services. There are many different methods and algorithms in RSs which can be applied to the fields. However, finding and evaluating proper RSs can be performed through different methods like online, user study and offline evaluations. Additionally, the evaluation requires proper metrics. Traditionally, accuracy has been the major metric, yet it is believed that accuracy alone is not a sufficient evaluation metric and other metrics like coverage, diversity, scalability and serendipity also should be taken into account.

Finding the best combination of the metrics for specific problems can be a challenging task and the research aims at finding a robust method for this challenge.

Motivation - There already have been several researches in the field and different metrics have been introduced and applied. Metrics and the combinations of the metrics have proven to be inconsistent in varying applications and several changes in evaluation methods and the metrics are required in an application, over a period of time. Hence, some applications are using constantly changing algorithms or manually handpicking recommendations. Thus, a research is being proposed here to develop an adaptive method to overcome this problem at once. The lack of having a consistent method and the importance of RSs, makes the proposed research an ideal research material.

Potential Impact - Nowadays people spend an important portion of their lives in social networks and rely on online and computer based services for a major part of their daily tasks. According to Statista, it is expected to have about 3 billion users of social networks in 2021. According to the same source, In Australia, over 12 million users are using e-commerce services and it’s expected to reach 14.9 million by 2022. The revenue from e-commerce amounts to 10.6 billion USD in the year 2017.

Additionally, it is a fact that RSs are an important part of the mentioned applications and having optimal RSs will definitely have positive impact on the success of the application. Applications that will benefit from the results are online shopping websites, social networks, travel agencies, streaming platforms and other similar services. All stakeholders, including the owners and customers of these applications will benefit from the improved RSs. Improved RSs will generate higher revenues for the application and will make sure the customers will get what they need and/or what they want. In conclusion, financially and scientifically, the research has an undeniable impact.

References 1. B. Kumar and N. Sharma, “Approaches, Issues and Challenges in Recommender Systems: A

Systematic Review,” Indian J. Sci. Technol., vol. 9, no. 47, 2016. 2. M. Taghavi, J. Bentahar, K. Bakthiyari, and C. Hanachi, “New Insights Towards Developing

Recommender Systems,” pp. 1–35, 2017.

Contact Details: To discuss this project further please contact: Dr. Yongli Ren ([email protected]) – Office 14.09.07 Dr. Mahdi Jalili ([email protected]) – Office 14.10.16 - 17 Prof. Falk Scholer ([email protected]) – Office 14.09.22



Reducing Energy Consumption in Computational and Cyber Physical Systems

School of Science, CS & SE Discipline, City Campus


It is forecast that energy consumption will increase worldwide by 33% in the next 15 years. However, such a trend has an adverse effect on sustainable human development and is considered one of the major challenges of the 21st century. This project is concerned with reducing energy consumption in both computational and cyber-physical systems that play a significant role in worldwide energy consumption especially in the commercial and industrial sectors. Areas of focus for reducing energy consumption in computational systems (systems that are primarily computation or software based) that are appropriate areas for PhD research in Computer Science are: • Offloading computation to computational resources/devices that are more energy efficient by design or for a specific purpose • Offloading computation to computational resources/devices that are powered by more efficient energy resources such as renewables • Offloading computation to computational resources closer to the required data and/or users • Optimizing the trade-off between the benefits of computation offloading (as in the first three points above) and versus the increased cost of data transmission over the network. Cyber-physical systems refer to systems that in addition to a significant computational component (as described above) also include physical machinery and artefacts that are software/computer controlled. Such systems include industrial automation and robotics, SCADA systems, and Smart Grids. In addition to the opportunities described above, such systems involve additional challenges: • Which parts of a cyber physical system can be offloaded to improve computational efficiency and therefore reduce energy consumption? • How can industrial scheduling be optimised to reduce the overall energy cost of such systems based on energy cost, and availability of renewable energy resources? • How can data measuring external factors such as weather, heating, ventilation and cooling requirements (HVAC), and real-time energy cost from the smart grid, be used to further optimize the scheduling of industrial activities involving cyber-physical systems? Previous related work involving the supervisor that may be of interest to prospective candidates [1-4].

References [1] K. H. Khan, C. Ryan and E. Abebe, Day Ahead Scheduling to Optimize Industrial HVAC Energy Cost

Based ON Peak/OFF-Peak Tariff and Weather Forecasting, in IEEE Access, vol. 5, pp. 21684-21693, 2017.

[2] K. H. Khan; C. Ryan; E. Abebe, Optimizing HVAC Energy Usage in Industrial Processes by Scheduling based on Weather Data, in IEEE Access, Vol. 5(1), 2017, DOI: 10.1109/ACCESS.2017.2715239

[3] C. Li, Y. Xu, X. Yu, C. Ryan and T. Huang, Risk-Averse Energy Trading in Multienergy Microgrids: A Two-Stage Stochastic Game Approach, in IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp. 2620-2630, Oct. 2017. doi: 10.1109/TII.2017.2739339

[4] Abebe, E., Ryan, C., Adaptive Application Offloading Using Distributed Abstract Class Graphs in Mobile Environments, Journal of Systems & Software (2012), Elsevier, vol 85. no. 12, pp. 2755–2769, DOI: 10.1016/j.jss.2012.05.091

Contact Details: To discuss this project further please contact: Dr. Caspar Ryan ([email protected]) Supervisor – Office 14.11.32



Scalable and Transferrable Occupant Behaviour Learning with Multi- Sensor Data from Multi-region Living Labs

Computer Science, City PhD Project

Project Description The focus of this PhD project will be on data-driven occupant research methods to support evidence-based decision-making. The development in machine learning and data mining methods that are generic and robust to multiple sensing modalities will enable a rich insight about occupant behaviors. This wealth of building sensor data opens new opportunities for extracting knowledge from data and data-driven modeling of occupant behavior. Among others, the data offers opportunities for creating models that are more individually customized to the particular occupant, building or climate zone. This wealth of data also creates new threats to the occupant in terms of the violation of the individuals’ right to privacy that has to be addressed.

The goal is to demonstrate how in situ data can be converted to meaningful information with key traits (e.g., occupant diversity, temporal trends, triggers, patterns and relationships). The project will investigate the potential applications of data-based methods for knowledge discovery and modeling of occupant behavior. The project will establish and evaluate new data-driven research opportunities in the light of the rich variety of models already developed in the field to understand the advantages and disadvantages of the different modeling paradigms.

The rich datasets and online repositories from the two living labs– RMIT and University of Southern Denmark – will be used by the PhD candidate. OU44, the 8500m2 3-storey building, is a highly energy efficient teaching building at the University of Southern Denmark in Odense, and a living lab for research in energy informatics and occupancy behavior, with full capability to monitor, manage and control the building operation. The building is equipped with energy efficient technologies including, ventilation units with heat recovery, LED lights, underfloor heating, PV modules, and heating, lighting and electricity consumption sub- meters, and temperature, humidity, CO2, Lux and PIR sensors on the room level. RMIT and a project on activity based working in multiple offices of Arup Melbourne and Sydney provide another living lab with multi-sensor, multi-device tracking data of occupants.

This project will extend the existing research by Dr. Salim and Prof Kjaergaard on occupant sensing, tracking, and prediction. The candidate needs to have a strong background in algorithms and data mining/machine learning. This project will contribute to the development of a new International Annex by IEA EBC (International Energy Agency's Energy in Buildings and Communities) on “Occupant behaviour-centric building design and operation”, particularly the Sub Task 2 on “data-driven methods for occupant behavior modelling”.

References [1] A.J.R. Ruiz, H. Blunck, T.S. Prentow, A. Stisen, M. B. Kjærgaard, Analysis methods for extracting knowledge from large-scale WiFi monitoring to inform building facility planning. PerCom 2014, pp. 130-138 [2] I.B.A-Ang, M. Hamilton, F. D. Salim, RUP: Large Room Utilisation Prediction with carbon dioxide sensor, Pervasive and Mobile Computing, Volume 46, 2018, Pages 49-72.

Contact Details: To discuss this project further please contact Dr. Flora Salim [email protected] 1st Supervisor – Dr. Flora Salim, School of Science, RMIT University. 2nd Supervisor –Prof. Mikkel Kjaergaard, University of Southern Denmark




Abnormal Event Detection in Big Video Data


Project Description – Abnormal event detection is a hot research issue for many applications like video surveillance and security. It aims to automatically detect unusual events from videos. Due to the limited availability of anomaly, developing learning method based on normal video data is the main strategy to address this issue. Although many methods have been proposed, such as probabilistic models [1, 2], dictionary learning [3, 4, 5, 6], and deep learning [7, 8, 9], abnormal event detection is still challenging. For one thing, the information carried by anomalies is difficult to be captured (we aim to address it in the problems 1 and 2). For another, using probabilistic models and dictionary learning, it’s not easy to design hand-crafted features to describe the complex video data. In this project, we aim to study four research problems: 1. How to utilize the information carried by anomalies? It’s hard to find the common patterns of anomalies, since abnormal events can be caused by various reasons. Lacking of labelled anomalies leads to the loss of anomaly information. This project aims to exploit anomalies and improve the normal event detector. The intuition is that anomalies in the continuous frames of a video are similar, but different from normal video. 2. How to propose a learning method only using the video-level label? It’s hard to describe the location of anomalies. For most supervised learning methods, both normal and anomaly training data are required. Especially, to locate anomaly in a frame, patch-level label is demanded. Thus, supervised learning is quite limited in some situations of abnormal event detection. In this project, we plan to design our method based on the video-level label. Multi-instance learning model can be the option. Its bag-instance structure matches video-frame data well. 3. How to generate the feature representation? While multiple features have been designed for abnormal event detection, it is still a challenging to decide which features are more effective in anomaly detection. We plan to adopt feature fusion and selection on multi-view features to develop an effective feature representation model. 4. How to developing a real-time abnormal event detection system? In practice, real-time detection meets more real-world tasks. This project aims to accelerate the detection from several aspects: feature extraction, decision function selection, detection over distributed environment etc.

Required Background: Algorithm, Abnormal Event Detection, Video Analysis

References [1] W. Li, V. Mahadevan, N. Vasconcelos: Anomaly Detection and Localization in Crowded Scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1): 18-32 (2014). [2] M.J., Roshtkhari, M.D., Levine: Online dominant and anomalous behavior detection in videos. CVPR 2013: 2611-2618. [3] Y. Cong, J. Yuan, J. Liu: Sparse reconstruction cost for abnormal event detection. CVPR 2011: 3449-3456. [4] C. Lu, J. Shi, J. Jia: Abnormal Event Detection at 150 FPS in MATLAB. ICCV 2013: 2720-2727. [5] H. Wen, S. Ge, S. Chen, H. Wang, L. Sun: Abnormal event detection via adaptive cascade dictionary learning. ICIP 2015: 847-851. [6] H. Ren, W. Liu, S. I. Olsen, S. Escalera, T. B. Moeslund: Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection. BMVC 2015: 28.1-28.13. [7] D. Xu, E. Ricci, Y. Yan, J. Song, N. Sebe: Learning Deep Representations of Appearance and Motion for Anomalous Event Detection. BMVC 2015: 8.1-8.12. [8]Y. Feng, Y. Yuan, X. Lu: Deep Representation for Abnormal Event Detection in Crowded Scenes. ACM Multimedia 2016: 591-595. [9] H. Lin, J. D. Deng, B. J. Woodford, A. Shahi: Online Weighted Clustering for Real-time Abnormal Event Detection in Video Surveillance. ACM Multimedia 2016: 536-540.

Contact Details: To discuss this project further please contact: Dr. Xiangmin Zhou ([email protected]) Supervisor – Office 14.08.13






This research project investigates novel approaches to the situation-aware social recommendation problem by

fusing media content, contexts and user situations to support individuals in smart decision making. In particular, we aim to invent new theories, models, efficient indexing and query processing techniques for effective and efficient recommendation over complex and dynamic big social media streams that contain metadata and different context and user situation information. This is driven by the demands of the online global services, such as mobile smartphones and GPS, and media shared services like Youtube and Twitter, and online e-business, like Netflix, Amazon etc. This project will:

1. Build novel data model to support the high effectiveness of recommender system. Effective relevance functions will be learnt by analysing the data attributes. 2. Determine what types of contexts, user situations and contents best suit a recommender system or a target user over the data streams of a particular online platform, so as to enhance the personality of the system; 3. Design efficient solutions to support the efficient recommendation and dynamic update maintenance of system over distributed environment like Apache Storm and Spark.

Motivation and comparison with the literature: Most recommender systems use contexts from a representational view [1], which defines contexts as a predefined attribute set and the structure does not change over time. Though previous studies have context definitions catered for applications and domains, like the behavioural categories[2], article popularity[3], social trust[4], and user interactions and activities [5][6][7], they utilize the pre-specified contexts in data modelling which cannot adaptively capture the most compact while discriminative feature set for various applications in dynamic streaming environment. In addition, the situational information about users cannot be captured. Existing solutions cannot effectively and efficiently perform recommendation in dynamic communities. They will produce low quality results if the dominant contexts change over streaming.

Required Background: Algorithm, Data Management and Mining, Indexing Techniques

References [1] G. Adomavicius and A. Tuzhilin: Context-Aware Recommender Systems. In Recommender Systems

Handbook, 2011. [2] M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu: Social contextual recommendation. CIKM, 2012. [3] R. Kumar, B K Verma and S. S. Rastogi: Context-aware Social Popularity based Recommender System.

IJCA, 92(2):37-42, April 2014. [4] X. Yang, H. Steck, and Y. Liu: Circle-based recommendation in online social networks. SIGKDD, 2012. [5] Y. Huang, B. Cui, J. Jiang, K. Hong: Real-time Video Recommendation Exploration. SIGMOD 2016. [6] X. Zhou, L.Chen, Y.Zhang, etc: Enhancing online video recommendation using social user

interactions. VLDBJ. 26(5): 637-656 (2017) [7] Y. Ren, M. Tomko, F. Salim, J. Chan, M. Sanderson: A Location-Query-Browse Graph for Contextual

Recommendation, IEEE TKDE.







This research project investigates novel approaches to the situation-aware social recommendation problem by fusing media content, contexts and user situations to support individuals in smart decision making. In particular, we aim to invent new theories, models, efficient indexing and query processing techniques for effective and efficient recommendation over complex and dynamic big social media streams that contain metadata and different context and user situation information. This is driven by the demands of the online

global services, such as mobile smartphones and GPS, and media shared services like Youtube and Twitter, and online e-business, like Netflix, Amazon etc. This project will:

1. Build novel data model to support the high effectiveness of recommender system. Effective relevance functions will be learnt by analysing the data attributes. 2. Determine what types of contexts, user situations and contents best suit a recommender system or a target user over the data streams of a particular online platform, so as to enhance the personality of the system; 3. Design efficient solutions to support the efficient recommendation and dynamic update maintenance of system over distributed environment like Apache Storm and Spark.




Handbook, 2011. [2] M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu: Social contextual recommendation. CIKM, 2012. [3] R. Kumar, B K Verma and S. S. Rastogi: Context-aware Social Popularity based Recommender System.

IJCA, 92(2):37-42, April 2014. [4] X. Yang, H. Steck, and Y. Liu: Circle-based recommendation in online social networks. SIGKDD, 2012. [5] Y. Huang, B. Cui, J. Jiang, K. Hong: Real-time Video Recommendation Exploration. SIGMOD 2016. [6] X. Zhou, L.Chen, Y.Zhang, etc: Enhancing online video recommendation using social user

interactions. VLDBJ. 26(5): 637-656 (2017) [7] Y. Ren, M. Tomko, F. Salim, J. Chan, M. Sanderson: A Location-Query-Browse Graph for Contextual



School of Science

DR222 – PhD (Mathematical Sciences) MR222 Master of Science (Mathematical Sciences)

HDR Project 2018

Application of Variational Analysis to the Stability\Sensitivity of

Optimisation problems Mathematical Sciences – City

Project Description In numerical analysis measure of how much a solution is changed when the input data varies are often qualified by measures of conditioning. Depending on the problem class the condition number may be defined in numerous ways but is essential the variation of the change in output compared with the change in input. These measures have wide use in stability and sensitivity analysis.

A related but potential more general framework consist of relating the distance a given problem is from the set infeasible or ill-defined problems, in their respective problem space, to a structural measure of that problem class. This fits the format of so-called radius theorems which have the following form: given a mapping with a certain desirable property, the “radius” of this property is the “minimal perturbation” such that the perturbed mapping may lose this property. An early example in linear algebra is a radius theorem that goes back to a paper by Eckart and Young [1]: for any nonsingular matrix A, can we characterize the smallest perturbation B so that A+B is singular. This distance, measured in the norm of B, is known to be equal to the inverse of the matrix norm of the inverse of A. That is, radius = 1/regularity [4, 5].

In this project we will study radius theorem for optimization problems of various classes. We do not simply want to consider a distance to infeasibility but the distance to the loss of important structural properties of the problem. These could be the existence of a strict local minimum, a tilt stabe local minimum [3] to the failure of optimality condition [2] or to the failure of related optimization algorithms to converge. We will be in particular interested in classes of conic optimisation problems.

References 1]. Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrica 1, 211– 218 (1936) [2]. A.L. Dontchev, A. Eberhard and R.T. Rockafellar, Radius Theorems for Monotone Mappings, Set Valued Analysis, (accepted 30/01/2018). [3].A. Eberhard, Y. Luo and S. Liu, On Partial Smoothness, Tilt Stability and the UV decomposition, Mathematical Programming series A., (accepted on 16/02/2018). [4]. A. Eberhard, V. Roshchina, and T. Sang, (In Press) Outer limits of subdifferentials for min-max type functions, in Optimization, Taylor and Francis, United Kingdom, pp. 1-19 ISSN: 0233-1934, 2017. [5]. Asen L. Dontchev and R.T Rockafellar Implicit Functions and Solutions Mappings, A View from Variational Analysis, second edition, Springer 2014.


Professor Anthony Eberhard (Email:[email protected]) Dr Yousong Lou (Email: [email protected])


School of Science


HDR Project 2018


Mathematical Sciences – City Project Description This research aims to solve/compute the equilibria in smooth measurable Bayesian games, using infinite dimensional Variational Inequalities (VI), [1] in a Hilbert space and then use order theoretic-projection based methods to perform higher order distributional comparative statics/sensitivity analysis of the equilibria to the belief structures in Bayesian games.

We assume that each player’s payoff function is weakly coercive and r-concave on its own actions, which gives us a set of pseudo-monotone VIs. The VIs are solvable by constructing weakly compact subsets of possible strategy spaces for each player, including their best replies. Then, we need sufficient assumptions satisfying the conditions for applying a proper fixed-point theorem on the best reply correspondence. Related to this problem, we can also study whether the use of the so-called Noisy stochastic games approach - introduced by Duggan [2] and He & Sun [3], and recently applied in Bayesian games by Meneghel & Tourky [4], might be helpful in weakening sufficient conditions for existence in pseudo-concave Bayesian games. Here, payoff gradients have pseudo-monotonicity properties [7,8]. Given the ordinal nature of pseudo- monotonicity, we can translate the problem to one stochastic pseudo-monotone VI [5], leading to the study of infinite dimensional pseudo-monotonicity associated with Bayesian games.

References 1. T. Ui, "Bayesian Nash Equilibrium and Variational Inequalities," Journal of Mathematical Economics,

pp. 139-146, 2016 2. J. Duggan, "Noisy Stochastic Games," Econometrica , 2012. 3. Wei, He; Yeneng, Sun, "Stationary Markov Perfect Equilibria in Discounted Stochastic Games," Journal

of Economic Theory, 2017. 4. I. Meneghel, R. Tourky, "Monotone purification in Bayesian games," 2013. 5. Aswin Kannan , Uday V. Shanbhag, "The pseudomonotone stochastic variational inequality problem:

Analytical statements and stochastic extragradient schemes," in American Control Conference (ACC), Portland, OR, USA, 2014.

6. Crouzeix J-P. , Eberhard A. and Ralph D., (2010), A geometrical insight on pseudoconvexity and pseudomonotonicity, Mathematical Programming, Series B, 123, pp. 61-83.

7. Eberhard, A.C. and Crouzeix J-P (2007), Existence of Closed Graph, Maximal, Cyclic Pseudo-Monotone Relations and Revealed Preference Theory, The Journal of Industrial and Management Optimization, Vol. 3, no. 2, pp 233—255.

Contact Details: Professor Andrew Eberhard (Email: [email protected]) Second Supervisor: To be advised.

DR222 – PhD (Mathematical Sciences) MR222 – Master of Science (Mathematical Sciences)


Optimal routing and scheduling

Mathematical Sciences – City Campus

Project Description – Changes in government funding for Home Care have necessitated providers becoming more competitive. The provision of Home Care is challenging in that some clients need to be visited by more than one carer simultaneously. Such synchronous visits have to be within a daily schedule that also requires visits to clients needing only one carer. Further challenges lie in matching client’s requirements with the carer’s capabilities. For example, some clients might require a qualified nurse to give injections while others might require a strong person to lift a patient. Matching skills with requirements within a routing and scheduling problem is not well-researched and yet is becoming of growing importance. This project will require the formulation of appropriate optimization models. These models will then have to be tested with realistic data.

References

1. “The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies” M S Rasmussen , T Justesen, A Dohn, J Larsen, EJOR 219 (2012) 598-610.

Contact Details: To discuss this project further please contact: Professor John Hearne ([email protected]) Supervisor – Office 08.09.11



Optimal routing and scheduling - heuristics


Project Description – Changes in government funding for Home Care have necessitated providers becoming more competitive. The provision of Home Care is challenging in that some clients need to be visited by more than one carer simultaneously. Such synchronous visits have to be within a daily schedule that also requires visits to clients needing only one carer. Further challenges lie in matching client’s requirements with the carer’s capabilities. For example, some clients might require a qualified nurse to give injections while others might require a strong person to lift a patient. Matching skills with requirements within a routing and scheduling problem is not well-researched and yet is becoming of growing importance. These problems can be formulated as Mixed Integer Programming models and small instances can be solved using commercial solvers such as CPLEX and Gurobi. Unfortunately the mathematical properties of such models preclude the use of standard solution methods for large instances of the problem. In this case it is necessary to develop heuristic methods to solve the problems in times suitable for operational purposes. This project will involve the develop of these heuristics.

References 1. “The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies”

M S Rasmussen , T Justesen, A Dohn, J Larsen, EJOR 219 (2012) 598-610.


DR222 – PhD (Mathematical Science) MR222 – Master of Science (Mathematical Science)



Mathematical Science – City Campus

Project Description – The recent scandal featuring Facebook and Cambridge Analytica highlights the use and misuse of social media to influence elections. The ever increasing use of social networks by individuals and companies, and the harvesting of such information, indicates that there are a number of questions that need answering in this context. One such question is to understand the connections that can be deduced from the information uploaded by a user, whether it is in the form of likes or comments, thumbs-up or down, tweets or re-tweets.

The study of the effectiveness of election campaigning has been increasing, with a large number of papers (for example [1, 2]) measuring effectiveness using the bare count of likes, re-tweets etc. In [3], the analysis of this sort of bare count was strengthened, using a statistical tool from the risk analysis of heart surgery [4], to gain an understanding of the interaction between posting and the likes gathered. While the research showed that being very prolific in posting does not necessarily lead to popularity in social media or, for that matter, in actually winning an election, it allowed for a more nuanced understanding.

This project aims to use tools from complex networks and statistics to further the understanding of connections between the people posting and the ones liking, or retweeting. The project will look for tools outside of the area of data analysis to do this exploration.

The candidate should have a background in mathematics, specifically in complex networks and statistics. The candidate should also be familiar with programming using R, and will be expected to obtain publicly available data.

References 1. Caldarelli G, Chessa A, Pammolli F, Pompa G, Puliga M. A Multi-Level Geographical Study of Italian

Political Elections from Twitter Data. PLoS ONE 2014; 9(5):e95809. https://doi.org/10.1371/journal.pone.0095809 PMID: 24802857

2. Giglietto Fabio. If Likes Were Votes: An Empirical Study on the 2011 Italian Administrative Elections. In:Sixth International AAAI Conference on Weblogs and Social Media 2012; Trinity College, Dublin, Ireland;AAAI Press.

3. Khairuddin MA, Rao A (2017), Significance of likes: Analysing passive interactions on Facebook during campaigning. PLoS ONE 12(6): e0179435. https://doi.org/10.1371/journal.pone.0179435

4. Novick Richard J, Fox Stephanie A, Stitt Larry W, Forbes Thomas L, Steiner Stefan. Direct comparison of risk-adjusted and non±risk-adjusted CUSUM analyses of coronary artery bypass surgery outcomes. The Journal of Thoracic and Cardiovascular Surgery 2006; 132(2):386-391. https://doi.org/10.1016/j. jtcvs.2006.02.053 PMID: 16872967


Prof Asha Rao ([email protected]) Jessica Liebig ([email protected])




Mathematical Sciences – City

Project Description – Location and routing problems are very widely studied in supply chain management (SCM) literature with the aim of minimising costs of satisfying demands. Drexl and Schnedier (2015) surveys over a hundred papers in that field. Unfortunately, similar problems in the public domain dealing with location of permanent or temporary healthcare, shelter, or relief facilities and routing of emergency vehicles are not studied as widely. We have seen a recent increase in the frequency and magnitude of emergencies caused by natural disasters from earthquakes, hurricanes, wildfires to floods. On top of that, the ever-growing scale of events bringing people together, i.e. Olympics, World Cup, etc. where they are exposed to emergencies caused by overcrowdedness, poor preparedness or terrorism, increases the impact of such emergencies. Abounacer et al. (2014) considered the solution of a three-objective location and transportation problem for disaster response. Rath and Gutjahr (2014) developed an evolutionary math-heuristic for locating warehouse and routing relief efforts after a disaster. Wang et al (2014) developed a multi-objective location/routing model for post- earthquake relief distribution. Rodríguez-Espíndola et al (2017) work on collaborative preparation efforts for floods with a case study in Mexico. Despite these very recent efforts, this field deserves significantly more effort to handle various types of disasters and planning and preparation for large scale events. So there clear is a need for bridging the gap in knowledge for better informing the planning and preparation for such emergencies. In this project we aim to develop models, algorithms and tools to tackle emergency facility and vehicle routing problems.In this project we aim; to implement/translate the SCM based location/routing models into the emergency facility location and vehicle routing problems and identify areas of application, solve these newly developed models that will minimise the impact of such emergencies using commercial solvers, design new algorithms to solve these problems faster compared to the commercial solvers, to package these models and algorithms into open source tools that can be used by planners all around the globe to have a real practical impact.In order to achieve our goals, we will; complete an extensive survey of the literature of existing approaches in SCM and the limited literature on emergency facility location and routing problem identify problem domains in emergency facility location that has the best potential to improve the current approaches and significantly improve the outcomes, formulate models using SCM literature and extensions to cover the emergencies and their underlying causes, develop exact and approximate algorithms to solve the models developed in practical time frames with sufficient precision, disseminate results in terms of papers, conference talks, and open source tool set that can be utilised by decision makers globally.

References

1. Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective location–transportation problem for disaster response. Computers & Operations Research, 41, 83-93.

2. Drexl, M., & Schneider, M. (2015). A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241(2), 283-308.

3. Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research, 42, 25-39.

4. Rodríguez-Espíndola, O., Albores, P., & Brewster, C. (2017). Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods. European Journal of Operational Research.

5. Wang, H., Du, L., & Ma, S. (2014). Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Transportation Research Part E: Logistics and Transportation Review, 69, 160-179.

Contact Details: To discuss this project further please contact: Assoc. Prof.. Melih Ozlen ([email protected]) Supervisor – Office: B008 F09 R038




Mathematical Sciences – City Project Description – In this project we focus on Multi-Objective Mixed Integer Programming (MOMIP) problem with the specific aim of developing algorithms to generate the trade-off curve and to optimise a utility function over it. We are hoping to transform these algorithms into a state-of-the-art computational tool that can then be used to make economic, environmental, safe and sustainable decisions. Current studies on multi-objective programming problems focus on either pure integer programming (MOIP) or pure linear programming (MOLP). MOIP problems involve decisions represented either by integer or binary (yes/no) variables mostly coming from the domain of combinatorial optimisation. However, MOLP problems do not have any integer/binary variables, they only have continuous decision variables. Based on this important distinction, algorithms and methods that deal with these problems are significantly different. Approaches developed for MOIP mostly take advantage of the combinatorial nature of the underlying problem [12] and/or well defined increments of the individual objective functions [7, 8, 10]. However, any solution of the MOLP problem lies on the boundary of the convex hull of extreme supported solutions, and focus is on identifying this set of solutions [2, 3, 4, 5, 6]. Although there are studies dealing with MOIP and MOLP problems independently, there is a gap in the literature for studies dealing with their hybrid, MOMIP problem. Methods developed for MOIP cannot directly be used on MOMIP as it generally lacks the purely combinatorial nature due to the existence of continuous variables. Similarly, methods developed for MOLP cannot be utilised as MOMIP has solutions that are not on the boundary of the convex hull of extreme supported solutions but rather lie inside the boundary which are classified as unsupported solutions. Within this project we are aiming; to develop new algorithms for generating the trade-off curve for MOMIP problem, to develop new algorithms for optimising a utility function over the efficient set of MOMIP problem, implement new algorithms on parallel architectures, transform algorithms into a computational tool. In order to achieve these aims we will; develop new algorithms based on existing MOLP & MOIP approaches, develop novel algorithms based on computational geometry, and global optimisation, parallelise developed algorithms to take advantage of modern computing architectures, and develop a computational tool.

References

[1] H. P. Benson, 1998a, ”Further analysis of an outcome set-based algorithm for multiple-objective linear programming”, Journal of Optimization Theory Applications 97(1):1-10. [2] H. P. Benson, 1998b, “Hybrid approach for solving multiple-objective linear programs in outcome space”, Journal Optimization Theory and Applications 98(1):17-35. [3] H. P. Benson., 1998c, “An outer approximation algorithm for generating all efficient extreme points in the outcome set of a multiple objective linear programming problem”, Journal of Global Optimization 13(1):1-24. [4] H. P. Benson and E. Sun, 2000, “Outcome space partition of the weight set in multiobjective linear programming”, Journal of Optimization Theory and Applications 105(1):17-36. [5] H. P. Benson and E. Sun, 2002, “A weight set decomposition algorithm for finding all efficient extreme points in the outcome set of a multiple objective linear program”, European Journal of Operational Research 139(1):26-41. [6] B.A. Burton and M. Ozlen., 2010, “Projective geometry and the outer approximation algorithm for multiobjective linear programming”, arXiv:1006.3085. [7] M. Laumanns, L. Thiele, and E. Zitzler, 2006, “An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method”, European Journal of Operational Research 169(3):932-942. 8 M len and M A i o lu 200 Multi-objective integer programming: a general approach for

generating all non-dominated solutions”, European Journal of Operational Research 199(1):25-35.



M len M A i o lu and B A Burton 201 ptimising a onlinear tility Function in Multi- Objective Integer Programming”, Journal of Global Optimization, 56 (1), 93-102.

[10] M. Ozlen, B.A. Burton and C.A.G. MacRae, 2013, “Multi-Objective Integer Programming: An Improved Recursive Algorithm”, Journal of Optimization Theory and Applications. doi: 10.1007/s10957-013-0364-y.

[11] O. Ozpeynirci and M. Koksalan, 2010, “An exact algorithm for finding extreme supported nondominated points of multiobjective mixed integer programs”, Management Science 56(12):2302-2315.

[12] A. Przybylski, X. Gandibleux, and M. Ehrgott, 2010a, “A two phase method for multi-objective integer programming and its application to the assignment problem with three objectives”, Discrete Optimization 7(3): 149-165.

[13] A. Przybylski, X. Gandibleux, and M. Ehrgott, 2010a, “A recursive algorithm for finding all nondominated extreme points in the outcome set of a multiobjective integer programme”, INFORMS Journal on Computing 22(3):371-386.


DR221 – PhD (Mathematical Sciences) MR222 –Master of Science (Mathematical Sciences)


Frictional Pebble Games in Graphs and Applications Mathematical Sciences – City

Project Description This project relates the general topic of analysing complex systems. Rigidity percolation is originally motivated by the problem of identifying independent rigid structures in physical networks dominated by central forces (assemblies of frictionless particles, structural networks, protein folding). It is related to the number of independent structural degrees of freedom in the system and harks back to the seminal work of Maxwell [1]. The fundamental nature of this constraint problem means it is also closely related to the graph reconstruction problem, with applications in particular in sensor networks and autonomous vehicle routing, mainly for localising devices based on their relative distances (see, e.g., [3]).

Whilst zero-energy perturbation methods can solve it in theory, in practice the continuous nature of the rigidity percolation problem prevents the reliable identification of rigid and floppy structures for large systems [4]. In contrast, the theorem of Laman [2] gives a purely combinatorial rigidity criteria using graph theory. However, it results in an algorithm that scales exponentially with the number n of vertices (particles). The development of the pebble game in graphs by Jacobs and Hendrickson [4] led to an O(n2) algorithm for 2D assemblies with 2 degrees of freedom and 3 trivial rigid body motions. It is difficult to overstate the impact of this algorithm, which has identified new universality classes of critical phenomena, and has rendered tractable entire classes of problems in many fields, including materials science, condensed matter physics, biophysics but also sensor networks and cyber security.

However, despite these advances, rigidity percolation for the important class of friction-dominated materials like granular assemblies is poorly understood. Sliding friction constraints and additional degr es of freedom make it much more challenging. Whilst Lee and Serienu [5] have identified generic (k,l)-pebble game algorithms for such broader classes of problems, a major outstanding barrier is the lack of an equivalent of Laman’s theorem for frictional contacts. However there exist strong indications that frictional rigidity percolation is governed by a combination of (k,l)-pebble games [6], which motivates the development of a frictional pebble game.

In this thesis, we propose to undertake fundamental research into (k,l)-pebble game algorithms on graphs to better solve the frictional rigidity percolation problem. The huge impact of the regular pebble game in various topics motivates us investigating the potential of this generalisation for other applications, in particular for sensor networks, and how these topics could learn from each other using this abstraction.

References [1] James Clerk Maxwell F.R.S. Letter on the calculation of the equilibrium and stiffness of frames. Philosophical Magazine, 27(182): 294-299 (1864) [2] G. Laman. On graphs and rigidity of plane skeletal structures. Journal of Engineering Mathematics, 4(4): 331-340, (1970) [3] Anthony Man-Cho So and Yinyu Ye, Theory of semidefinite programming for Sensor Network Localization. Mathematical Programming (B) 109(2), 367-384 (2007). [4] Donald J. Jacobs and Bruce Hendrickson, An algorithm for two-dimensional rigidity percolation: The pebble game, Journal of Computational Physics, 137(2): 346-365 (1997) [5] Audrey Lee and Ileana Streinu. Pebble game algorithms and sparse graphs, Discrete Mathematics, 308(8): 1425-1437 (2008). [6] Daniel Lester and Ruru Li, The frictional pebble game: an algorithm for rigidity percolation in saturated frictional assemblies, Journal of Computational Physics (2017)

Contact Details: To discuss this project further please contact: Associate Professor Marc Demange (Email:[email protected]) Dr Daniel Lester (Email:[email protected])



Colouring circle graphs: interval representation, algorithms and applications

Mathematical Sciences – City Project Description Circle graphs (also known as overlap graphs) [6] are the intersection graphs of chords of a circle. They can be equivalently defined by a set of intervals of the real line. We say that two intervals overlap if they have a non- empty intersection but none is contained in the other. In an overlap representation, vertices are the intervals and edges represent overlap of two intervals. This class of graphs appears notably in some logistics problems and in particular stacking problems (see, e.g., [1], [12]). After unloading a ship, containers are stored in the port before being uploaded again on another ship or truck. Due to space and logistics constraints, stored containers are stacked such that the container needing to leave first is preferably on the top of a stack. Minimising the space used for storing corresponds to colouring the vertices of the related graph such that two adjacent vertices are of different colours and the total number of colours is minimum. Colouring overlap graphs has also applications in VLSI design and for the book-embedding problem of a general graph [10]. Natural generalizations include, in particular, sub-tree filament graphs [7]. Colouring overlap graphs with a minimum number of colours is known as NP-hard [10] and our project is essentially motivated by the question of finding efficient algorithms for colouring such graphs and their generalisations. We consider in particular the possibility to approximate this problem with guaranteed approximation ratio but also the online setting where intervals are revealed over the time and one needs to assign colours when intervals are revealed and without any knowledge about the next intervals. This set-up is very natural in stacking problems. Few results are known about approximation and online competitive algorithms for colouring overlap graphs [3, 4,5,8,9]. The structure of overlap graphs is still only very partially understood [6], and recent results (see, e.g., [5]) show how structural results can be turned into efficient algorithms. This project aims to investigate in a more systematic way the structure of overlap graphs and in particular hereditary subclasses and use it for the design of new efficient algorithms for generalised overlap graph colouring problems. We will consider both an offline set-up (all intervals are known in advance) and online one. We plan to consider new domains of applications that will motivate variants of our problems.

References 1. M. Avriel, M. Penn, and N. Shpirer. Container ship stowage problem:

complexity and connection to the coloring of circle graphs. Discrete Applied Mathematics , 103(1-3): 271- 279, 2000.

2. J. Cerny. Coloring circle graphs. Electronic Notes in Discrete Mathematics 29:457-461, 2007. 3. S. Cornelsen and G. Di Stefano. Track assignment. Journal of Discrete Algorithms, 5(2):250-261, 2007. 4. M. Demange, G. Di Stefano, and B. Leroy-Beaulieu. On the online track assignment problem. Discrete

Applied Mathematics, 160(7-8): 1072-1093, 2012. 5. M. Demange and M. Olsen. A Note on Online Colouring Problems in Overlap Graphs and

TheirComplements. In: Rahman M., Sung WK., Uehara R. (eds) WALCOM: Algorithms and Computation. WALCOM 2018. Lecture Notes in Computer Science 10755: 144-155, 2018, Springer, Cham

6. M.C. Golumbic. Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics 57). North- Holland Publishing Co., Amsterdam, The Netherlands, 2004.

7. J.M. Keil and L. Stewart. Approximating the minimum clique cover and other hard problems in subtree filament graphs. Discrete Applied Mathematics, 154(14): 1983-1995, 2006.

8. F. G. Konig and M. E. Lubbecke. Sorting with complete networks of stacks. In ISAAC 2008, Proceedings: 895-906, Berlin, Heidelberg, 2008. Springer Berlin Heidelberg.

9. F. Shahrokhi. A new upper bound for the clique cover number with applications. arXiv preprint arXiv:1502.06168 , 2015.

10. W. Unger. On the k-colouring of circle-graphs. In STACS 88, Proceedings: 61-72, Berlin, Heidelberg, 1988. Springer Berlin Heidelberg.

Contact Details: Associate Professor Marc Demange (Email: [email protected]) Dr Martin Olsen (Email: martino.btech.au.dk) Dr Graham Clarke (Email: [email protected])



Modelling of anaerobic digester reactor incorporating delays Mathematical Sciences – City

Project Description The aim of this project is to mathematically model methane production from sewage, vegetable or animal wastes in an anaerobic reactor, promoting sustainability by replacing fossil fuel generated methane by the more sustainable methane from wastes. The mathematical modelling of such reactor dynamics is complex and can involve large systems of nonlinear differential equations whose analysis can require sophisticated numerical processes - see [1], which involves 32 state variables. A greatly simplified model [2] involves 4 state variables and this reduced complexity allows some general analytical analysis of the behaviour of the reactor. Finally, a greatly simplified model based on [3] reduces the number of state variables to 2 - two species of bacteria: the acidogenic bacteria which consume the initial complex solids substrates to produce volatile fatty acids and the methanogenic bacteria which feed on these volatile fatty acids to produce methane - but preserves the nonlinearity of the species' interaction and much of the complexity of the reactor's behaviour. In all the models described above, values of the state variables are determined instantaneously in time. However, it is possible that such values can involve delays, so that variable values draw on information from some set previous time. Moreover, the delays and the defining model parameters themselves may in fact change with time. With these two modifications, the simplified two species model described above can be expected to display very complex behaviour, with possible significant instabilities.indeed. Thus, this project aims to use analytic methods to study the behaviour of the components of this nonlinear two species model when it involves varying delays and varying model parameters. Some preliminary work [4] by the project proposers involving a constant delay has proved to be encouraging.

References 1. D.J.Batstone, J.Keller, R.I.Angelidaki,S.V. Kalyuzhny, S.G. Pavlostathis, A.Rozzi, et al. The IWA

anaerobic digestion model No 1 (ADM1). Water Science and Technology, 45(10), 6573,(2002) 2. D.T. Hill, Simplified Monod kinetics of methane fermentation of animal wastes. Agricultural wastes, 5.1

(1983): 1-16 3. J. F. Andrews. A mathematical model for the continuous culture of microorganisms utilizing inhibitory

substrates, Biotechnology and Bioengineering, 10(6),pp. 707-723, (1968). 4. Khan, A. Shepherd, J. and Stacey, A. 2016, 'Numerical modelling of anaerobic digestion reactor

dynamics with a delay', in ANZIAM Journal, Cambridge University Press, United Kingdom, vol. 57, pp. 142-159 ISSN: 1446-8735

Contact Details: Associate Professor John J Shepherd (Email: [email protected]) Dr Andrew Stacey (Email: [email protected]) Dr Ashfaq Khan (Email: [email protected])




Mathematical Science Discipline/ Applied Statistics Research Group -City

Project Description – The deaths of a new-born or a pregnant woman, during, at and after delivery continues to be a serious and sensitive issue to every country in the world. The aim of this project is to examine the application of multivariate quality control and quality assurance procedures to identify and monitor the key performance indicators responsible for neonatal and maternal mortality. This will provide timely out of control signals to the condition of the foetus or mother for the medical intervention to ensure safe delivery. The project will assess the performance of antenatal care system from both administration and data recording and analysis aspects. Traditionally, multivariate quality control monitoring have been applied in manufacturing areas, however, only in the recent years researches have been discussing the possibility of implementing the Statistical process control systems into medical area. Therefore the project is highly demanded and yet very new in the field of medical investigation. The finding can easily be applied into other medical area including the performance analysis of patients with Heart problems and Diabetes.

In the medical area often the wellbeing of the patients depends on several correlated key performance indicators and can only be modelled, monitored and assessed through multivariate quality control techniques including development of the appropriate multivariate quality control charts, multivariate capability index, multivariate fault diagnostics and multivariate profile monitoring charts.

References 1. Veena SR, Gale CR, Krishnaveni GV, Kehoe SH, Srinivasan K, Fall CH. Association between

maternal nutritional status in pregnancy and offspring cognitive function during childhood and adolescence: a systematic review. BMC Pregnancy and Childbirth 2016, 16(1):220.

2. A. Huang, K. Wu, W. Zhao, H. Hu, Q. Yang, and D. Chen, "Attendance at prenatal care and adverse birth outcomes in China: A follow-up study based on Maternal and Newborn's Health Monitoring System," Midwifery, vol. 57, pp. 26-31, 2018.

3. WHO U, UNFPA, World Bank Groun and the United Nations Population Division: Meauring Maternal Mortality, key facts. In: Geneva Foundation for Medical Education and Research. vol. Fact sheet No 348. Geneva 27, Switzerland: World Heath Organization (WHO); 2016: 20.

4. Chou D, Tuncalp O, Firoz T, Barreix M, Filippi V, von Dadelszen P, van den Broek N, Cecatti JG, Say L, Maternal Morbidity Working G: Constructing maternal morbidity - towards a standard tool to measure and monitor maternal health beyond mortality. BMC pregnancy and childbirth 2016, 16:45.

Contact Details: To discuss this project further please contact: 1st supervisor :Dr Mali Abdollahian ([email protected]) Supervisor – Office +61 3 99252248 2nd supervisor: Dr Yan Wang ([email protected]) Supervisor – Office +61 3 99252381





Improving the process performance through Fault diagnosis in Multivariate process monitoring and performance analysis for high

dimensional data Mathematical Sciences – City

Project Description The quality and performance of a product or service is assessed based on a large number of correlated key performance indicators KPIs (high dimensional data). Multivariate quality control charts and multivariate capability indices are capable of such monitoring. Multivariate performance analysis will play a greater role in monitoring and improving multivariate processes and services. One of the hurdles in designing optimal multivariate control charts/capability indices for large dimensional data is the enormous computing resources and time that is required by the simulation algorithm to estimate the charts parameters/ capability indices. This project aims to develop novel algorithms to improve the ability of the Multivariate charts and capability indices in assessing the performance of systems with large dimensional correlated KPIs in a computationally efficient way.

One of the main challenges in deploying multivariate control charts is to identify which KPIs are responsible for the out-of-control signals detected by the charts and what is the extent of their contribution to the signals. In assessing the capability of the processes with high dimensional data the current research is only capable of identifying whether the KPIs are falling within the specified market tolerance limits by estimating the multivariate capability indices. However, the current research is unable to carry out the task of source identification and ranking of responsible KPIs with respect to the shift in process mean, spread and yield. The project aims to develop smart diagnostic techniques based on both statistical and machine learning techniques to effectively identify the variables that are responsible for the process being unable to meet the market expectations and to classify the percentage of their contribution to the faults. The proposed techniques can be applied in monitoring and assessing the performance of industrial products, clinical processes and service industries.

References 1. Nadeera Gunaratne, Malihe Abdollahian, Shamsul Huda & John Yearwood ”Exponentially weighted

control charts to monitor multivariate process variability for high dimensions” International Journal of Production Research, 2017 , http://dx.doi.org/10.1080/00207543.2016.1278081

2. Shamsul Huda, Mali Abdollahian, Musa Mammadov, John Yearwood, Shafiq Ahmed, Ibrahim Sultan “A hybrid Wrapper-Filter approach to detect the source(s) of out-of-control signals in Multivariate Manufacturing Process” European Journal of Operational Research, 2014, http://dx.doi.org/10.1016/j.ejor.2014.02.032


Dr Mali Abdollahian Email: [email protected] Dr Vural Aksakalli Email: [email protected] Dr Yan Wang Email: [email protected]

http://dx.doi.org/10.1080/00207543.2016.1278081

http://dx.doi.org/10.1016/j.ejor.2014.02.032






Dissimilarity Vectors as a Paradigm for Biometric Template Protection Mathematical Science – City Campus

Project Description – Biometric Template Protection is a research area that has been important since the large scale advent of biometric authentication in areas as diverse as border security to health management to financial services. The use of biometric templates as tokens in an authentication scenario brings security implications if biometric template comparison is done when the templates are unencrypted. To solve this issue, several approaches to template protection have been proposed and broadly classified into non invertible transforms, salting and biometric cryptosystems[1]. However a solution that can be adopted on commercial systems has not been developed as often protected templates cause a drop in accuracy.

The dissimilarity representation is a technique to represent an object or image as a vector of dissimilarities from a chosen set of prototype objects or images, first proposed by Pekalska and Duin[2]. It has previously been used as a classification technique for fingerprint templates by Riesen and Bunke[3] and for offline signature verification by Batista et al[4] and Eskander et al[5]

The feasibility of the use of dissimilarity vectors as a non-invertible transform approach to create a template protected biometric system was initially proposed by Arakala et al[6]. This project will aim to build on the exploratory study by Arakala et al[] to develop a high accuracy and high security template protection system using the dissimilarity vector paradigm. It will require testing on several types of biometric modalities using publicly available data and rigorous security analysis to answer the following questions:

1. How does the dissimilarity vector based system compare to existing implementations of template protected

biometric systems in accuracy and security? 2. What kinds of biometric template representations can be protected using a dissimilarity vector based

system? 3. What is the tradeoff between information leakage and security in the system? 4. What are the different approaches to compromise the system and how can they be quantified? 5. Can the system be used across multiple instances and are these template instances unlinkable?

The student will need to be skilled in writing software algorithms and have excellent mathematical skills. A background in information security will be highly valued.


Science and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI 48824, USA.

2. E. Pekalska[ and R. P. W. Duin, The Dissimilarity Representation for Pattern Recognition- Foundations and Applications, ser. Series in Machine Perception and Artificial Intelligence. World Scientific, 2005.

3. K. Riesen and H. Bunke, “Approximate graph edit distance computation by means of bipartite graph matching,” Image and Vision Computing, vol. 27, no. 7, pp. 950–959, 2009.

4. L. Batista, E. Granger, and R. Sabourin, “Applying dissimilarity


Dr Arathi Arakala ([email protected]) Dr Stephen Davis ([email protected]) Prof Kathy Horadam ([email protected])




Mathematical Sciences Discipline / Statistics Research Group – City Campus

Project Description – As a renewable energy source, solar radiation is attracting the attention of researchers. Estimation of the amount of solar radiation reaching the ground in predefined time intervals is required for all kinds of planning activities around solar radiation. For example, to synchronize solar electricity generators with microgrids, we need to estimate the amount of solar radiation in very short time intervals. For a solar farm investment, it would be of interest to forecast the yearly amount of solar radiation at a certain location. Due to the cost of taking solar radiation measurements over an area of interest, use of readily available predictors like meteorological or terrestrial covariates is focused on. Although there are a lot of models that have been proposed to achieve this aim, most of the available models are developed for restricted locations over the globe and generalizability of the models is questionable.

In this project, proposed models for the estimation of solar radiation in the literature will be explored, they will be compared to each other in terms of accuracy over a real dataset, and a new statistical methodology will be focused on for the estimation of solar radiation.

A strong knowledge of R and statistical modelling are required for this research. Knowledge of machine learning techniques and their computer implementation is highly desirable.

References

1. Baser, F., Demirhan, H., 2017, A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation, Energy, 123, 229-204.

2. Demirhan, H., Atilgan Kayhan, Y., 2015, New Horizontal Global Solar Radiation Estimation Models for Turkey based on Robust Coplot Supported Genetic Programming Technique, Energy Conversion and Management, 106, 1013-1023.

3. Demirhan, H., 2014, The problem of multicollinearity in horizontal solar radiation estimation models and a new model for Turkey, Energy Conversion and Management, 84, 334-345.

4. Demirhan H Mente T Atilla M 201 Statistical comparison of global solar radiation estimation models over Turkey, Energy Conversion, and Management, 68, 141-148.

Contact Details: To discuss this project further please contact: Dr Haydar Demirhan ([email protected]) Supervisor – Office 8.9.83



Investigating the influence of arterial wall compliance on the transport

of blood-borne species Mathematical Sciences – City

Project Description Motivation

Arterial wall compliance (i.e. deformation during a cardiac cycle) is essential to the proper function of the cardiovascular system. It is recognised that the computational modelling of arterial wall compliance can introduce difficulties, as it involves a strong coupling between blood flow and the arterial wall structure (termed fluid-structure interaction). In attempt to bypass this difficulty, many studies of the cardiovascular system have often assumed arterial wall compliance to be negligible. However, whilst the influence of wall compliance on the transport of blood flow has been well studied, its influence on the transport of blood-borne species is not yet fully understood.

Scope

In this research project, the student will develop computational models of compliant and non-compliant arterial walls for a variety of arteries within the cardiovascular system. The influence of wall compliance will then be investigated on the transport of blood-borne species, such as low density lipoproteins (LDL), which are critical to the development of atherosclerosis (a precursor of leading cardiovascular diseases). It is expected that the outcomes of this study will be used to inform future studies on the permissibility of the non- compliant wall assumption.

The project is a part of the collaborative research between RMIT University and Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia. It is offered as a Masters Research Project, and has the potential to be expanded to a PhD project.

Expected outcomes

The student will be expected to produce high calibre research, which will be published in leading journals.

Keywords

Computational Fluid Dynamics (CFD), cardiovascular system, blood flow, atherosclerosis

References [1] Roustaei, M., et al. (2018), J. Biomech., 67, p114. DOI: 10.1016/j.jbiomech.2017.11.029 [2] Chung, S., et al. (2012), J. Biomech. 45, p371. DOI: 10.1016/j.jbiomech.2011.10.002 [3] Lantz, J., et al. (2011), Int. J. Appl. Mech. 3, p759. DOI: 10.1142/S1758825111001226 [4] Koshiba, N., et al. (2007), J. Biomech. Eng. 129, p374. DOI: 10.1115/1.2720914

Contact Details: To discuss this project further please contact: Dr Yan Ding (Email: [email protected]) Dr Sargon Gabriel (Email: [email protected]) Dr Yuqing Feng (Email: N/A)




Modelling the pulsatile transport of blood-borne species in the

cardiovascular system Mathematical Sciences – City


Pulsatility is essential for the transport of blood flow and blood-borne species throughout the cardiovascular system. However, when modelling blood flow throughout the cardiovascular system, the action of pulsatility introduces difficulties associated with the temporal nature of the flow. This is particularly the case for the transport of blood-borne species, such as low density lipoproteins (LDL), which are critical to the development of atherosclerosis (a precursor of leading cardiovascular diseases).

Scope

In past studies, the research group has developed a computational model for evaluating the transport of blood- borne species in pulsatile blood flow. The model was validated within a coronary artery, and was shown to be superior to approximations made with a steady flow model. In this research project, the student will use the model to study the transport of blood-borne species in arteries larger than that of a coronary, such as the carotid arteries and aorta, where the influence of blood flow pulsatility is more significant. If the current model is found to require further development, the student will be given the opportunity to make these developments.


Expected outcomes


Keywords


References [1] De Nisco, G., et al. (2018), J. Biomech. 68, p33. DOI: 10.1016/j.jbiomech.2017.12.009 [2] Li, X., et al. (2017), J. R. Soc. Interface 14, p1. DOI: 10.1098/rsif.2017.0140 [3] Liu, X., et al. (2011), J. Biomech. 44, p1123. DOI: 10.1016/j.jbiomech.2011.01.024


Dr Yan Ding (Email: [email protected]) Dr Sargon Gabriel (Email: [email protected]) Dr Yuqing Feng (Email: N/A)







Disturbances in blood flow are recognised to be influential in the genesis of cardiovascular diseases, such as atherosclerosis. At the present, there are many haemodynamic measures, such as the Oscillatory Shear Index (OSI) and Transverse Wall Shear Stress (transWSS), that can quantify various characteristics of flow disturbances. However, the haemodynamic significance of these measures is still not fully understood.

Scope

In this research project, the student will use industry-leading computational tools to study the emergence of flow disturbances in major arteries of the cardiovascular system. An assessment of existing haemodynamic measures will be made, and these will be related to the localisation of atherosclerosis. If existing haemodynamic measures are found to be lacking, the student will also be given the opportunity to develop novel haemodynamic measures, or build upon existing ones.


Expected outcomes


Keywords


References [1] Gabriel, S.A., et al. (2017), J. Theor. Biol. 430, p195. DOI: 10.1016/j.jtbi.2017.07.008 [2] Peiffer, V., et al. (2013), J. Biomech. 46, p2651. DOI: 10.1016/j.jbiomech.2013.08.003 [3] He, X., et al. (1996), J. Biomech. Eng. 118, p74. DOI: 10.1115/1.2795948

Contact Details: To discuss this project further please contact: Dr Yan Ding (Email: [email protected]) Dr Sargon Gabriel (Email: [email protected]) Dr Yuqing Feng (Email: N/A)







Atherosclerosis is a degenerative disease of the arterial wall, and is a precursor of leading cardiovascular diseases. Governing the growth of atherosclerosis, is an inflammatory process involving multiple biochemical species. The inflammatory process occurs within the arterial wall, below the endothelium, where low density lipoprotein (LDL) particles aggregate, and macrophages (type of white blood cells) are recruited to consume them. Following a cascade of processes, involving the accumulation of macrophages that have taken up oxidized LDL, a lesion forms from the net mass aggregate. This lesion can be problematic to its host artery, as it can critically impede blood flow from reaching downstream tissue, causing starvation and even death of the tissue. If this is to occur in the arteries supplying the heart, the result is called a heart attack. If this is to occur in the arteries supplying the brain, the result is called a stroke. Both these diseases represent the leading causes of death worldwide.

Scope

In this research project, the student will develop a mathematical model of the inflammatory process governing atherosclerosis. This will involve the construction of a system of differential equations that describe the spatial and temporal evolution of the biochemical species present in the inflammatory process. The student will then be given the opportunity to develop the solutions of this system of equations, and compare these to numerical solutions from equivalent computational models. It is expected that the outcomes of this study will be used to better understand the inflammatory process governing atherosclerosis.


Expected outcomes


Keywords


References 1. Thon, M.P., et al. (2018), Bull. Math. Biol. 80, p175. DOI: 10.1007/s11538-017-0367-1 2. Chalmers, A.D., et al. (2015), Math. Biol. 71, p1451. DOI: 10.1007/s00285-015-0864-5 3. Friedman, A., et al. (2015), Bull. Math. Biol. 77, p758. DOI: 10.1007/s11538-014-0010-3








Project Description Most statistical tests rest upon the normality assumption and this assumption needs to be checked for parametric statistical procedures, because their validity heavily depends on it. Normality checks on the sample are done by using visual inspection (i.e. histogram, boxplot, probability-probability plot and the quantile quantile plots) and through the normality tests. There are many normality tests proposed so far in the literature. D'Agostino and Stephens [1] provided a detailed description of these normality tests. Extensive Monte-Carlo simulation studies on the Type I error and power comparisons of these normality tests are also given in [2–9]. According to these studies, most of the normality tests do not perform well and have low powers for symmetric short-tailed and symmetric long-tailed distributions, because the mentioned tests are not able to distinguish between normal distribution and any other symmetric distributions (i.e. t -distribution, double exponential, and logistic distribution).

The goal of this project is to study on the adaptation of normality tests for the symmetrical short-tailed and symmetrical long-tailed distributions which will improve the power performance of these normality tests.

Background in computational statistics is a must. The candidate should also be familiar with programming in R, Python or similar.

References

[1] D’Agostino, R. B. and Stephens, M. A.1986. Goodness-of-fit Techniques, NewYork: Marcel Dekker. [2] Shapiro, S. S., Wilk, M. B. and Chen, H. J.1968. A comparative study of various test for normality. J. Amer. Statist. Assoc., 63(324): 1343–1372. [3] Pearson, E. S., D'Agostino, R. B. and Bowman, K. O. 1977. Test for departure from normality: Comparison of powers. Biometrika, 64(2): 231–246. [4] Wong, W. K. and Sim, C. H. 2000. Goodness-of-fit based on empirical characteristic function. J. Statist. Comput. Simul., 65: 243–269. [5] Keskin, S. 2006. Comparison of several univariate normality tests regarding Type I error rate and power of the test in simulation based on small samples. J. Appl. Sci. Res., 2(5): 296–300. [6] Oztuna, D., Elhan, A. H. and Tuccar, E.2006. Investigation of four different normality tests in terms of Type I error rate and power under different distributions. Turk. J. Med. Sci., 36(3): 171–176. [7] Farrell, P. J. and Rogers-Stewart, K. 2006. Comprehensive study of tests for normality and symmetry: Extending the Spiegelhalter test. J. Statist. Comput. Simul., 76(9): 803–816. [8] Thadewald, T. and Buning, H. 2007. Jarque–Bera test and its competitors for testing normality – a power comparison. J. Appl. Statist., 34(1): 87–105. [9] Yazici, B. and Yolacan, S. 2007. A comparison of various tests of normality. J. Statist. Comput. Simul., 77(2): 175–183.

Contact Details: To discuss this project further please contact: Dr Anil Dolgun (Email: [email protected]) Dr Alice Johnstone (Email: [email protected]) Dr Haydar Demirhan (Email: [email protected])






Project Description In biomedical and behavioural sciences, the reliability of a rating system is usually evaluated by analysing inter-rater agreement data. The inter-rater agreement coefficient (a.k.a, inter-rater reproducibility or concordance coefficient) is a statistical measure that quantifies the extent of agreement among observers. It gives a score to measure the degree of homogeneity or consensus in the ratings given by observers. There are number of statistics used to determine the inter-rater agreement and different statistics are appropriate for different types of measurements [1-2]. Among them the Kappa statistic is widely used to assess the inter-rater agreement for nominal data. However, there are two specific cases for the Kappa statistic that cause a paradox: i) low kappa values despite a high observed agreement under highly symmetrically imbalanced marginals, and (2) higher kappa values for asymmetrical imbalanced marginal distributions [3-4].

The goal of this project is to study on assessing inter-rater agreement under the two specific paradoxes previously defined. Using an appropriate arcsine transformation, the negative effect of marginal distributions can be eliminated on the Kappa statistic, hence it will make the Kappa statistic less sensitive to the imbalanced marginal distributions.

Background in computational statistics and categorical data analysis are required. The candidate should also be familiar with programming in R.

References [1] Banerjee, M., Capozzoli, M., McSweeney, L., & Sinha, D. (1999). Beyond kappa: A review of interrater agreement measures. Canadian journal of statistics, 27(1), 3-23. [2] Yilmaz, A. E., & Saracbasi, T. (2017). Assessing agreement between raters from the point of coefficients and log-linear models. Journal of Data Science, 15(1), 1-24. [3] Feinstein, A. R., & Cicchetti, D. V. (1990). High agreement but low kappa: I. The problems of two paradoxes. Journal of clinical epidemiology, 43(6), 543-549. [4] Cicchetti, D. V., & Feinstein, A. R. (1990). High agreement but low kappa: II. Resolving the paradoxes. Journal of clinical epidemiology, 43(6), 551-558.

Contact Details: To discuss this project further please contact: Dr Anil Dolgun (Email: [email protected]) Dr James Baglin (Email: [email protected]) Dr Haydar Demirhan (Email: [email protected])


School of Science


HDR Project 2018



Project Description In a two-dimensional square lattice, a self-avoiding walk (SAW) is a path beginning at the origin which does not pass through the same lattice point twice. Counting the number of SAWs, on the two-dimensional lattice, is a fundamental problem in combinatorics and statistical physics. The history of counting SAWs goes back at least six decades to a paper by Orr [3], who considered the problem of counting SAWs of lengths n=1,…,6, on a three dimensional lattice. Since then, a plethora of papers have been published on this research topic, largely concerned with either the effective algorithmic calculation of SAW’s of increasing lengths, or the determination of exact closed-form expressions for counting SAWs on a subset of the two-dimensional lattice containing the origin. Recently the problem of counting all SAWs, on the finite lattice strips of widths one and two, via an exact formula, was completely solved [1], [2].

The aim of this project is to investigate whether the arguments and techniques used to produce the closed- form expressions for the finite lattice strip of widths one and two, can be extended to produce closed-form expressions for enumerating SAWs on finite lattice strips of varying widths, greater than or equal to three. Alternatively, if such exact formulas are not possible, then the project will turn attention to the production of summation formule, for the effective computation of SAWs on these subsets of the two-dimensional square lattice.

The candidate should have a good mathematical background in the area of discrete mathematics, particularly in the area of enumerative combinatorics. Some experience in implementing recursive algorithms in any programming language, would also be an advantage.

References [1]. M. A. Nyblom, “Counting all self-avoiding walks on a finite lattice strip of width one”, J.. Alg. Num. Th. Appl, 39 (2017), 875–882. [2]. M. A. Nyblom, “Counting all self-avoiding walks on a finite lattice strip of width one and two”, (to appear) Rocky Mountain Journal of Mathematics (2018). [3]. W. J. C. Orr, “Statistical treatment of polymer solutions at finite dilution”, Trans. Faraday Soc. 43 (1947), 12-27.


Dr Michael Anthony Nyblom (Email: [email protected]) Professor Asha Rao (Email: [email protected])




Integrated model for joint species distributions

Mathematical Science– City Campus

Project Description – Species Distribution Models (SDMs) have become an increasingly important research tool in the ecological and environmental sciences. These models are used to predict maps of the spatial distribution of species presence or abundance based on the locations of individuals observed in a sample. Regression types models fitted to location-specific measurements of environmental covariates such as temperature, rainfall or elevation are the basis for making predictions of species presence or abundance at a given location. These predictions can be readily computed for different environmental scenarios by modifying the SDM’s environmental inputs in a manner that reflect hypothetical climate-change scenarios, changes in landscape characteristics or other changes through time. The predictive ability of these models gives them a wide range of conservation applications including the management of threatened species, conservation planning, as well as predicting the likely ranges of invasive species.

Often the data available for species distribution modelling is referred to as presence-only (PO) or presence- background (PB) data (Pearce and Boyce, 2006), where a set of locations where individuals of the species under investigation have been observed, but there is usually no information on absences. It is commonly recognized that the presence-only data can only be used to estimate the distribution of species sighting, where the sightings distribution is typically biased toward regions more frequented by whoever is collecting the data l (Aarts et al., 2012; Warton and Shepherd, 2010; Renner and Warton, 2013; Fithian and Hastie, 2013; Wang and Stone, 2018). Presence-absence and other data sets collected via systematic surveys do not typically suffer from such bias. Unfortunately, the presence-a

References 1. G. Aarts, J. Fieberg, and J. Matthiopoulos (2012). Comparative interpretation of count, presence–absence

and point methods for species distribution models. Methods in Ecology and Evolution 3, 177–187. 2 J. Elith, S.J. Phillips, T. Hastie, M. Dud´ık, Y.E. Chee, and C.J. Yates (2011). A statistical explanation of

maxent for ecologists. Diversity and Distributions 17, 43-57. 3. William Fithian and Trevor Hastie (2013). Finite-sample equivalence in statistical models for presence-only

data. The Annals of Applied Statistics 7(4), 1917-1939. 4. Jennie L Pearce and Mark S Boyce (2006). Modelling distribution and abundance with presence-only data.

Journal of Applied Ecology, 43(3), 405–412. 5. Ian W Renner and David I Warton (2013). Equivalence of maxent and poisson point process models for

species distribution modeling in ecology. Biometrics 69, 274-281. 6. Ian W Renner, Adrian Baddeley, Jane Elith, William Fithian, Trevor Hastie, Steven Phillips, Gordana

Popovic, and David I Warton (2014). Point process models for presence-only analysis — a review. Methods in Ecology and Evolution 6, 366-379.


Dr Yan Wang ([email protected]) Prof Lewi Stone ([email protected]) Dr Ascelin Gordon ([email protected])



Mining tropical cyclones using Market Basket Analysis Techniques


Project Description – Cyclone modelling and prediction are key elements of an early warning system to assist society in adaptation to and mitigation of risks caused by land-falling cyclones. In this project, we propose to apply the association rule mining to model the tropical cyclones. Informative association rule mining is fundamental for knowledge discovery from transaction data, for which brute-force search algorithms, e.g., the well-known Apriori algorithm, were developed. However, operating these algorithms becomes computationally intractable in searching the large rule space such as that of the 4-dimensional tropical cyclone data. The stochastic search algorithm will be developed in the project to tackle this challenge by using the idea of annealing Gibbs sampling. Large rule space of exponential order can still be randomly searched by this algorithm to generate an ergodic Markov chain of viable length. The ergodic chain contains the most informative rules with probability 1, creating a much reduced rule space for subsequent mining without information loss. By using the informative association rule, we aim to identify the key meteorological and environmental features that influence the near-surface winds, and develop an innovative statistical approach for cyclone seasonal prediction. The project aims to provide ground-breaking statistical mining methodology for research on tropical cyclone climatology in the Southern Hemisphere. This project is a collaborative research in statistical climatology with researchers from Associate Professor Guoqi Qian from the University of Melbourne. The World Meteorological Organization (WMO) version of the IBTrACS global TC database is used for our study, which consists of observations from WMO-designated forecast centers only. This global TC best track database is endorsed by the WMO Tropical Cyclone Program. The database is used to obtain the time and position of TC genesis for each TC observed on a global domain.

References 1. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large

databases. ACM SIGMOD Rec 22(2):207–216. 2. Hahsler M, Grün B, Hornik K (2005) arules – A computational environment for mining association rules

and frequent item sets. J Stat Softw 14(15):1–25. 3. Bayardo RJ, Agrawal R (1999) Mining the most interesting rules. 5th ACM SIGKDD International

Conference on Knowledge Discovery and Data Mining (ACM, New York), pp 145–154. 4. Qian G, Field C (2002) Using MCMC for logistic regression model selection involving large number of

candidate models. Monte Carlo and Quasi-Monte Carlo Methods 2000, eds Fang K-T, et al. (Springer, Berlin), pp 460–474.

5. Qian G, Rao C. R., Sun, X. Y. and Wu, Y. H. (2016). Boosting association rule mining in large datasets via Gibbs sampling. PNAS, 4958-4963.

6. Amatya A, Demirtas H (2015) MultiOrd: An R package for generating correlated ordinal data. Commun Stat Simul Comput 44(7):1683–1691.

7. Chaganty NR, Joe H (2006) Range of correlation matrices for dependent Bernoulli random variables. Biomet


Dr Yan Wang ([email protected]) Ass Pro Guoqi Qian ([email protected]) Dr Mali Abdollahian ([email protected])




Contents

Click on the project links for more information

Applied Chemistry and Environmental Science

Professor Rachel A. Caruso

Hybrid photocatalytic nanomaterials for water purification Professor Adam Lee


Professor Karen Wilson


Dr Ravichandar Babarao


Dr Jampaiah Deshetti Cost-effective porous materials for controlling engine emissions

Applied Physics

Professor Gary Bryant

Mimicking atomic potentials with colloids: charged particles with a depletion interaction Rapid measurement of the effects of antimicrobial drug candidates on bacterial motility Effects of vitrification (glass formation) on biological membranes

Dr Andrew Martin



Dr Qiming Zhang


Biosciences and Food Technology

Professor Dayanthi Nugegoda

Effects of pollutants on health of seabirds Professor Ian Macreadie

Overcoming reduced proteostatis in ageing with a systems biology approach

Production of worm proteins for testing as novel immunosuppressants

Professor Peter Smooker

Identifying colonisation factors in Campylobacter jejuni Detection and Identification Procedures for Foodborne Pathogens

Professor Ewan Blanch


Professor Harshan Gill


Dr Samantha Grover Indonesian Peatland Pestoration

Dr Kylie Boyce Defining Genetic Causes of Antifungal Drug Resistance

Dr Ravi Shukla


Computer Science

Professor Mark Sanderson



Professor Zahir Tari

Effective Countering cyber threats over Smart-Grid Infrastructures Low-Latency High-Throughput Computational Models for Heavily Data-Driven Applications in Hybrid Data Centres


Associate Professor Tao Gu


Human Activity Sensing with Wireless Signals

Associate Professor Sebastian Sardina



Dr Zhifeng Bao


Dr Jeffrey Chan




Dr Shane Culpepper


Associate Professor Donald Wlodkowic



PhysiOmics-on-a-chip: integrated fluidic and imaging systems for high-throughout phenotypic screening in drug discovery and predictive toxicology


Mathematical Sciences

Professor Andrew Eberhard



Professor John Hearne



Associate Professor Melih Ozlen



Associate Professor Marc Demange

Frictional Pebble Games in Graphs and Applications


Associate Professor John J Shepherd

Modelling of anaerobic digester reactor incorporating delays

Dr Mali Abdollahian


Improving the process performance through Fault diagnosis in Multivariate process monitoring and performance analysis for high dimensional data

Professor Asha Rao


Dr Caspar Ryan Reducing Energy Consumption in Computational and Cyber Physical Systems

Dr Flora Salim

Scalable and Transferrable Occupant Behaviour Learning with Multi-Sensor Data from Multi-region

Living Labs

Dr. Xiangmin Zhou Abnormal Event Detection in Big Video Data


Dr Yongli Ren


Dr Haydar Demirhan


Dr Yan Ding





Dr Anil Dolgun



Dr Michael Anthony Nyblom


Dr Arathi Arakala Dissimilarity Vectors as a Paradigm for Biometric Template Protection

Dr Yan Wang

Mining tropical cyclones using Market Basket Analysis TechniquesIntegrated model for joint species distributions




Applied Chemistry and Environmental Science – City Campus

Project Description – Photocatalysis makes use of a photoactive material that absorbs light to produce an electron-hole pair and hence can initiate oxidative and reductive reactions with species adsorbed on the photocatalyst surface. This can lead to the breakdown of pollutants in aqueous or gas phases, the generation of fuel (through hydrogen generation from splitting water or the production of hydrocarbons through the reduction of CO2), or antimicrobial reactions killing viruses, bacteria and fungi. In this project, porous, hybrid photocatalytic nanomaterials will be synthesized to maximize the interfacial interaction of the two materials and hence the benefits of the heterojunction. The materials will be characterized to understand the composition and structural effects on the properties of the materials and then assessed for their photocatalytic performance by the breakdown of pollutants in water.

This project will contribute to an Australian Research Council Discovery Project focused on the development of hybrid photocatalysts for water purification and the application of high-throughput approaches to speed up the determination of the composition/structure-property-performance relationships. The candidate will be working with a team of academics from the School of Science and the School of Engineering, as well as collaborators at CSIRO and in Japan. In addition to the wet chemical synthesis used to prepare the materials, the candidate will be trained to make use of a number of characterisation methods including both scanning and transmission electron microscopy, powder X-ray diffraction, gas sorption and spectroscopy techniques. High-throughput approaches to characterisation of the materials and testing the photocatalytic properties will be developed and applied.

It is expected that the successful candidate will have majored in one of the following disciplines: chemistry, chemical engineering, materials science or materials engineering.

Contact Details: To discuss this project further please contact: Prof. Rachel A. Caruso ([email protected]) Office 03.03.13





Project Description – Cascade reactions are sequential chemical transformations in which the starting substrate undergoes a reaction whose product becomes the substrate for the next step, and so on, until a stable product is reached. Cascades offer great advantages in respect of process efficiency and waste minimisation. In addition to their intrinsic elegance, cascade reactions thus represent a key green chemical technology by which to reduce the number of manipulations required for the manufacture of pharmaceuticals and fine chemicals.[1] However, catalytic cascades, in which the product of a reaction catalysed by species A undergoes a subsequent, distinct transformation catalysed by a second species B, are hindered by undesired interactions between the initial substrate and the second active species, or indeed between the two catalytic species (e.g. an acid and base).

We recently discovered a revolutionary yet versatile solution to the above problems that enables control over the position of chemically and/or physically different components within three-dimensional architectures.[2] This solution, by which chemically distinct functionalities can be introduced in a spatially orthogonal (mutually exclusive) fashion, offers unparalleled control over sequences of chemical reactions in porous solid catalysts (Fig. 1).

Fig. 1. (left) Spatially orthogonal chemically functionalised Pd macroporous-Pd mesoporous SBA-15 (HAADF-STEM image inset), and (right) catalytic advantage for the aerobic selective oxidation of cinnamyl alcohol.

This PhD project will focus on the creation of new hierarchically ordered, porous architectures [3] from diverse and commercially significant metal oxides for shape/size-selective catalytic cascades including: oxidative amination of allylic alcohols to unsaturated amides; and the selective hydrogenation of furfural, both industrially important reactions for the production of sustainable fine chemicals, agrochemicals and pharmaceutical intermediates from biomass feedstocks. The student will learn a range of synthesis and characterization skills, including electron microscopy, XPS, XRD, DRIFTS, and N2 porosimetry, and have the opportunity to conduct cross-disciplinary research at international synchrotron light sources and during visits to collaborators at the University of Cambridge and Manchester University.

References [1] M.J. Climent, A. Corma, S. Iborra, M.J. Sabater, ACS Catal. 2014, 4, 870. (Review)[2] C.M.A. Parlett, M.A. Isaacs, S.K. Beaumont, L.M. Bingham, N.S. Hondow, K. Wilson, A.F. Lee - Nature

Mater. 2016, 15, 178.[3] C.M.A. Parlett, K. Wilson, A.F. Lee - Chem. Soc. Rev. 2013,42, 3876.




Scheme 1: Tandem process for the production of 2,4 dihydroxybutyric acid from glucose.



Project Description – The quest for sustainable resources to meet the demands of a rapidly rising global population while mitigating the risks of rising CO2 emissions and associated climate change, represents a grand challenge for humanity. Biomass offers the most readily implemented and low cost solution for sustainable transportation fuels, and the only non-petroleum route to organic molecules for the manufacture of bulk, fine and speciality chemicals and polymers. [1] Bio-derived chemicals can be employed as ‘drop-in’ replacements of existing fossil derived chemicals or as new products that may offer enhanced properties for use in high performance materials polymers and coatings. Tandem synthetic pathways, inspired by natural metabolic pathways of organisms can allow multi-step conversions to take place in one reaction vessel, and are viewed as a potentially step-changing approach for the selective production of new platform chemicals from biomass. Multi-enzyme ‘one-pot’ bio-catalytic processes are attracting growing interest for their superior process efficiency over whole cell fermentation processes which suffer from product inhibition and/or cell death at substrate concentrations required to make processes industrially viable. [2] Recently a 5 enzyme cascade has been proposed as a potential

route to produce malic acid (a valuable succinic acid derivative) from glucose in a “one-pot” route outlined in Scheme 1. Malic acid is used widely used in the food industry, but its potential as a versatile platform chemical has been largely overlooked. [3] The potential of a malic acid platform could be unlocked via the development of tailored heterogeneous catalysts for its transformation, which requires advances in materials chemistry, nanoscience, surface chemistry and process engineering. The production of 2,4 dihydroxybutyric acid (2,4 DHBA) from malic acid is one such example, which is proposed as an advanced additive for curing coatings that offers superior hydrogen bonding characteristics over currently utilised petroleum derived additives.

This PhD project will focus on the development of a heterogeneous catalyst for the selective hydrogenation of malic acid, derived from an enzymatic cascade, to 2,4 dihydroxybutyric acid (and its associated lactone). Control over selectivity of the hydrogenation process will require careful tuning of the metal nanoparticle size, shape and composition and morphology of the support. Pore dimensions of the nanoporous support will also be investigated to exploit confinement effects [4] that will direct the orientation (and thus malic acid coordination), thereby avoiding undesirable side reactions. By tuning the support surface polarity and ensuring selective in-pore deposition of nanoparticles we anticipate fine control over the binding of malic acid and thus chemo-selective hydrogenation.

References 1. Wilson, K. & Lee, A.F. Phil. Trans. R. Soc. A, 374, 20150081, (2016).2. Filice, M. & Palomo, J.M. ACS Catal. 4, 1588−1598 (2014).3. Kumar, R.V., Pakshirajan, K. & Pugazhenthi, G., Platform Chemical Biorefinery, Ch 9, 159–179, (2016).4. Durndell, L.J., Wilson, K. & Lee, A.F., RSC Adv., 5, 80022-80026, (2015).





Applied Chemistry and Applied Biology & Biotechnology –City/Bundoora Campuses

Project Description – Emergence of multi drug resistant (MDR) bacteria is an enormous and growing threat facing the world. Particularly vulnerable are immunocompromised patients exposed to opportunistic bacteria such as Pseudomonas aeruginosa and Burkholderia cenocepacia. New approaches to combat this global problem involve exploitation of host-pathogen interaction processes not targeted by current drugs. Carbohydrate-recognizing proteins (lectins) of many pathogens, including P. aeruginosa and B. cenocepacia spp., use host cell surface carbohydrates (glycans) for adhesion. Thus, lectins represent novel targets for interference in the infection process. Our hypothesis is that the consideration of an ensemble of multiple binding modes will reveal detailed structural mechanisms of lectin-carbohydrate recognition not achievable by other methods. The main goal of this project is to delineate the structural requirements of carbohydrate recognition by lectins from opportunistic bacteria: PA-IL and PA-IIL (P. aeruginosa), BC2L-C (B. cenocepacia), and BambL (Burkholderia ambifaria). Translation of the fundamental information on dynamic carbohydrate recognition will be in the development of a carbohydrate-centric virtual screening (VS) approach that will be applied to identify small molecules with anti-adhesive properties. The specific aims of this project are to: (1) examine conformational properties of free and bound carbohydrate ligands using advanced spectroscopic techniques, to determine the basis for recognition by lectins, and (2)use this information to develop a carbohydrate-centric VS approach to screen large available chemical libraries to identify novel

References 1. Dingjan T, Spendlove I, Durrant LG, Scott AM, Yuriev E, Ramsland PA (2015): Structural biology of

antibody recognition of carbohydrate epitopes and potential uses for targeted cancer immunotherapies.Mol. Immunol. 67, 75.

2, Mutter ST, Zielinski F, Cheeseman JR, Johannessen C, Popelier PLA, Blanch EW (2015): Conformational dynamics of carbohydrates: Raman optical activity of D-glucuronic acid and N-acetyl-D-glucosamine using a combined molecular dynamics and quantum chemical approach. Physical Chemistry Chemical Physics 17, 6016.

3. Soliman C, Walduck AK, Yuriev E, Richards JS, Cywes-Bentley C, Pier GB, Ramsland PA. (2018)Structural basis for antibody targeting of the broadly expressed microbial polysaccharide poly-N-acetylglucosamine. J. Biol. Chem. in press. DOI:10.1074/jbc.RA117.001170.


Prof Ewan Blanch ([email protected]) Associate Professor Paul Ramsland ([email protected])






Applied Chemistry and Environmental Science / Applied Porous Materials Team (CSIRO)

Project Description – An exciting opportunity is available for an outstanding PhD candidate to work on an ARC funded project on design of hybrid porous materials for capture and release of oxygen. The past decade has seen the emergence of metal organic frameworks (MOFs) or porous coordination polymers (PCPs) as potential candidate materials across a plethora of applications [1, 2]. MOFs are crystalline materials built from metal ions or clusters bridged by organic linkers to form one-, two- or three-dimensional structures. MOFs are porous materials that hold the world record for specific surface area and storage of gases. MOF research has become one of the fastest growing fields in materials science. More than 20,000 different MOFs have been reported and studied within the past decade and nearly 130,000 hypothetical MOFs have been designed based on different metal clusters and ligands [3]. Recent evidence has indicated that the MOF can be tuned to capture oxygen over nitrogen. However, significant new chemistry is needed to develop new MOF materials with enhanced performance for selectively capturing oxygen over nitrogen in designing portable oxygen concentrators [4]. Understanding these issues is critical for MOF manufacturing, processing and performance. This project will focus on fundamental understanding of what makes MOFs stable in realistic conditions using state of the art computational techniques [5] and to exploit this new understanding to guide development of robust porous materials for capturing oxygen. We seek chemistry/physics/engineering graduates who have some background in any of the following: atomistic modelling, in silico design of materials, programming, high-throughput screening, Monte Carlo methods and first principles calculations. The project involves close collaboration with experimentalist in the field of nanomaterials and device fabrication. Specifically, the project involves: (i) Develop in silico screening tool for pre-screening thousands of materials for capturing oxygen. (ii) Develop new descriptors based on the structure–property relationship to identify materials that merit experimental characterisation. (iii) Use a combinatorial approach integrating modelling and chemical experiments to speed up the design cycle from laboratory testing to industrial use. (iv) Develop understanding of the stability of MOF materials in realistic conditions to find the best, robust materials for large-scale synthesis.

References [1] J. Jiang et al. Chemical Society Reviews 40 (2011) 3599.[2] M. Rubio-Martinez et al. Chemical Society Reviews 46 (2017) 3453.[3] D. J. Xiao et al. J. Am. Chem. Soc. 138 (2016) 7161.[4] A. J. Howarth et al. Nature Reviews, Materials 1 (2016).[5] J. D. Evans et al. Chemistry of Materials 29 (2017) 199.


Dr Ravichandar Babarao ([email protected]) – Office 3.2.03 A/Prof Matthew Hill ([email protected]) – New Horizon Building, Clayton

DR229 – PhD (Applied Chemistry)

MR229 – Master of Science (Applied Chemistry)


Indonesian Peatland Pestoration

Applied Chemistry – City Campus


Smoke haze has become a major issue in South East Asia in recent decades, negatively impacting on public

health and the economy of several countries in the region. The majority of smoke haze is derived from

intentional and accidental anthropogenic burning of forest and peatlands. To address the smoke haze crisis, the

Government of Indonesia created the Badan Restorasi Gambut (BRG, or Peatland Restoration Agency) in

2016 with the mandate to restore 2.4 M Ha of peat by 2020. Peatland restoration also reduces greenhouse gas

emissions, as drained tropical peatland is a globally significant source of CO2 through oxidation processes

and burning.

Peatland restoration, which involves rewetting, revegetation and revitalisation of livelihoods1, has not been

attempted in tropical regions on a large scale. This project aims to support the biophysical transition that will

need to occur for effective and equitable restoration of this peatland, and ensure the continued wellbeing of

the communities that are dependent on it.

References

1. Anil, J, Nandakumar, K & Nagar, A 2007, ‘Biometric Template Security’, Department of Computer

Science and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI 48824,

USA.

2. E. Pekalska[ and R. P. W. Duin, The Dissimilarity Representation for Pattern Recognition- Foundations

and Applications, ser. Series in Machine Perception and Artificial Intelligence. World Scientific, 2005.

3. K. Riesen and H. Bunke, “Approximate graph edit distance computation by means of bipartite graph

matching,” Image and Vision Computing, vol. 27, no. 7, pp. 950–959, 2009..

Contact Details:

To discuss this project further please contact:

Dr Samantha Grover ([email protected])




Cost-effective porous materials for controlling engine emissions

Applied Chemistry and Environmental Science – City Project Description

Advanced combustion engines are being developed to meet higher standards of fuel efficiency and lower greenhouse gas emissions. However, the commercial potential is depending on meeting emission standards for the control of serious pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons, and particulate matter. The pollutants are serious to the atmosphere, and most concern is due to their negative impact on cardiovascular health [1]. With increasing demands from the environmental organizations, the control of these pollutants is the urgent task today. Several exhaust gas after treatment technologies were employed to control the emissions using filters in automobile engines [2, 3]. Three way catalysis (TWC) namely, catalytic CO oxidation, NOx reduction, and hydrocarbons oxidation has been extensively used to control pollutants during combustion. Various catalysts such as precious metals, alkali metal oxides, spinel oxides, and transition metal oxides have been intensively investigated for TWC application. However, these oxides are relatively unstable, require high temperatures (>350 °C), and possess ill-defined particle structure. Therefore, the lower exhaust temperatures of advanced engines require catalysts that are active at <150 °C to meet future emission regulations. A porous network of nanomaterial is an interesting phenomenon in gas adsorption and catalytic oxidation reactions as it provides a facile pathway for the reactants to be actively involved in the oxidation reaction. Surface area is also a crucial issue, and it needs to be considered when metal oxides used in catalysis. In this project, the PhD student will work on designing nanostructured porous metal oxides by wet chemistry approaches such as co-precipitation, hydrothermal, sol-gel, and solvothermal synthetic methods. The various other parameters related to the synthetic schemes would also be taken into account e.g. factors determining nanoparticle monodispersity, size tunability, yield, effect of solvent, pH, temperature etc. Structural, surface, redox properties of these materials will then be recorded and the effect of changing of different parameters will be evaluated. A characterization scheme using the state of the art techniques, namely, XRD, N2 BET Surface area, SEM, TEM, HR-TEM, TPR, TPD, FT-IR, RAMAN and XPS will be employed to fully characterize the structural, surface, and redox properties of the as-prepared catalysts. Using TGA instrument and GC analysis, the student will study the catalytic activities of porous materials. References

1. S. Chen, L. Luo, Z. Jiang, W. Huang, , ACS Catalysis, 5 (2015) 1653-1662

2. D. Jampaiah, V.K. Velisoju, P. Venkataswamy, V.E. Coyle, A. Nafady, B.M. Reddy, S.K. Bhargava, ACS Applied Materials & Interfaces, 9 (2017) 32652-32666

3. James E. Parks Science 327 (5973), 1584-1585.

Contact Details:


Dr Jampaiah Deshetti (Email: [email protected]) Professor Suresh Bhargava (Email: [email protected]) Dr Yan Wang (Email: [email protected])

DR230 – PhD (Applied Physics)

MR230 – Master of Science (Applied Physics)


Mimicking atomic potentials with colloids: charged particles with a

depletion interaction


PhD or Masters Project Project Description –

Colloidal suspensions provide model for understanding the fundamentals of crystallization and glass

formation in atomic systems. As the inter-particle distance is ~ 100s of nm, light scattering can be used to

study such systems. As the motions are Brownian rather than ballistic, kinetics and dynamics can be studied in

real time [1, 2].

To date, there have been extensive studies of 3 model systems: repulsive hard spheres [3]; repulsive charged

particles [4]; and repulsive hard spheres with a depletion attraction [5]. However, to date there have been no

studies of systems with a short range attraction combined with a long range repulsion – ie something that

mimics real atomic systems, such as the Lenard-Jones potential. This project aims to develop such a model

system using PMMA-based nanoparticles suspended in water or water/DMSO mixtures. The project will have

two aspects:

1) Masters project: preparing and characterizing a novel sterically stabilized charged PMMA nanoparticle

in the size range 100-150 nm, and developing a suitable polymer system to provide the depletion effect,

then characterizing the phase behaviour of the system. This will be done using light and X-ray

scattering. Would result in 2 high quality publications.

2) PhD project: as above, plus a detailed study pf particle dynamics using light scattering, and detailed

structural characterisation of the crystalline phases using Small Angle X-ray and Neutron Scattering.

This would result in an additional 2-3 high quality publications.

The primary techniques include Dynamic and Static Light Scattering and Differential Dynamic Light

Scattering. For the PhD project, Small angle X-ray and Neutron scattering (SAXS & SANS) will be

conducted at the Australian Synchrotron and at Lucas Heights in Sydney).

References

1. Schope, H.J., G. Bryant, and W. van Megen, Two-step crystallization kinetics in colloidal hard-sphere

systems. PHYSICAL REVIEW LETTERS, 2006. 96: p. 175701.

2. Martinez, V.A., G. Bryant, and W. van Megen, Slow dynamics and aging of a colloidal hard sphere

glass. PHYSICAL REVIEW LETTERS, 2008. 101(13): p. 135702.

3. van Megen, W., V.A. Martinez, and G. Bryant, Scaling of the Space-Time Correlation Function of

Particle Currents in a Suspension of Hard-Sphere-Like Particles: Exposing When the Motion of

Particles is Brownian. PHYSICAL REVIEW LETTERS, 2009. 103(25): p. 258302.

4. Palberg, T., Crystallization Kinetics of Repulsive Colloidal Spheres. Journal of Physics: Condensed

Matter, 1999. 11(28): p. R323-R360.

5. Pham, K.N., et al., Glasses in hard spheres with short-range attraction. Physical Review E, 2004. 69(1).

Contact Details:


Prof Gary Bryant ([email protected]) Supervisor – Office 14.7.04A

Bill van Megen ([email protected]) Supervisor – Office 14.7.04A




Rapid measurement of the effects of antimicrobial drug candidates on

bacterial motility



Antimicrobial drug resistance is a global health emergency and there is an urgent need for new clinical

antimicrobial agents that work in different ways to current drugs, and are therefore more likely to be effective

(1). One mechanism of particular interest is motility, the ability of bacteria to explore their environment and

spread, as this is how bacteria spread and grow. Despite their enormous potential, antimicrobial drugs that

prevent motility have received little attention because of the limitations of traditional techniques to measure

bacterial movement (2)). These measurements are critical for assessing the effectiveness of potential drugs.

This project we will apply the new technique of Differential Dynamic Microscopy (DDM) to assess the

effectiveness of potential candidate antimicrobial molecules, and understand how such molecules affect

motility. DDM (3) was first used to study bacterial motility by the Soft Matter group at Edinburgh University,

in collaboration with CI Bryant at RMIT (4). Since then, the Edinburgh group have proved the utility of the

technique using E. Coli (5), the bacteria responsible for maladies such as gastroenteritis, meningitis in

newborns, pneumonia and urinary tract infections. Bryant’s group at RMIT has recently applied the technique

to understand the role of motility in the effects of carbohydrate-based surfactants on bacterial growth, in

collaboration with PIs Bansal and Wilkinson (6).

This project will extend this work to look at motility in a range of organisms, and the effects of both existing

and novel antibiotics on bacterial motility.

The project could be at either Masters of PhD level, with the Masters level project focussing on one bacterial

species.

References

1. Carlet J, Collignon P, Goldmann D, Goossens H, Gyssens IC, Harbarth S, et al. Society's failure to

protect a precious resource: antibiotics. The Lancet.378(9788):369-71.

2. Berg HC. The rotary motor of bacterial flagella. Annual Review of Biochemistry. 2003;72:19-54.

3. Cerbino R, Trappe V. Differential dynamic microscopy: Probing wave vector dependent dynamics with

a microscope. Physical Review Letters. 2008;100(18).

4. Wilson L, Martinez V, Schwarz-Linek J, Tailleur J, Bryant G, Pusey P, et al. Differential Dynamic

Microscopy of Bacterial Motility. Physical Review Letters. 2011;106(1).

5. Schwarz-Linek J, Arlt J, Jepson A, Dawson A, Vissers T, Miroli D, et al. Escherichia coli as a model

active colloid: A practical introduction. Colloids and surfaces B, Biointerfaces. 2016;137:2-16.

6. Hu, Y, Zou, W, Julita, V, Ramanathan, R, Tabor, RF, Nixon-Luke, R, Bryant, G, Bansal, V and

Wilkinson, BL. Photomodulation of bacterial growth and biofilm formation using carbohydrate-based

surfactants. Chemical Science. 2016;7:6579 (Cover Image).

Contact Details:






Effects of vitrification (glass formation) on biological membranes



In nature, many species of plants and animals have evolved to avoid membrane damage during freezing and

dehydration by vitrifying – ie forming a glass from the concentrated sugar suspensions1. Early studies of the

effects of sugars concentrated on the effects of non-vitrified glasses, though there were attempts to understand

and model the effects of vitrified glasses2. These studies examined the effects membrane transition

temperatures, but not on the membrane structure, due to limitations in the available technology. Over the past

few years we have pioneered the study of the effects of various molecules on membrane structure using

advanced scattering techniques3-5.

While initial studies of the effects of glasses on membranes In this project we will study thIn this project we

will apply these advanced techniques to the study of the effects of glass formation on membrane structure, as

well as studying the effects of ice crystallization.

The primary lab based techniques to be used include Small angle X-ray scattering (SAXS) and Differential

Scanning Calorimetry (DSC). We will also investigate the properties of the sugar glasses themselves using

Powder X-ray Diffraction, DSC and Dynamic Mechanical Analysis (DMA). In addition, we will conduct

synchrotron SAXS studies (at the Australian Synchrotron) and neutron scattering studies (at Lucas Heights in

Sydney and at overseas facilities), using molecular deuteration to fine tune the location of molecules within

the membrane-glass system. As well as studying traditional glass formers, we will study the effects of novel

crystallization inhibitors and vitrification agents.

The project could be at either Masters of PhD level, with the Masters level project focussed on lab based

measurements.

References

1. Koster, K. L.; Anderson, M., Interactions between vitrified sugars and lipid - mechanism by which the

fluit-to-gel phase-transition temperature is lowered. Plant Physiology 1995, 108 (2), 110-110.

2. Koster, K. L.; Lei, Y. P.; Anderson, M.; Martin, S.; Bryant, G., Effects of vitrified and nonvitrified

sugars on phosphatidylcholine fluid-to-gel phase transitions. Biophysical Journal 2000, 78 (4), 1932-

1946.

3. Garvey, C. J.; Lenne, T.; Koster, K. L.; Kent, B.; Bryant, G., Phospholipid Membrane Protection by

Sugar Molecules during Dehydration-Insights into Molecular Mechanisms Using Scattering

Techniques. International Journal of Molecular Sciences 2013, 14 (4), 8148-8163.

4. Kent, B.; Hunt, T.; Darwish, T. A.; Hauss, T.; Garvey, C. J.; Bryant, G., Localization of trehalose in

partially hydrated DOPC bilayers: insights into cryoprotective mechanisms. Journal of the Royal

Society Interface 2014, 11 (95).

5. Kent, B.; Hauss, T.; Deme, B.; Cristiglio, V.; Darwish, T.; Hunt, T.; Bryant, G.; Garvey, C. J., Direct

Comparison of Disaccharide Interaction with Lipid Membranes at Reduced Hydrations. Langmuir

2015, 31 (33), 9134-9141

Contact Details:







Project Description – The atomic structure of an amorphous material, like a glass, is a seemingly random atomic network. However, these materials are only truly random on length scales larger than a few nanometres. The local environment around any given atom can be highly ordered by bonding. It is the local structure that ultimately determines the properties of amorphous materials. However, disordered structures are also extremely difficult to measure because the long-range randomness limits electron or x-ray characterization methods to 1D1. As a consequence, there is much we do not know about the structural origins of amorphous material properties. For example, the search for a microscopic explanation of the glass transition, whereby a supercooled liquid suddenly becomes solid without crystallizing, remains one of the biggest outstanding challenges in condensed matter physics2.

This Project is based on a new theory3 to characterise the local structure of amorphous materials in 3D with electron scattering. The new method exploits the statistics of large datasets collected by scanning a focused electron probe over the material. The goal of this Project is to develop the new 3D technique experimentally and to make some of the first demonstrations with amorphous solids using the high-resolution electron microscopes at the RMIT Microscopy and Microanalysis Facility.

The Project involves data analysis, electron microscopy and numerical simulation. It would suit a candidate who is interested in learning both simulation and experimental methods.

References 1. Eliot, S. R. Physics of Amorphous Materials. (Longman, 1983).2. Binder, K. et al. Glassy Materials and Disordered Solids. (World Scientific Publishing, 2011).3. Martin, A. V. Orientational order of liquids and glasses via fluctuation diffraction. IUCrJ 4, 24, (2017)






Project Description – Disordered matter is ubiquitous in the world we live in, from the glass in our windows to the polymers in our plastics to living biological systems. However, disordered matter is as mysterious as it is common. For example, we lack a microscopic explanation of the glass transition, whereby a supercooled liquid suddenly becomes solid without crystallizing1. More generally, it is extremely difficult to predict the properties of disordered materials because we do not know their atomic structures accurately. The problem is that disordered materials scatter x-rays and electrons randomly, obscuring local order and producing a 1D characterisation only (atomic pair distance statistics)2.

This Project is based on a new theory3 that enables 3D statistics of disordered nanostructure to be measured. The goal of this Project is to develop x-ray scattering techniques for 3D nanostructure characterization for use at the Australian Synchrotron. In a collaboration with chemistry, biology and the Australian Synchrotron, the candidate will work on applications of the new technique to soft matter systems, such as the lipidic cubic phase which is highly topical for membrane protein crystallization and drug delivery.

There is also scope within the Project to develop new x-ray techniques for x-ray free-electron lasers. These remarkable new international facilities are a billion times brighter than synchrotron sources and produce femtosecond pulses enabling snapshot measurements of dynamic samples like liquids. There are exciting possibilities for using the new 3D methods to study supercooling and phase transitions.

The Project involves data analysis, x-ray experimentation and numerical simulation. It would suit a candidate who is interested in learning both simulation and experimental methods.

References 1. Binder, K. et al. Glassy Materials and Disordered Solids. (World Scientific Publishing, 2011).2. Eliot, S. R. Physics of Amorphous Materials. (Longman, 1983).3. Martin, A. V. Orientational order of liquids and glasses via fluctuation diffraction. IUCrJ 4, 24, (2017)





Laboratory of Artificial-Intelligence Nanophotonics

Project Description – Deep learning without supervision has transformed the field of artificial intelligence, but the limitations of conventional computer hardware are hindering progress [1]. Nanophotonic chips could be the answer by carrying out deep learning at the speed of light. Although photonic chips have long been used to speed data transfer, non-volatile memories is required to integrate the nanophotonic chips to achieve deep learning without supervision. This project will involve the fabrication of photonic chip with long lifetime memory for on-chip optical deep learning. This is enabled by the direct laser writing technique with nanometer resolution in three dimensions that developed by our group [2].

This project will focus on the direct writing of three dimensional photonic chips with structure inspired by neural networks. The memory at nanoscale will be induced by the femtosecond laser interaction with nanomaterials [3] such as graphene and nanodiamonds in the as formed photonic chips. This is based on our recent development of optical memories enabled by direct laser writing [3], [4]. Optical memory at nanoscale will be induced by laser at deterministic and on-demand in the photonic chips. Characterization of the memory after laser fabrication will be carried out from the optical and optoelectronic point of view.

Training on the use of femtosecond laser and imaging systems will be required via attending the Laser Safety Course and by the Supervisors.

References [1]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. [2]. Gan, Z., Cao, Y., Evans, R. A., & Gu, M. (2013). Three-dimensional deep sub-diffraction optical beam lithography with 9 nm feature size. Nature communications, 4, 2061. [3]. Gu, M., Zhang, Q., & Lamon, S. (2016). Nanomaterials for optical data storage. Nature Reviews Materials, 1, 16070. [4]. Gu, M., Li, X., & Cao, Y. (2014). Optical storage arrays: a perspective for future big data storage. Light: Science and Applications, 3(5), e177.

Contact Details: To discuss this project further please contact: Professor Min Gu ([email protected]) Senior Supervisor – Office Building 91 Dr Qiming Zhang ([email protected]) Dr Ben Cumming([email protected])

Associate Supervisor – Office Builiding 14 Associate Supervisor – Office Builiding 14

DR232 – PhD (Food Science) MR232 – Master of Science (Food Science)



Food Science – Bundoora Campus

Project Description – Culinary herbs are a highly prized cooking ingredient and are used for imparting specific aroma and colour to various dishes, as well as for preserving food. The demand for fresh and ready to use culinary herbs continue to grow worldwide due to the growing influence of cooking shows, popularity of ethnic cuisines, readiness of consumers to try new flavours and increasing knowledge about the health-promoting properties of various herbs.

The Australian culinary herb industry is relatively small but is growing each year at a consistent rate of 5-10%. To be successful in a globalised market place, it needs to implement more efficient production and pest management systems and develop innovative premium quality products with improved shelf-life. Currently, over 20% of all herbs grown in Australia end up as trimmings or grading out due to specifications required by the retailers and customers. This leads to a significant waste disposal cost and burden on resources.

Thus, there is urgent need to develop new formulations and processing and packaging technologies for transforming waste and low value herbal trimmings into high value, high quality, and shelf-stable products, and reduce post harvest losses.

Therefore this project plans to transform waste herb trimmings and low quality product into high value food products and ingredients. This will include the development of new formulations/recipes, processing protocols, products, shelf-life and safety studies, and sensory and consumer testing.

References 1. Australian Government. National Food Waste Strategy: Halving Australia’s food waste by 2030,

Commonwealth of Australia 2017 [Internet] Canberra (Australia): Australian Government; 2017 [ cited17/03/2018]; [54 p.] Available from: https://www.environment.gov.au/system/files/resources/4683826b-5d9f-4e65-9344-a900060915b1/files/national-food-waste-strategy.pdf

2. McCrindle. Food Bank Hunger Report 2017, FoodBank 2017 [Internet] Australia: McCrindle; 2017 [ cited17/03/2018]; [20 p.] Available from: https://www.foodbank.org.au/wp-content/uploads/2017/10/Foodbank-Hunger-Report-2017.pdf

3. Department of the Environment and Energy. Canberra (Australia): Tackling Australia’s food waste; 2017[cited 19/02/2018]. Available from: http://www.environment.gov.au/protection/national-waste-policy/food-waste

4. Australian Government.Working together to reduce food waste in Australia, Commonwealth of Australia2017


Prof Harsharn Gill ([email protected]) Dr Jessica Danaher ([email protected]) Dr Lisa Newman ([email protected])





Biotechnology and Biological Sciences – Bundoora

Project Description – There is increasing evidence that endocrine disruption from exposure to chemical pollutants impacts wildlife including birds on a global scale (Sumpter, 2003, Nordstad 2012, Tartu et al. 2014). Alterations in wildlife development, reproduction, and behaviour that coincide with the presence of endocrine disrupting chemicals (EDCs) have been widely reported (Colburn, et al. 1993, Damstra, et al. 2012). Persistent organic pollutants (POPs) and Mercury (Hg) are recognised EDCs (Kibria et al 2010) that are bioaccumulated and bioconcentrated up trophic chains with detrimental effects on top predators. There is no research linking effects of bioaccumulated POPS or Hg in Australian seabirds with their endocrinology and health. Finger et al. (2016) established that little penguins (Eudyptula minor ) in a site in Melbourne had detectable concentrations of Hg in their tissues with an increasing trend over 3 years from 2011- 2013; while resident penguins in this city location had higher blood and feather Hg concentrations than those from a remote island (Finger et al. 2015). These studies did not investigate effects on the endocrine function or health of the birds. Professor Dayanthi Nugegoda has a current collaborative SWRRFI grant to study pollutant body burdens and hormone levels in shearwaters from Lord Howe Island. The current research project will similarly evaluate pollutant body burdens in relation to health indicators including endocrinology and reproduction in Little Penguins in Phillip Island Nature Park. The project will be co-supervised and field support provided by research staff from the Park.

References Colburn, T., F. S. vom Saal and A. M. Soto (1993). "Developmental effects of endocrine-disrupting chemicals

in wildlife and humans." Environ Health Perspect 101: 378-384. Damstra, T., S. Barlow, A. Bergman, R. Kavlock and G. Van Der Kraak (2012). "Global Assessment of the

State-of-the-Science of Endocrine Disruptors." WHO/PCS/EDC.World Health Organization, Geneva, Switzerland.

Finger, A. Lavers, J. L. Orbell, J. D. Dann, P.,Nugegoda, D. and Scarpaci, C. (2016) Seasonal variation and annual trends of metals and metalloids in the blood of the Little Penguin (Eudyptula minor) Mar. Pollut. Bull., 110, 261-273.

Finger, A. Lavers, J. Dann, P. Nugegoda, D. Orbell, J. Robertson, B. and Scarpaci C.( 2015), The Little Penguin (Eudyptula minor) as an indicator of coastal trace metal pollution, in Environmental Pollution, 205, 365-377

Nordstad T, Moe B, Ove Bustnes J, Bech C., Chastel O, Goutte A, Sagerup K, Trouvé C, Herzke D T, Gabrielsen G.W (2012) Relationships between POPs and baseline corticosterone levels in black-legged kittiwakes (Rissa tridactyla) across their breeding cycle. Environmental Pollution 164, 219-226

Tartu S, Angelier F, Herzke D, Moe B, Bech C, Gabrielsen G W., Bustnes J O, Chastel O. (2014) The stress of being contaminated? Adrenocortical function and reproduction in relation to persistent organic pollutants in female black legged kittiwakes. Science of The Total Environment, 476-477, 553.

Sumpter J.P. 2003. Endocrine disruption in wildlife: The future? Pure and Applied Chemistry 75, 2355-2360.

Contact Details: To discuss this project further please contact: Professor Dayanthi Nugegoda ([email protected].) Supervisor – Office: B223 F01 R044

DR231 – PhD (Applied Biology/Biotechnology)

MR231 – Master of Science (Applied Biology/Biotechnology)


Overcoming reduced proteostatis in ageing with a systems biology

approach

Discipline of Biosciences and Food Technology

Bundoora

PhD Project


This project will utilize yeast expressing deleterious proteins, such as amyloid beta, that accumulate with

ageing in humans and in yeast. The ageing in yeast will be assayed in a systems biology approach to examine

ways in which proteostasis can be improved in aging cells.

A substantial contributor to ageing is a decline in proteostasis. The impact of this is particularly obvious in

the brain and neuronal cells where loss of function and cell death may result from the lack of clearance of

proteins. The Alzheimer's amyloid beta protein accumulates with age and its toxicity is associated with

neuronal death. In yeast it can be observed that amyloid beta also accumulates with age and is toxic. Studies

of GFP-labelled amyloid beta in yeast have provided a convenient opportunity to study cellular mechanisms to

overcome toxicity and to study compounds that overcome toxicity [1,2]. Increasing autophagic turnover of

proteins is one mechanism to reduce problematic proteins of ageing. Some compounds enable old cells to do

this more effectively, and it is of interest to examine ingredients in foods that may also act in this manner. It

can be inferred that improving proteostasis will delay the incidence of Alzheimer's disease, Parkinson's

disease, and possibly other diseases where the accumulation of toxic proteins is highly deleterious to the brain.

References

[1] Porzoor and Macreadie. Systems biology of Alzheimer’s disease: Methods and protocols. (Castrillo JI and

Oliver SG ed.), Humana Press Ch 12: 217-226 (2015).

[2] Porzoor et al. Biomolecules 5: 505-527 (2015).

Contact Details:


1st Supervisor –Prof Ian Macreadie, Bundoora 223.1.28C

2nd Supervisor – A/Prof Benu Adhikari, Bundoora 201.6.5




Production of worm proteins for testing as novel immunosuppressants

Discipline of Biosciences and Food Technology

Bundoora

PhD Project


This project is multi-disciplinary and a requires joint effort from 3 groups for cloning and yeast expression,

protein characterization and purification, and testing in an animal model.

Hookworms suppress the immune system enabling their survival in humans [1, 2]. The ways in which they do

this could provide opportunities for new therapeutic interventions in diseases where chronic inflammation is a

major problem .e.g. asthma, inflammatory bowel disease, psoriasis, fibromyalgia, etc. Prof Alex Loukas

(James Cook University) has identified proteins secreted from hookworms that may suppress the immune

system, allowing worms to persist in humans. He has also developed a mouse model for chronic

inflammation, including asthma, where the hookwork proteins can be evaluated for their efficacy [3]. Prof

Loukas will provide genes coding for these candidate immunosuppressing proteins. The cloning and small

scale secretion of these proteins from Pichia pastoris will be performed with Prof Ian Macreadie at RMIT

University. Scale up and purification of the proteins will be performed with Adj Prof George Lovrecz at

CSIRO. The purified proteins will be tested in mice models of chronic inflammation, under the supervision of

Prof Alex Loukas (JCU). JCU will be responsible for ethics approval.

References:

[1] McSorly and Loukas Parasite Immuno (2008) 32, 549 doi: 10.1111/j.1365-3024.2010.01224.x.

[2] Ruyssers et al. Clin Devel Exp Immunol (2008) 567314 doi: 10.1155/2008/567314

[3] Navaro et al. Science Translational Med. (2016) 8, 362, 362ra143

DOI: 10.1126/scitranslmed.aaf8807

Contact Details:


1st Supervisor –Prof Ian Macreadie, Bundoora 223.1.28C

2nd Supervisor – Adj Prof George Lovrecz, CSIRO Clayton and Parkville

3rd Supervisor – Prof Alex Loukas, James Cook University, Cairns

DR232– PhD (Food Science)MR232 – Master of Science (Food Science)


Identifying colonisation factors in Campylobacter jejuni.

BFT, Bundoora West


One of the primary organisms causing food poisoning worldwide is Campylobacter, particularly C. jejuni.

This is largely due to the consumption of poultry. The bacterium colonises poultry to such high levels that

there is inevitably contamination of some meat after processing. If the meat is incorrectly prepared,

undercooked etc then human infection can occur.

As a commensal in poultry, Campylobacter must avoid the chicken gut immune system. How it does this is

largely unknown. There is presumably a combination of tolerance and active suppression. The molecules that

mediate such suppression are unknown. We have identified five molecules that are non-classically secreted

from the bacterium that may play a role in this. Previous work using molecular modelling and tissue culture

assays indicated one may induce pores in target cells.

This project will use a combination of genome mining, molecular modelling, recombinant protein expression,

protein structure determination, and immunology to help to uncover the functions of the molecules. If they are

shown to be involved in avoiding the immune system, they are prime targets for immunotherapeutic

intervention, which may allow chickens to target C. jejuni, reducing colonization. This would significantly

reduce carcass contamination and subsequent human infection.

Contact Details:


Professor Peter Smooker ([email protected]) Location: 223.1.29, tel 99257129




Detection and Identification Procedures for Foodborne Pathogens


Project Description

This project aims to compare, contribute and address deficiencies and gaps in orthogonal measurement and

confirmation methods for the detection and quantification of foodborne pathogens, including a range of

bacteria and foodborne viruses. The research will improve techniques to better protect Australian consumers

from foodborne illness and assist the food manufacturing industry in providing its consumers with safe food

products. More specifically, this research will:

• Compare current methods of detection for Foodborne viruses and develop methods or enhanced

sampling plans to improve the detection of viruses, particularly Hepatitis A (HAV), from foods and publish

the findings (this research will provide the content for two publications).

• Compare modern methods of detection of foodborne pathogenic bacteria using Matrix Assisted Laser

Desorption Ionisation - Time of Flight (MALDI-TOF) mass spectrometry and Fourier Transform Infrared

microscopy (FTIR)

• Establish novel techniques for the detection of foodborne viruses from different matrices (mass

spectrometry, FTIR) and publish the findings.

• Evaluate and design methods for the direct isolation of foodborne pathogenic bacteria such as

Salmonella spp., Listeria spp. and E. coli (among others) from foods with minimal enrichment for

identification with the FTIR microscope and publish the findings.

The research will be conducted in collaboration with the National Measurement Institute - Australia (NMIA)

and research will jointly be undertaken at the NMI’s Port Melbourne site and RMIT University. The NMIA is

the peak Australian measurement body whose vision is to ‘provide measurement policy, science and

regulation that underpins the economy and well-being of Australia’.

Contact Details


Professor Peter Smooker ([email protected]) Room– 223.1.29, Ph: 99257129 Mrs Prue Bramwell ([email protected]) Room - 223.1.48, Ph: 99257128





PhysiOmics-on-a-chip: integrated fluidic and imaging systems for high-throughout phenotypic screening in drug discovery and predictive

toxicology


Project Description – Small model organisms such as i.e. C.elegans, D.melanogaster and zebrafish offer some of the most promising alternative and cost-effective biological models for high-throughput predictive toxicology, molecular pathology and drug discovery routines.

Unlike cell lines, small metazoan model organisms offer an intact, multicellular system that models integrative biochemical and physiological processes. At the same time they are amenable to non-invasive, whole-animal in vivo imaging. The latter can be utilized for both single-cell imaging of processes such as e.g. apoptosis as well as imaging of physiological process such as cardiovascular system function, central nervous system development and even imaging of entire organism neurobehavioral responses. Combined with high fecundity, transparency, and small size, such organisms are ideal model systems for chemical screening and drug discovery efforts.

The lack of high-throughput and high-content screening systems available for rapid phenotypic profiling on living small model organisms is currently a significant impediment to discovering more sensitive toxicological endpoints and performing accelerated drug discovery routines.

Given the unmet need for novel drugs, this project seeks to develop innovative Lab-on-a-Chip technologies to non-invasively assess various physiological endpoints using small organisms such as transgenic zebrafish. The new technologies will provide the next generation high-content phenotypical based on non-invasive analysis of biological complexity in living organisms.

Skills learned in this project: - Lab-on-a-Chip technologies design including 3D Computer Assisted Design (3D CAD)- Computational Fluid Dynamics (CFD) in silico modeling- Microfabrication methods incl. photolithography, stereolithography, laser ablation- Metrology incl. optical profilometry and scanning electron microscopy (SEM)- Integration with sensors and actuators to achieve high-throughput automated bioanalysis- Biological assays for validation of chip-based technology performance- Statistical data analysis, report writing and scientific data presentation

Future career opportunities: skills gained in this project can facilitate future careers in: biomedical devices industry, Lab-on-a-Chip R&D, drug discovery, biopharmaceutical industry

References [1] Fuad et al., Sensors and Actuators B: Chemical 256 (2018), 1131-1141.[2] Fuad et al., Biomicrofluidics 11(5) (2017), 051101.[3] Zhu et al., Environmental Science & Technology 49(24) (2015), 14570-14578.[4] Zhu et al., Zebrafish 12(4) (2015), 315-318.[5] Skommer & Wlodkowic, Expert Opinion in Drug Discovery 10(3) (2015), 231-244.

Contact Details: To discuss this project further please contact: A/Prof Donald Wlodkowic ([email protected]) Supervisor Office 223.1.32A




Phenomics Laboratory – A/Prof Wlodkowic (Science, Bundoora) Kaslin Research Group – Dr Kaslin (ARMI, Monash University)

Jusuf Research Group – Dr Jusuf (School of BioSciences, The University of Melbourne)

Project Description – The developing nervous system is a sensitive target for pharmaceutical compounds and environmental toxicants. Developmental neurotoxicity (DNT) in early life-stage exposures can lead to temporary or permanent, long-term effects on motor activity, sensory function, and cognition. Currently, there is minimal data available on DNT for thousands of chemicals used in commerce. Moreover, many pharmaceuticals still have poorly characterized DNT side-effects.

Altered neurological functions and neurotoxicity will generally be behaviourally apparent and together with electrophysiology analysis can serve as useful end-points in the DNT profiling. DNT profiling can be very useful to develop new neuroprotective drugs, as well as elucidate neurotoxic mechanisms of pharmaceutical drug candidates or environmental contaminants.

This project will explore innovative discovery models of DNT profiling such as neurobehavioral zebrafish embryo bioassays, electroretinography, live neuron imaging and intact animal bioenergetic profiling to investigate impact and mechanisms of neurotoxic chemicals at the physiological level (PhysiOmics). Unlike cell lines, zebrafish embryo models offer an intact, multicellular system that integrates physiological processes and, at the same time, allows noninvasive, whole-animal imaging and analysis at both the morphologic- and functional-level. Such in vivo discovery system can be utilized to rapidly discover new molecular countermeasures to protect the nerve cells exposed to harmful chemicals (neuroprotective drugs).

Skills learned in this project: - fluorescent microscopy, video-microscopy and time-resolved imaging- non-invasive neurobehavioral bioassays on living zebrafish embryos- fluorescent bioassays on living zebrafish embryos and neuronal cell cultures- non-invasive bioenergetic bioassays on living neuronal cells and fish embryos- non-invasive zebrafish electroretinography- statistical data analysis, report writing and scientific data presentation

Future career opportunities: skills gained in this project can facilitate future career in: neurosciences, cell biology, drug discovery, predictive toxicology, environmental toxicology, biotechnology companies, pharmaceutical companies, agrochemical companies. References

[1] Huang et al., Science of The Total Environment 615 (2018), 107-114.[2] Campana and Wlodkowic, Environmental Science & Technology 52(3) (2018), 932-946.[3] Fuad et al., Biomicrofluidics 11(5) (2017), 051101.[4] Lindsey and Kaslin, Zebrafish 14(6) (2017), 574-577.[5] Kokel et al., Nature Chemical Biology 6(3) (2010), 231-237.



DR231– PhD (Applied Biology/Biotechnology) MR231 – Master of Science (Applied Biology/Biotechnology)



Phenomics Laboratory – A/Prof Wlodkowic (RMIT, Science, Bundoora) Brander Cancer Research Institute – Prof Darzynkiewicz (NYMC, NY, USA)

NCI-NIH - Dr Telford (NIH, Bethesda, USA)

Project Description – Beyond their contribution to basic metabolism, the major cellular organelles, in particular mitochondria and the endomembrane system, can determine whether cells respond to stress in an adaptive (survival) or suicidal (death) manner. The latter processes are commonly referred as programmed cell death (PCD). PCD underlies many pathological conditions arising upon exposure to toxic chemicals, side-effects or overdoses of pharmaceuticals. Many conditions such as neurotoxicity, cardiotoxicity, nephrotoxicity arise from induction of molecular processes that initiate and execute the tightly controlled molecular processes of cell suicide.

Cell death and the closely intertwined regulation of cell cycle in eukaryotic cells serve as useful end-points in the toxicological profiling of chemicals that can be harmful. The utilization of modern fluorescent assays enables researchers to perform real-time, high-throughput and multiparameter analysis on living cells exposed to toxicants or pharmaceuticals. This enables us to rapidly characterise, track and quantify the modes of cell death as well as develop new molecular countermeasures to protect the cells exposed to harmful chemicals (cytoprotective chemicals).

This project will explore induction of PCD as well as perturbations in the cell cycle in response to environmental pollutants. Results will increase our still limited understanding of the impact of toxicants on adaptive cell process, induction of the PCD and the de-regulation of cell cycle underlying mechanisms such as neurotoxicity, developmental toxicity and cardiotoxicity.

Skills learned in this project: - in vitro cell culture, fluorescence bioassays on living cells and fish embryos- real-time metabolic bioassays on living cells and fish embryos- cell proliferation assays- DNA damage: single cell Comet assay, H2AX phosphorylation assays- multiparameter flow cytometry- automated time-lapse fluorescent microscopy- multimodal microplate scanners with build-in cell culture- statistical data analysis, report writing and scientific data presentation

Future career opportunities: skills gained in this project can facilitate future career in: cell biology, drug discovery, predictive toxicology, neurosciences, experimental biology, environmental toxicology, clinical toxicology and biopharmaceutical companies. References

[1] Campana and Wlodkowic, Environmental Science & Technology 52(3) (2018), 932-946.[2] Adams et al. The Journal of Physiology 594 (12) (2016), 3245-3270.[3] Skommer et al. Biosensors and Bioelectronics 42 (2013), 586-591.[4] Wlodkowic et al. Methods in Cell Biology 103 (2011), 55-98.[5] Wlodkowic et al. Cytometry Part A 77(7) (2010), 591-606.


DR231– PhD (Applied Biology/Biotechnology)



Behavioural Ecotoxicology: Discovering intertwined effects of

contaminants on aquatic animal behaviour and metabolism


PhD Project Project Description –

Behavioural toxicology has emerged as a promising discipline to bridge the laboratory-to-field divide. Animal

behaviour integrates the internal physiology of the animal and the external conditions of the environment.

Toxicant-induced behavioural impairments often point to underlying physiological deficits and can be linked

to bioenergetic deficits and/or impairment of major organ systems such as underlying neuro- and

cardiotoxicity. Evaluating behavior and energy expenditure can be used to rapidly evaluate impact of drugs

and toxicants – especially if the affected parameters relate directly to survival, growth, or reproduction.

Neurobehavioral responses and metabolic profiling significantly precede mortality and are gradually gaining

acceptance as more sensitive and ethically acceptable endpoints of sub-lethal stress.

The lack of high-throughput and high-content screening systems available for behavioural toxicology and

metabolic profiling is currently a significant impediment to discovering more sensitive toxicological

endpoints. It is also limiting fundamental physiological studies on mechanisms underlying specific life-stage

responses to chemical stressors.

This project aims to develop and validate new high-content screening bioassays applicable for marine and

freshwater invertebrate aquatic model species. It will utilize the video-microscopy and fluorescent real-time

probes to non-invasively assess behavioural and metabolic endpoints. The data will be cross-correlated with

conventional techniques and bioenergetics profiling to develop a physiological framework underlying rapid

screening in environmental risk assessment.

Skills learned in this project:

- video-microscopy and video imaging of small aquatic model organisms

- behavioural bioassays such as analysis of swimming activity, avoidance behaviours, etc

- ecotoxicological bioassays according to standard OECD/EPA guidelines

- multimode high-content plate scanners

- real-time bioenergetic profiling using fluorescent indicator probes

- high-throughput behavioural screening and data analysis

- statistical data analysis, report writing and scientific data presentation

Future career opportunities: skills gained in this project can facilitate future career in: ecotoxicology,

environmental risk management, predictive toxicology, neurosciences, drug discovery, biopharmaceutical

industry, agrochemical industry

References:

[1] Huang et al., Sensors and Actuators B: Chemical 226 (2016), 289-298

[2] Huang et al., Scientific Reports 7(1) (2017), 17603

[3] Campana and Wlodkowic, Environmental Science & Technology 52(3) (2018), 932-946

[4] Heckmann et al., Environmental Toxicology and Chemistry 29(6) (2010), 1396-8

[5] Kokel et al., Nature Chemical Biology 6(3) (2010), 231-237.

Contact Details:





Defining Genetic Causes of Antifungal Drug Resistance


Project Description

Fungi infect 1.7 billion people world-wide and 1.5 million people die from invasive fungal disease (IFD) annually (1). Deaths due to fungal infections are increasing with mortality often exceeding 50% with delayed treatment further increasing this mortality rate to 100% (1). Fungal infections represent a significant disease burden in Australia, with an estimated 62,341 hospitalizations, 4967 deaths and a cost of $583 million over a 5-year period recorded in 1995-1999 (2). A major contributor to these high mortality rates is the limitednumber of antifungal drugs available and emerging antifungal drug resistance. Resistance to every class ofantifungal drugs has been described and for some drug classes is extremely common (3). Antifungal resistancecan cause relapse of the IFD and further increase mortality rates (3, 4). Clinical strains of the fungal pathogenCryptococcus neoformans have been isolated which possess a higher than normal mutation rate (a mutatorphenotype) and also display rapid emergence of resistance to antifungal drugs (5).

This project will aim to:

• Determine if an elevated mutation rate accelerates the development of resistance to antifungal drugs inthe fungal species which represent a significant disease burden both worldwide (C. neoformans) andin Australia (Aspergillus and Candida)

• Define the molecular mechanisms giving rise to drug resistance both in vivo and in vitro. Thisinformation will contribute to the development of a diagnostic assay enabling rapid on-site detectionof fungal pathogens and antifungal drug resistance.

Expertise will be gained in advanced molecular biology, the genetic manipulation of fungal pathogens (generation of genetically modified organisms), bioinformatics and genomics.

References

1. Brown GD, Denning DW, Gow NA, et al. 2012. Hidden killers: human fungal infections. Sci Transl Med4:165rv13.

2. Slavin M, Fastenau J, Sukarom I, et a.l 2004. Burden of hospitalization of patients with Candida andAspergillus infections in Australia. Int J Infect Dis 8:111-20.

3. Coelho C, Casadevall A. 2016. Cryptococcal therapies and drug targets: the old, the new and the promising.Cell Microbiol 18:792-9.

4. Rhodes J, Beale MA, Vanhove M, et al 2017. A Population Genomics Approach to Assessing the GeneticBasis of Within-Host Microevolution Underlying Recurrent Cryptococcal Meningitis Infection. G3 (Bethesda) 7:1165-1176.

5. Boyce KJ, Wang Y, Verma S, et al. 2017. Mismatch Repair of DNA Replication Errors Contributes toMicroevolution in the Pathogenic Fungus Cryptococcus neoformans. MBio. 8(3). Pii: e00595-17.

Contact Details


Dr Kylie Boyce email: [email protected] (03) 9925 7101 Prof Ian Macreadie email: [email protected] (03) 9925 6627






Bioscience and Food Technology/ Tissue Engineering & NanoBiotechnology Research Laboratory –

RMIT Bundoora/City

Multicentre cross disciplinary Joint PhD Project


Current research in pancreas stem cell biology has achieved limited success in generating alternate cell types

for treatment of diabetes. This project aims to deliver three-dimensional scaffolds that will not only allow for

efficient differentiation of pancreatic cells but also enhance their survival and function through integration of

endothelial cell microfluidic channels. This study will be executed through expert collaborators from cross

disciplinary research areas viz. bio-printing, cell biology, and engineering in a milestone-driven workflow.

Current tissue engineering efforts in islet micro-encapsulation, (which provide a quasi-3D scaffold) have

achieved limited success because these scaffolds i) do not support vascularisation and hence low impact on

the long-term survival; or ii) are immunogenic and prone to fibrosis and immune cell attack. Such strategies

have been tested since early 1990s. To develop our nanobio-printing derived scaffold, we aim to i) identify

and characterize the appropriate biomaterial (already available and from the RMIT nanobio-material library)

for 3D printing of islets; ii) test the biomaterial, Bioprinted-scaffold and cellularised scaffold/devices for in

vitro function; and iii) assess the functional potential of these nanobioprinted scaffolds in animal models. The

project will deliver bio-printed 3D scaffolds via two major approaches: i) Biomimicry, and ii) Autonomous

self-assembly, along with integrated microfluidics to collectively deliver the desired nano-scale derivation of

cellular microenvironment on a bioprinted tissue scaffold which will be tested for glucose responsiveness. The

success of PRINT cell project will be measured by transplantable 3D printed functional Islet scaffolds,

knowledge/IP generation, multidisciplinary research training and advancement of knowledge in the field.

A prospective PhD student with strong background knowledge of Cell Biology/ 3D Bioprinting with ever-

lasting enthusiasm and risk taking capacity are encouraged to apply. Student will have ample opportunities to

learn state-of-the-art cross disciplinary tools and techniques available in the three different school in RMIT, St

Vincent Hospital and NHMRC Clinical Trails Centre (CTC) through routine mentoring and interaction. In

addition, student will be nurtured for research ethics, critical thinking, time & project management, and

problem-solving skills.

Meet the Team: Ravi Shukla, Anand Hardikar, Milan Brandt, Vipul Bansal, Ma Qian, Khasharyar

Khoshmanseh, Sara Baratchi, Mugdha Joglekar, Wayne Howthrone, Zeyad Nasa, Suresh Bharagava,

References

1. A 3D map of the islet routes throughout the healthy human pancreas. Scientific Reports, 2015, 5, 14634

2. Epithelial-to-mesenchymal transition in pancreatic islet beta cells. Cell Cycle 2010, 9(20), 4077-4079

3. New pancreas from old: microregulators of pancreas regeneration. Trends in Endocrinology and

Metabolism, 2007, 18(10), 393-400

Contact Details:

To discuss this project further please contact anyone of us:

RAVI SHUKLA ([email protected]) Joint Supervisor – Office +61 3 9925 2970

ANAND HARDIKAR ([email protected]) Asso. Supervisor– Office +61 2 9562 5000

MILAN BRANDT (milan.brandt@rmit,edu.au) Joint Supervisor – Office +61 3 9925 4179

KASH KHOSMANESH ([email protected] ) Asso. Supervisor –Office +61 3 9925 2851

SARA BARATCHI ([email protected]) Asso. Supervisor –Office +61 3 9925 7036

https://scholar.google.com.au/citations?user=_p-K5eUAAAAJ&hl=en

http://sydney.edu.au/medicine/people/academics/profiles/anand.hardikar.php

https://www.youtube.com/watch?v=-5ELveQ_7tA

https://scholar.google.com.au/citations?user=MskJvAUAAAAJ&hl=en

https://www.rmit.edu.au/contact/staff-contacts/academic-staff/m/ma-professor-qian



https://scholar.google.com.au/citations?user=hzyWO7UAAAAJ&hl=en


https://www.westmeadinstitute.org.au/people/researcher-profiles-search/professor-wayne-hawthorne/biography





mailto:milan.brandt@rmit,edu.au







Project Description – The design of the interfaces of search engines has been relatively fixed for the past 25 years: a user types in a query, clicks a search button and is presented with a list of results. The user scans the list and clicks on any results that look promising. A great deal of research has considered how to approximate user satisfaction from the way that users click: how long do they dwell on search results, how quickly do they reformulate their query? However, user interfaces to search engines are changing: increasingly users speak queries to search engines, search engines often return answers, not links to documents; systems like Siri or smart watches speak or display highly concise answers. In many of these situations, the user will never click on a result. The research question of the project will be:

With the evolution of search engine interfaces, how can search engine designers know when a user is satisfied with their search results?

The project has the potential to examine one or more of the following aspects • To what extent can existing research methods be applied to these new interfaces?

o Extensive research has been conducted on modeling user behavior and approximating usersatisfaction. However, the vast majority of work has assumed users work with a classic click-based search engine interface.

• Can the way a user speaks their query indicate satisfaction and/or annoyance?o Emotion detection in speech is now possible, could such a signal indicate a user struggling

with their search task?• Can the range of sensors in a user’s phone or watch be exploited to detect clues to suggest user

satisfaction?o Mobile phones and watches can detect the context of the user (e.g. at home/work, in the car).

They can also detect the way they are being held. Could such clues to exploited to betterunderstand user satisfaction?

• Can knowledge of other activities a user undertakes be used to determine user satisfaction?o A user may not click on a search result, but by seeing an answer from a search engine, are the

activities a user does after searching affected by the answer?

References 1. Priyogi, B., Sanderson, M., Salim, F., Chan, J., Tomko, M., Ren, Y. (2018) Identifying In-App User

Actions2. from Mobile Web Logs. In Proceedings of The 22nd Pacific-Asia Conference on Knowledge Discovery

and Data Mining, Springer.3. Hassan, A., Shi, X., Craswell, N., & Ramsey, B. (2013, October). Beyond clicks: query reformulation as a

predictor of search satisfaction. In Proceedings of the 22nd ACM international conference on Conferenceon information & knowledge management (pp. 2019-2028). ACM.

4. Ong, K., Järvelin, K., Sanderson, M., & Scholer, F. (2018, March). QWERTY: The Effects of Typing onWeb Search Behavior. In Proceedings of the 2018 Conference on Human Information Interaction &Retrieval (pp. 281-284). ACM.

Contact Details: To discuss this project further please contact: Mark Sanderson ([email protected]) – Office 14.09.17





Project Description – Recommender systems underpin the way that Netflix, Amazon – and a wide range of e-commerce sites – observe the objects and activities that users like and then recommend something new. While extensive research has been conducted in recommender systems, there are still many potential projects. The focus of this project is to enable recommender systems to better react to the continual change in user’s preferences. The research question of the project will be

How to improve the accuracy of recommender systems by mitigating the problem of time drifting inputs using ensemble learning techniques?

The project will examine the following aspects • How will ensemble learning be adapted to manage user preference change?

o Ensemble learning requires a set of single learners to build from. These learning will need toincorporate a forgetting mechanism to discard old user preferences and be self-adaptive. Thisis considered a major scientific and engineering challenge. Once adapted an ensemble learnerwill be constructed.

• How is user preference change detected and monitored?o There are two approaches to define how the learner will adapt: informed methods that detect

the drift through triggering mechanisms and blind methods that implicitly are adapted tochanges without drift detection. Both approaches will be examined in this project.

• What are the performance criteria?o Here, we will focus on the criteria to obtain an unfailing system that handles user preference

change. The requirements for such systems are autonomy (i.e. the level of humaninvolvement), reliability, and complexity (i.e. time and memory consumption).

References 1. B. Kumar and N. Sharma, “Approaches, Issues and Challenges in Recommender Systems: A Systematic

Review,” Indian J. Sci. Technol., vol. 9, no. 47, 2016.2. M. Taghavi, J. Bentahar, K. Bakthiyari, and C. Hanachi, “New Insights Towards Developing

Recommender Systems,” pp. 1–35, 2017.3. J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A

survey,” Decis. Support Syst., vol. 74, pp. 12–32, 2015.4. I. Khamassi, M. Sayed-Mouchaweh, M. Hammami, and K. Ghédira, “Discussion and review on evolving

data streams and concept drift adapting,” Evol. Syst., 2016.5. Y. Koren, “Collaborative filtering with temporal dynamics,” Commun. ACM, vol. 53, no. 4, p. 89, 2010.6. H. Liu, X. Kong, X. Bai, W. Wang, T. M. Bekele, and F. Xia, “Context-Based Collaborative Filtering for

Citation Recommendation,” IEEE Access, vol. 3, pp. 1695–1703, 2015.7. Y. Kabutoya, R. Sumi, T. Iwata, T. Uchiyama, and T. Uchiyama, “A topic model for recommending

movies via linked open data,” Proc. - 2012 IEEE/WIC/ACM Int. Conf. Web Intell. WI 2012, pp. 625–630,2012.


Mark Sanderson ([email protected]) – Office 14.09.17 Yongli Ren ([email protected]) – Office 14.09.07

DR221– PhD (Computer Science) MR221 – Master of Science (Computer Science)


Effective Countering cyber threats over Smart-Grid Infrastructures


Project Description – Smart-grids are the cornerstones of future energy infrastructure. Today's ageing power grids have had their inadequacies exposed by challenges such as: the rising cost of fuels, need for lower greenhouse emissions, and the rise of renewable energy [1]. Tomorrow’s Smart Grids will incorporate increased sensing, communication, and distributed control systems to accommodate renewable generation, EV (Electric Vehicle) loads, peak demand reduction and energy loss minimization. Of the various impediments affecting smart-grids, security is the foremost concern for all stake holders, estimating economic losses at $6B annually [2]. This project aims to research a distributed and multi-granular framework for effective data security that is achieved through anomaly detection over smart-grid sensing systems. This will evaluate cyber threats from a data centric point of view for effective protection, and the framework will be compatible with and cater to the dynamic and non-homogeneous data typically present in multi-faceted smart-grid environments.

Specifically this project intends to extend some of the early work carried it out by Prof Tari [3-6] to produce: • Efficient data processing framework of a hierarchical nature that identifies the most optimal nodes foranalytical processing: This mitigates issues relating to the very large amount of streaming data involved, andthe complexity of a largely heterogeneous population of computational nodes involved in sensing andcommunication. The emphasis will be on dynamic evaluation of such nodes, based on limitations such as nodedensity, connectivity, availability, and ever-present resource constraints.• Distributed data-clustering technique based on the aggregated data over a defined node hierarchy.This will address limitations in centralised data clustering models, and factors in non-homogeneous data-domains. We will also consider cross-correlation of data across multiple nodes, allowing for optimal accuracyduring classification.• Multi-granular anomaly detection technique that will consider observed correlations (if any) amongstdata-clusters produced at each analytical level as well as individual data points on the defined node hierarchy.This will be an adaptive process able to clearly discern between abnormal and normal data in a non-parametricand non-probabilistic manner.

References [1] V. Gungor et al.: Smart Grid Technologies — Communication Technologies and Standards. IEEE

Transactions on Industrial Informatics, 7(4):529 –539, 2011.[2] H. Khurana et al.: Smart-Grid Security Issues. IEEE Security Privacy, 8(1), 2010.[3] A. Anwar, A. Mahmmood and Z. Tari: Ensuring Data Integrity of OPF Module and Energy Database

by Detecting Changes in Power Flow Patterns in Smart Grids. IEEE Transactions on IndustrialInformatics (TII), 13(6):3299-3311, 2017.

[4] A. Almalawi, A. Alharthi, Z. Tari, M. A. Cheema, and I. Khalil: kNNVWC: An Efficient k-NearestNeighbours Approach based on Various-Widths Clustering. IEEE Transactions on Knowledge and DataEngineering (TKDE), 28(1):68-81, 2016.

[5] A. Almalawi, X. Yu, Z. Tari, A. Fahad, and I. Khalil: Unsupervised Anomaly-Based Detection Approachfor Integrity Attack on SCADA Systems. Elsevier Journal on Computer Security, 46:94-110, 2014.

[6] C. Quieroz, A. Mahmood, and Z. Tari: A Probabilistic Model to Measure the Survivability of SCADASystems. IEEE Transactions on Industrial Informatics, 9(4):1975-1985, 2013.


Prof Zahir Tari ([email protected]) Office B014 F11 R018



Low-Latency High-Throughput Computational Models for Heavily Data-Driven Applications in Hybrid Data Centers


Project Description – The need to process a huge volume of data during in a small amount of time is dramatically increasing especially as the size of the data moves into Exabyte in the near [1]. While use of such applications was previously confined to the finance sector, it is becoming now prevalent in almost every industry where analytical processing over massive data sets can solve business problems. To meet such low-latency requirements of data mining and machine learning applications, datacentre providers must expand the computing capacity of the underlying infrastructure by exploiting graphics processing units (GPU) and Field Programmable Gate Arrays (FPGAs) as new hardware accelerators, the so called heterogeneous datacentres. However, there is no mechanism that can appropriately project the complex characteristics of modern applications emerging in enterprise/scientific domains into the available computing capacity of a system with hundreds or thousands of heterogeneous computers. Additionally, using existing resources allocation solutions in heterogeneous datacentres result in significant resource wastage [2][3]. The general aim of this project is to investigate innovative solutions/methods to control and to make use of the capabilities of the new hardware accelerators in a heterogeneous computing systems to substantially enhance the resource efficiency when running data-driven applications. The specific aims of this project are:

• To explore inference algorithms for modelling the complex interaction of different components ofdata-driven applications with the heterogeneous resources.

• To design resource allocation controlling algorithms that not only maximize the system performance,but also detect and resolve resources' bottlenecks.

• To equip the designed algorithms with adaptive optimal control tools for online tuning ofcorresponding parameters to effectively deal with modelling uncertainties in a datacentre as well asthe sporadic rise and fall in the incoming requests.

References [1] P. Lotfi-Kamran, B. Grot, M. Ferdman, S. Volos, O. Kocberber, J. Picorel, A. Adileh, D. Jevdjic, S.

Idgunji, E. Ozer, et al., “Scale-out processors,” in ACM SIGARCH Computer Architecture News, vol.40, pp. 500–511, IEEE Computer Society, 2012.

[2] R. Hameed,W. Qadeer, M.Wachs, O. Azizi, A. Solomatnikov, B. C. Lee, S. Richardson, C. Kozyrakis,and M. Horowitz, “Understanding sources of inefficiency in general-purpose chips,” in ACMSIGARCH Computer Architecture News, vol. 38, pp. 37–47, ACM, 2010.

[3] L. Keys, S. Rivoire, and J. D. Davis, “The search for energy-efficient building blocks for the datacenter,” in International Symposium on Computer Architecture, pp. 172–182, Springer, 2010.


Professor Zahir Tari ([email protected]) Office: B014 F11 R018





Project Description – Data exfiltration is the unauthorized leakage of data from computers by sophisticated malware and malicious insiders. Data exfiltration is a serious problem since it may have catastrophic effect on businesses, governments as well as individuals if such exfiltration involves sensitive data. Examples include exfiltration of data involving business inventions, national intelligence, classified research, individual’s credit card and biometric profile. Specifically, data exfiltration has resulted in huge economic losses as well as unprecedented breaches of national security. A study by the Ponemon Institute [1] reported that the average per-incident cost of reported data leakage by businesses was $4 million in 2015/2016, and the number of reported data breaches surpassed all previous years [2]. The aim of this project is to develop solutions to detect sensitive data exfiltration attempts by malwares, as well as human users, and block those attempts without affecting legitimate users’ normal usage of computers. The specific objectives of this project are to develop:

• Existing real-time identification techniques of sensitive data are not scalable for analyzing largeamount of data generated by memory resident application. This project will develop efficient datarepresentation and summarization techniques that will enable multi-granular searching of sensitiveinformation from memory.

• Some Malware can circumvent existing exfiltration detection mechanisms by loading small parts ofsensitive data from file or by accessing dynamically generated sensitive data (e.g., from Email). Thisproject will develop a meta-searching technique that will continuously monitor memory resident datato detect exfiltration attempts of sensitive data by malwares or insiders.

• A key limitation of existing techniques is malwares can circumvent existing detection mechanisms bypartially loading sensitive data over a period of time from file or by accessing dynamically generatedsensitive data (e.g., from Email). This project will develop a technique that can piece together thedifferent parts of data (dynamically generated) accessed over a period of time by by process(es), todetermine if those parts constitute sensitive data.

References

[1] L. Ponemon, “2016 Ponemon Institute Cost of a Data Breach Study,” Security Intelligence Institute.[2] DataLossDB, “Data Loss Statistics”. Retrieved 26 February 2017, from https://blog.datalossdb.org/


Professor Zahir Tari ([email protected]) Office B014 F11 R018






Project Description – Indoor positioning is the process of obtaining a device or user location in indoor environments, which enables a wide range of applications in health, industry, and surveillance. An increasing popularity of smartphones demands accurate indoor positioning solutions to be deployed in real world. Indoor positioning also facilitates building the infrastructure for emerging Internet of Things (IoT).

Wi-Fi based indoor positioning has been the most popular suite of technologies since most of the smartphones are Wi-Fi enabled. Such systems typically utilise radio signals collected from existing Wi-Fi access points. Studies [5-7] show that the accuracy of Wi-Fi based systems depends much on Wi-Fi bandwidth. New standards are evolving to support Wi-Fi access points with multiple antennas. This provides us an exciting opportunity to break the Wi-Fi bandwidth barrier, and hence strongly motivate us to revisit the design of indoor positioning systems.

In this project, we will investigate effective ways to increase Wi-Fi bandwidth and discover a fundamental breakthrough in accurate indoor positioning. To achieve this goal, we identify the following objectives to be accomplished. 1) Investigate signal propagation and its properties under Wi-Fi access points with multiple antennas.2) Discover key principles for practical system design, and develop advanced algorithms to push theperformance limit of indoor positioning.3) Develop demonstration applications utilising our positioning algorithms to provide real-life validationand also address practical issues for potential deployment.

References 1. Jie Xiong, Karthikeyan Sundaresan, and Kyle Jamieson. ToneTrack: Leveraging Frequency-Agile Radios

for Time-Based Indoor Wireless Localization, in Proc. of the 21st Annual International Conference onMobile Computing and Networking (MobiCom 2015), Paris, France, 7-11 Sept. 2015.

2. Jon Gjengset, Jie Xiong, Graeme McPhillips, and Kyle Jamieson. Phaser: Enabling Phased Array SignalProcessing on Commodity WiFi Access Points, in Proc. of the 20th annual international conference onMobile computing and networking (MobiCom 2014), Maui, Hawaii, USA, 7-11 Sept. 2014.







Human Activity Sensing with Wireless Signal



Project Description – Human activity sensing has drawn much attention due to the growing demands from many domain applications, such as surveillance, health care, and human-computer interaction. A traditional approach is to capture data from wearable sensors such as accelerometer and gyroscope, and process sensor data using machine learning or data mining algorithms. This approach, though proven to be effective, requires a user to wear on-body sensors which may be uncomfortable and obtrusive to the user.

In recent years, radio-based human activity sensing [1-2] has explored the possibility of using radio signals without the need of physical sensors or special devices. The underpinning principle is that the human body causes radio signal attenuation, and different body movements generate different changes in radio signals. By analysing these change patterns, we are able to discriminate different activities. Since radio-based sensing does not require a person to carry any device, it provides a contactless and privacy-preserving approach to detecting human activities. This new approach plays an important role in developing next generation applications. While the existing radio-based sensing systems look promising and encouraging, they mostly adopt a trial-and-error approach to make the system function based on tedious experiments and they lack the theoretical foundation to guide system design in practice. Understanding the fundamental theory and design principles in radio-based human activity sensing is crucial to the success of its real deployment in our society.

In this project, we will discover and reveal the underlying theory in radio-based human activity sensing. Having better understanding on the signal propagation properties, we will then develop a theoretical framework, and define the fundamental principles to guide practical system design. We will also validate our theoretical framework and demonstrate its scientific merit through building typical real-life activity sensing applications such as contactless activity detection and vital signs monitoring. We aim to achieve the following objectives. (1) Understand radio signal propagation and discover its properties in radio-based sensing systems. (2) Develop a realistic sensing model by extending our basic model, and define a set of key design principlesto guide practical system design for real-life applications. (3) Develop several activity sensing applications tovalidate our theoretical framework in real life. These applications will enable us to assess the scientific meritof our work, and also address practical issues for potential deployment.

References 1. X. Qi, G. Zhou, Y. Li, and G. Peng. RadioSense: Exploiting Wireless Communication Patterns for Body

Sensor Network activity recognition, In Proc. of the 33rd IEEE Real-Time Systems Symposium (RTSS2012), San Juan, Puerto Rico, USA, Dec 4-7, 2012.

2. X. Liu, J. Cao, S. Tang, and J. Wen. Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals, In Proc. ofthe 35th IEEE Real-Time Systems Symposium (RTSS 2014), Rome, Italy, Dec 3-5, 2014.









Project Description – With computers now present in everyday devices like mobile phones, credit cards, cars and planes or places like homes, offices and factories, the trend is to build embedded complex systems from a collection of simple components. Thus a complex surveillance system for a smart house can be “realised” (i.e., implemented) by suitably coordinating the behaviours (i.e., the operational logic) of hundreds (or thousands) of simple devices and artifacts—lights, blinds, a microwave, video cameras, robotic arms, etc.—installed in the house. The Behavior Composition problem involves automatically building an embedded controller-coordinator to bring about a desired target complex system by suitably coordinating the available components.

Researchers at RMIT had contributed significantly in the last 12 years to the composition problem, in collaboration with many other internationally recognised scientists and in particular with the AI group in La Sapienza University, Rome, Italy. A short high-level description can be found in [1], and more comprehensive treatment have been published in the Artificial Intelligence Journal [2,3], the premier journal in AI.

One application of composition that has recently received attention is smart manufacturing, where existing capabilities in a production plant are coordinated to achieve a production recipe [4-6]. Our work developed over the years is in fact used in the large 2012-2018 EU project EP/K018205/1 “EVOLVABLE ASSEMBLY SYSTEMS –Towards open, adaptable and Context-awaare equipment and systems,” which aims at developing highly adaptable manufacturing systems and includes 11 large industries such as Airbus, Siemens, and GE. Dr Brian Logan (University of Nottingham), one of the project’s investigators, has visited RMIT to learn about the problem and technique and is currently a collaborator on the topic (e.g., [4]).

Nonetheless, many (most) challenges remain unanswered. The overarching aim of this project is to develop a behavior composition account that meets the needs of the manufacturing domain, by accommodating production of multiple items, probabilistic models of failure, smooth transitions between production recipes, true concurrent execution of devices in the plant, execution of multiple different production recipes in the same production plant, etc. The project involves both theoretical work in developing adequate representation models and practical work in devising effective computational techniques to solve the problem efficiently.

References [1] Giuseppe De Giacomo, Fabio Patrizi, and Sebastian Sardina. Building virtual behaviors from partiallycontrollable available behaviors in nondeterministic environments. In Proceedings of the InternationalConference on Automated Planning and Scheduling (ICAPS), pages 523-526,, 2014.[2] Giuseppe De Giacomo, Fabio Patrizi, and Sebastian Sardina. Automatic behavior composition synthesis.Artificial Intelligence Journal, 196:106-142, 2013.[3] Giuseppe De Giacomo, Alfonso Gerevini, Fabio Patrizi, Alessandro Saetti, and Sebastian Sardina. Agentplanning programs. Artificial Intelligence, 231:64--106, 2016.[4] Paolo Felli, Brian Logan, and Sebastian Sardina. Parallel behavior composition for manufacturing. InProceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 272--278, 2016.[5] Lavindra de Silva, Paolo Felli, Jack C. Chaplin, Brian Logan, David Sanderson, Svetan M. Ratchev:Synthesising Industry-Standard Manufacturing Process Controllers. AAMAS 2017: 1811-1813[6] Paolo Felli, Lavindra de Silva, Brian Logan, Svetan M. Ratchev: Process Plan Controllers for Non-Deterministic Manufacturing Systems. IJCAI 2017: 1023-1030






Project Description – Plan-goal recognition (PR or GR) is the problem of identifying an agent’s intent or purpose by observing her behaviour. Its growing number of applications include language understanding and response generation, adversarial reasoning for games and the military, smart homes for the cognitively impaired, and human-machine interaction. Deception is the other side of the coin, and it involves the synthesis of behavior that while achieving the goals is able to deceive an observer as much as possible.

Traditionally, PR/GR has involved matching a sequence of observations to some plan in a given plan library, the “winning” plan being the one that best matches the observations [1]. Given that each plan presumably sets out to achieve a goal, having identified the agent’s plan, the observer has implicitly identified her goal. Recent developments in GR [2,3,4] dispense with the overhead of a plan library by treating the problem as one of “planning in reverse”. This innovation has enabled plan recognition to leverage advances made by the planning community and relies on a key insight that the probability of a plan can be linked directly to its cost, under the assumption that agents are rational.

The aim of this project is to push further the application of automated planning to the problem of goal recognition and deception. Over the last 3 years, researchers at RMIT University have studied the problem of GR [3] and deception [4] in the context of navigation. The former amounts to observing an agent navigate a space (e.g., an airport terminal or video game map) and understand its destination (e.g., specific gate or room in the game); the latter ask for the construction of trajectories that are “deceiving,” but still reach the intended destination. The work done has been welcome and [3] has received the Best Student Paper Award at the premier conference on Intelligent Agents and has been invited into the IJCAI-18 Sister Conference Track. This project aims to further extend those results in one or more of the following directions:

• Direction 1: integrate GR and deception in navigation with the more general case of task-orientedbehavior (as in [2]), by mixing spatial reasoning techniques with general action reasoning.

• Direction 2: extend existing planning-based techniques with adapting cost models, that are learnt overtime and change dynamically.

• Direction 3: develop deception framework in the context of an observer who may have limitedobservation capabilities

• Direction 4: develop techniques for designing observation capabilities to improve GR (e.g., installinga camera in particular strategic locations)

References [1] H. Kautz and J. Allen. Generalized plan recognition. In Proceedings of the National Conference on

Artificial Intelligence (AAAI), pages 32–37, 1986 [2] M. Ramirez and H. Geffner. Probabilistic plan recognition using off-the-shelf classical planners. In

Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 1121–1126, 2010.[3] Peta Masters and Sebastian Sardina. Cost-based goal recognition for path-planning. In Proceedings of the

International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pages 750--758, 2017. Best Student Paper Award.

[4] Peta Masters and Sebastian Sardina. Deceptive path-planning. In Proceedings of the International JointConference on Artificial Intelligence (IJCAI), pages 4368--4375, 2017.


http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1821

http://www.aamas2017.org/proceedings/pdfs/p750.pdf

https://www.ijcai.org/proceedings/2017/610




Computer Science & IT - City

Project Description – This project aims to study the problem of Influence Maximization for facility deployment in urban computing. Our first goal is: given a set of facility candidates U, a database of vehicle/user trajectories T and a budget L, how to find a set of facilities within the budget L so that the deployed facilities can influence the largest number of moving trajectories. We call it as the Optimal Facility Deployment problem. In this project, we expect to solve four research questions, each of which is raised from the following challenges. The first core challenge is that multiple facilities have influence overlap on trajectories and it is critical to identify and reduce the overlap of the influence among different facilities, while keeping the budget constraint into consideration. The second challenge is that, the deployment of each facility has non-equal cost, which makes this problem NP-hard. Therefore, we would like to devise a series of algorithms that both achieve theoretically non-trivial approximation ratio while performing as efficient and feasible as possible for decision making purpose. The third challenge is how to efficiently handle the incremental version of the facility deployment problem: given an existing placement plan and more budgets, how to place more facilities in a way that further maximize the influence as much as possible. We aim to incrementally update the current placement without computing the global influence from scratch. The last challenge is how to build an appropriate model to measure the cost and benefits in multi-faceted way, to cater for different scenarios and needs.

Significance and Benefits: The solution to this problem can significantly advance almost any facility selection problem in urban area, where an influence overlap always exists. In particular, it is useful but not limited to the optimal deployment of warehouses, gas stations, electric vehicle charge stations, outdoor advertising billboards.

Required Background: Strong Algorithm design and analysis, Data Mining (with a focus on clustering), Database Index

References [1] G. Li, S. Chen, J. Feng, K.-l. Tan, and W.-s. Li. Efficient location-aware influence maximization. InProceedings of the 2014 ACM SIGMOD international conference on Management of data, pages 87-98.ACM, 2014.[2] S. Khuller, A. Moss, and J. S. Naor. The budgeted maximum coverage problem. Information ProcessingLetters, 70(1):39-45, 1999.

Contact Details: To discuss this project further please contact: 1st Supervisor – Dr. Zhifeng Bao ([email protected])




CSSE discipline, Machine Learning, Big Data and Data Analytics Groups Location (City)

PhD Project

Project Description Sequence based problems involve learning, predicting and recommending sequences, an order set of entities, and a subset of these have characteristics of (machine) learning and optimisation problems. Three examples are tour recommendation, journey planning and recommending routes in ride-sharing taxis. In tour recommendation [1], an itinerary of places to visit and transportation schedules are recommended to tourists based on their interests and past travels. In journey planning [4], a trip from A to B using different transportation modes are recommended to users based on their preferences and transportation schedules. In route recommendation in ride-sharing taxis, a route is suggested for taxi drivers to maximise the chances they will get a fare and minimise idle time. All these applications are sequence based problems and are simultaneously a machine learning problem, e.g., recommending locations to visit, learning preferences and predicting traffic, and an optimisation problem, e.g., scheduling under constraints. Solving this recommendation and scheduling problem requires a joint, integrated approach.

In the group, there has been work done in itinerary recommendation [3] and journey planning [4], but there is still the open problem of how to simultaneously learn from data, either for preferences or utilising previous sequences and solve sequence based optimisation that satisfies non-trivial constraints.

In this project, the student will research novel approaches that simultaneously learns and optimises. As initial examples, the project will apply this to itinerary recommendation and journey planning, but there is scope to expand to other applications.

References: [1]. J. Borràs, A. Moreno, and A. Valls. Intelligent tourism recommender systems: A survey. Expert Systems with Applications, 41, 16 (2014), 7370–7389. [2]. K. Lim, J. Chan et al. Personalized Itinerary Recommendation with Queuing Time Awareness, in Proceedings of ACM CIKM, (to appear), 2017. [3]. K. Lim, J. Chan et al. Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Duration, in Proceedings of AAAI IJCAI, pp. 1778-1784, 2015. [4]. M. Haqqani, X. Li and X. Yu. An Evolutionary Multi-criteria Journey Planning Algorithm for Multimodal Transportation Networks, in Proceedings of ACALCI, pp.144-156, 2017.


Dr Jeffrey Chan ([email protected]) 1st Supervisor – Office 14.08.15 Prof Xiaodong Li ([email protected]) 2nd Supervisor – Office 14.08.14B






Project Description Machine learning is about developing and using models to solve many prediction and learning problems from data. Combined with the availability of large volumes of data, advances in large scale processing, machine learning has captured the imagination of the general public and is one of the main drivers of the recent advances in artificial intelligence and data science.

However, there remains many outstanding and open challenges. In this project, we aim to tackle such challenges and research cutting edge problems in machine learning, particularly in dimensionality reduction, hybrid learning models, deep learning and graph based learning. These include but not limited to:

• Novel approaches for dimensionality reduction, e.g., in matrix factorisation [1] and feature selection[2], which can help with improving predictive performance, identifying information biomarkers, andimprove modern day recommender systems.

• Research into hybrid deep learning models, that combines deep learning with more traditionalmachine learning models. An example is in deep recommendation learning [3], that combines theability of deep learning models to learn arbitrary functions with factorisation models strength inrecommendation.

• New approaches in hybrid machine learning and contraint programming models, that incorporates thestrengths of both in order to easily incorporate complex user feedback and guidance.

• New approaches in graph based learning, including blockmodelling [4]. This includes developing ofnew probabilistic matrix factorisation models that combine facets of probabilistic Bayesian modelswith factorisation models, allowing contextual information to be easily incorporate.

The supervisors form a larger Melbourne based machine learning cluster, which are actively engaged in studying and researching on these problems. The student will perform such research and join this larger based machine learning cluster.

References 1. D. Lee and S. Seung, “Learning the parts of objects by non-negative mattrix factorization”, Nature, 401,pp. 788-791, 1999.2. X. Nguyen, J. Chan, S. Romano and J. Bailey, “ Effective Global Approaches for Mutual InformationBased Feature Selection”, in Proceedings of the 20th ACM SIGKDD International Conference on KnowledgeDiscovery and Data Mining, pp. 512-521, 2014.3. S. Zhang, L. Yao and A. Sun, “Deep Learning based Recommendation System: A Survey and NewPerspective”, arXiv: 1707.07435v5, 2017.4. J. Chan, W. Liu, C. Leckie, J. Bailey, K. Ramamohanarao, “Discovering Latent Blockmodels in Sparse andNoisy Graphs using Non-negative Matrix Factorisation”, in Proceedings of 22nd ACM InternationalConference on Information and Knowledge Management, pp. 811-816, 2013.


Dr Jeffrey Chan ([email protected]) 1st Supervisor – Office 14.08.15 TBD 2nd Supervisor –





Project Description In optimisation, typically an expert or analyst will first model the optimisaton problem as an objective function and constraints, then select an approach solver to optimise it. The modelling and solver selection can have a large effect on the quality of the final solution and how fast this solution was found. However, good modelling and selection can be difficult, and still largely a manual affair, requiring years of experience to do well. Although optimisation problems surround us, due to the high barriers of its applications, it has been limited to experts and larger organisations.

In recent years, there has been big advances in machine learning, which essentially is about designing models that learns from data to do predictions and other useful analysis. There have been advances in meta-learning [1], which tries to optimise the parameters of the model by itself as well as learning to play games [2]. This interest in what is called “learning to learn”, has been extended to the emerging field of learning to optimise [3], where machine learning models learn from past problem formations and solving experiences, to learn the optimisation model and appropriate solver.

This field is in its infancy, and there are many open challenges to solve. This project aims to solve some of these, including:

• Problem representation, essentially how to model an optimisation problem such that we can learnproblem formulations across different optimisation problems. This might require research intoembeddings and kernels [4].

• Learning from past solving experiences, which could involved deep reinforcement learning [3] orother artificial intelligence approaches such as evolutionary computing to learn over previousproblems and solving experiences

• Studying the theoretical bounds of learning to optimise, i.e., in which problem types is this type oflearning possible.

References 1. C. Lemke, M. Budka, and B. Gabrys, “Metalearning: a survey of trends and technologies”, Artificial

Intelligence Review, 44(1), pp. 117-120, 2015. 2. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wiestra and M. Riedmiller, “Playing

Atari with Deep Reinforcement Learning”, arXiv:1312.5602, 2013. 3. K. Li and J. Malik, “Learning to Optimize”, in Proceedings of 5th International Conference on Learning

Represenation, 2017 4. A. Grover and J. Leskovec, “node2vec: Scalable Feature Learning for Networks”, in Proceedings of 22nd

ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 855-864, 2016


Dr Jeffrey Chan ([email protected]) 1st Supervisor – Office 14.08.15 A/Prof Kai Qin ([email protected]) 2nd Supervisor – Swinburne University, Data Science Institute





CSIT – City Campus

Project Description – Conversations are a natural way for humans to seek information, and there is decades of study on formal dialogs and interactions of users with reference librarians. The next step is to design automated systems that are ‘virtual assistants’, eliciting information needs, correcting misconceptions, providing the right amount of information at the right time across all possible domains. Multi-turn conversations should also become more prevalent in our digital lives due to the proliferation of devices that are accessible anytime/anywhere (without screen or keyboard), the maturity of speech recognition/synthesis approaches, and recent developments in general representation learning. Today’s digital assistants are only capable of very basic “conversations”, which usually means simple user questions (“What’s the weather like today?” or “When does my flight leave tomorrow?”), followed by a single system answer. In contrast, this research will lead to multi-turn, multi-user, multi-task and multi-domain conversational information seeking systems. How best to model and manage state is an important and open problem in Information Retrieval.

Building on extensive recent progress in dialog systems, we distinguish Conversational IR Systems from traditional search system as including capabilities such as long term user state (including tasks that may be continued or repeated with or without variation), taking into account user needs beyond topical relevance (how things are presented in addition to what is presented), and allowing initiative to be taken by either the user or the system at different points of time. As information is presented, requested or clarified by either the user or the system, the narrow channel assumption also means that CIR Systems must address issues including presenting information provenance, user trust, federation between structured and unstructured data sources and summarization of potentially long or complex answers in easily consumable units.

Potential project subtasks (depending on student interests) include machine learning models for CIR, Question Answering with structured and unstructured data, end-to-end system design and evaluation, and efficiency-effectiveness trade-offs in complex CIR systems.

References 1. Radlinski, F., & Craswell: A theoretical framework for conversational search. In Proceedings of

the 2017 Conference on Conference Human Information Interaction and Retrieval (CHIIR2017), Mar 2017, pp. 117-126.

2. R. Benham and J. S. Culpepper: Risk-Reward Trade-offs in Rank Fusion. Proceedings of the22nd Annual Australasian Document Computing Symposium (ADCS 2017), Dec 2017, pp 1.1-1.8.

3. R.-C. Chen, L. Gallagher, R. Blanco, and J. S. Culpepper. Efficient Cost-Aware Cascade Rankingin Multi-Stage Retrieval. Proceeding of the 40th Annual International Conference on Researchand Development in Information Retrieval (SIGIR 2017) , Aug 2017, pp 445-454.

Contact Details: To discuss this project further please contact: Dr Shane Culpepper ([email protected]) Supervisor – Office 14.09.21

DR221 – PhD (Computer Science)

MR221 – Master of Science (Computer Science)




Project Description The problem Recommender systems (RS) are very commonly used and vital in real-life applications, i.e.

smart shopping carts, vacation recommender and financial services. There are many different methods and

algorithms in RSs which can be applied to the fields. However, finding and evaluating proper RSs can be

performed through different methods like online, user study and offline evaluations. Additionally, the

evaluation requires proper metrics. Traditionally, accuracy has been the major metric, yet it is believed that

accuracy alone is not a sufficient evaluation metric and other metrics like coverage, diversity, scalability and

serendipity also should be taken into account.

Finding the best combination of the metrics for specific problems can be a challenging task and the research

aims at finding a robust method for this challenge.

Motivation - There already have been several researches in the field and different metrics have been

introduced and applied. Metrics and the combinations of the metrics have proven to be inconsistent in varying

applications and several changes in evaluation methods and the metrics are required in an application, over a

period of time. Hence, some applications are using constantly changing algorithms or manually handpicking

recommendations. Thus, a research is being proposed here to develop an adaptive method to overcome this

problem at once. The lack of having a consistent method and the importance of RSs, makes the proposed

research an ideal research material.

Potential Impact - Nowadays people spend an important portion of their lives in social networks and rely on

online and computer based services for a major part of their daily tasks. According to Statista, it is expected to

have about 3 billion users of social networks in 2021. According to the same source, In Australia, over 12

million users are using e-commerce services and it’s expected to reach 14.9 million by 2022. The revenue

from e-commerce amounts to 10.6 billion USD in the year 2017.

Additionally, it is a fact that RSs are an important part of the mentioned applications and having optimal RSs

will definitely have positive impact on the success of the application. Applications that will benefit from the

results are online shopping websites, social networks, travel agencies, streaming platforms and other similar

services. All stakeholders, including the owners and customers of these applications will benefit from the

improved RSs. Improved RSs will generate higher revenues for the application and will make sure the

customers will get what they need and/or what they want. In conclusion, financially and scientifically, the

research has an undeniable impact.

References

1. B. Kumar and N. Sharma, “Approaches, Issues and Challenges in Recommender Systems: A

Systematic Review,” Indian J. Sci. Technol., vol. 9, no. 47, 2016.

2. M. Taghavi, J. Bentahar, K. Bakthiyari, and C. Hanachi, “New Insights Towards Developing

Recommender Systems,” pp. 1–35, 2017.

Contact Details:


Dr. Yongli Ren ([email protected]) – Office 14.09.07

Dr. Mahdi Jalili ([email protected]) – Office 14.10.16 - 17

Prof. Falk Scholer ([email protected]) – Office 14.09.22

DR221 – PhD (Computer Science)

MR221 – Master of Science (Computer Science)


Reducing Energy Consumption in Computational and

Cyber Physical Systems

School of Science, CS & SE Discipline, City Campus


It is forecast that energy consumption will increase worldwide by 33% in the next 15 years. However, such a

trend has an adverse effect on sustainable human development and is considered one of the major challenges

of the 21st century. This project is concerned with reducing energy consumption in both computational and

cyber-physical systems that play a significant role in worldwide energy consumption especially in the

commercial and industrial sectors.

Areas of focus for reducing energy consumption in computational systems (systems that are primarily

computation or software based) that are appropriate areas for PhD research in Computer Science are:

• Offloading computation to computational resources/devices that are more energy efficient by design

or for a specific purpose

• Offloading computation to computational resources/devices that are powered by more efficient energy

resources such as renewables

• Offloading computation to computational resources closer to the required data and/or users

• Optimizing the trade-off between the benefits of computation offloading (as in the first three points

above) and versus the increased cost of data transmission over the network.

Cyber-physical systems refer to systems that in addition to a significant computational component (as

described above) also include physical machinery and artefacts that are software/computer controlled. Such

systems include industrial automation and robotics, SCADA systems, and Smart Grids. In addition to the

opportunities described above, such systems involve additional challenges:

• Which parts of a cyber physical system can be offloaded to improve computational efficiency and

therefore reduce energy consumption?

• How can industrial scheduling be optimised to reduce the overall energy cost of such systems based

on energy cost, and availability of renewable energy resources?

• How can data measuring external factors such as weather, heating, ventilation and cooling

requirements (HVAC), and real-time energy cost from the smart grid, be used to further optimize the

scheduling of industrial activities involving cyber-physical systems?

Previous related work involving the supervisor that may be of interest to prospective candidates [1-4].

References

[1] K. H. Khan, C. Ryan and E. Abebe, Day Ahead Scheduling to Optimize Industrial HVAC Energy Cost

Based ON Peak/OFF-Peak Tariff and Weather Forecasting, in IEEE Access, vol. 5, pp. 21684-21693,

2017.

[2] K. H. Khan; C. Ryan; E. Abebe, Optimizing HVAC Energy Usage in Industrial Processes by Scheduling

based on Weather Data, in IEEE Access, Vol. 5(1), 2017, DOI: 10.1109/ACCESS.2017.2715239

[3] C. Li, Y. Xu, X. Yu, C. Ryan and T. Huang, Risk-Averse Energy Trading in Multienergy Microgrids: A

Two-Stage Stochastic Game Approach, in IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp.

2620-2630, Oct. 2017. doi: 10.1109/TII.2017.2739339

[4] Abebe, E., Ryan, C., Adaptive Application Offloading Using Distributed Abstract Class Graphs in Mobile

Environments, Journal of Systems & Software (2012), Elsevier, vol 85. no. 12, pp. 2755–2769, DOI:

10.1016/j.jss.2012.05.091

Contact Details:


Dr. Caspar Ryan ([email protected]) Supervisor – Office 14.11.32



Scalable and Transferrable Occupant Behaviour Learning with Multi-Sensor Data from Multi-region Living Labs

Computer Science, City PhD Project

Project Description The focus of this PhD project will be on data-driven occupant research methods to support evidence-based decision-making. The development in machine learning and data mining methods that are generic and robust to multiple sensing modalities will enable a rich insight about occupant behaviors. This wealth of building sensor data opens new opportunities for extracting knowledge from data and data-driven modeling of occupant behavior. Among others, the data offers opportunities for creating models that are more individually customized to the particular occupant, building or climate zone. This wealth of data also creates new threats to the occupant in terms of the violation of the individuals’ right to privacy that has to be addressed.

The goal is to demonstrate how in situ data can be converted to meaningful information with key traits (e.g., occupant diversity, temporal trends, triggers, patterns and relationships). The project will investigate the potential applications of data-based methods for knowledge discovery and modeling of occupant behavior. The project will establish and evaluate new data-driven research opportunities in the light of the rich variety of models already developed in the field to understand the advantages and disadvantages of the different modeling paradigms.

The rich datasets and online repositories from the two living labs– RMIT and University of Southern Denmark – will be used by the PhD candidate. OU44, the 8500m2 3-storey building, is a highly energy efficient teaching building at the University of Southern Denmark in Odense, and a living lab for research in energy informatics and occupancy behavior, with full capability to monitor, manage and control the building operation. The building is equipped with energy efficient technologies including, ventilation units with heat recovery, LED lights, underfloor heating, PV modules, and heating, lighting and electricity consumption sub-meters, and temperature, humidity, CO2, Lux and PIR sensors on the room level. RMIT and a project on activity based working in multiple offices of Arup Melbourne and Sydney provide another living lab with multi-sensor, multi-device tracking data of occupants.

This project will extend the existing research by Dr. Salim and Prof Kjaergaard on occupant sensing, tracking, and prediction. The candidate needs to have a strong background in algorithms and data mining/machine learning. This project will contribute to the development of a new International Annex by IEA EBC (International Energy Agency's Energy in Buildings and Communities) on “Occupant behaviour-centric building design and operation”, particularly the Sub Task 2 on “data-driven methods for occupant behavior modelling”.

References [1] A.J.R. Ruiz, H. Blunck, T.S. Prentow, A. Stisen, M. B. Kjærgaard, Analysis methods forextracting knowledge from large-scale WiFi monitoring to inform building facility planning. PerCom2014, pp. 130-138[2] I.B.A-Ang, M. Hamilton, F. D. Salim, RUP: Large Room Utilisation Prediction with carbondioxide sensor, Pervasive and Mobile Computing, Volume 46, 2018, Pages 49-72.

Contact Details: To discuss this project further please contact Dr. Flora Salim [email protected] 1st Supervisor – Dr. Flora Salim, School of Science, RMIT University. 2nd Supervisor –Prof. Mikkel Kjaergaard, University of Southern Denmark



Abnormal Event Detection in Big Video Data


Project Description – Abnormal event detection is a hot research issue for many applications like video surveillance and security. It aims to automatically detect unusual events from videos. Due to the limited availability of anomaly, developing learning method based on normal video data is the main strategy to address this issue. Although many methods have been proposed, such as probabilistic models [1, 2], dictionary learning [3, 4, 5, 6], and deep learning [7, 8, 9], abnormal event detection is still challenging. For one thing, the information carried by anomalies is difficult to be captured (we aim to address it in the problems 1 and 2). For another, using probabilistic models and dictionary learning, it’s not easy to design hand-crafted features to describe the complex video data. In this project, we aim to study four research problems: 1. How to utilize the information carried by anomalies? It’s hard to find the common patterns of anomalies,since abnormal events can be caused by various reasons. Lacking of labelled anomalies leads to the loss ofanomaly information. This project aims to exploit anomalies and improve the normal event detector. Theintuition is that anomalies in the continuous frames of a video are similar, but different from normal video.2. How to propose a learning method only using the video-level label? It’s hard to describe the location ofanomalies. For most supervised learning methods, both normal and anomaly training data are required.Especially, to locate anomaly in a frame, patch-level label is demanded. Thus, supervised learning is quitelimited in some situations of abnormal event detection. In this project, we plan to design our method based onthe video-level label. Multi-instance learning model can be the option. Its bag-instance structure matchesvideo-frame data well.3. How to generate the feature representation? While multiple features have been designed for abnormal eventdetection, it is still a challenging to decide which features are more effective in anomaly detection. We plan toadopt feature fusion and selection on multi-view features to develop an effective feature representation model.4. How to developing a real-time abnormal event detection system? In practice, real-time detection meetsmore real-world tasks. This project aims to accelerate the detection from several aspects: feature extraction,decision function selection, detection over distributed environment etc.

Required Background: Algorithm, Abnormal Event Detection, Video Analysis

References [1] W. Li, V. Mahadevan, N. Vasconcelos: Anomaly Detection and Localization in Crowded Scenes. IEEETrans. Pattern Anal. Mach. Intell. 36(1): 18-32 (2014). [2] M.J., Roshtkhari, M.D., Levine: Online dominantand anomalous behavior detection in videos. CVPR 2013: 2611-2618. [3] Y. Cong, J. Yuan, J. Liu: Sparsereconstruction cost for abnormal event detection. CVPR 2011: 3449-3456. [4] C. Lu, J. Shi, J. Jia: AbnormalEvent Detection at 150 FPS in MATLAB. ICCV 2013: 2720-2727. [5] H. Wen, S. Ge, S. Chen, H. Wang, L.Sun: Abnormal event detection via adaptive cascade dictionary learning. ICIP 2015: 847-851. [6] H. Ren, W.Liu, S. I. Olsen, S. Escalera, T. B. Moeslund: Unsupervised Behavior-Specific Dictionary Learning forAbnormal Event Detection. BMVC 2015: 28.1-28.13. [7] D. Xu, E. Ricci, Y. Yan, J. Song, N. Sebe: LearningDeep Representations of Appearance and Motion for Anomalous Event Detection. BMVC 2015: 8.1-8.12.[8]Y. Feng, Y. Yuan, X. Lu: Deep Representation for Abnormal Event Detection in Crowded Scenes. ACMMultimedia 2016: 591-595. [9] H. Lin, J. D. Deng, B. J. Woodford, A. Shahi: Online Weighted Clustering forReal-time Abnormal Event Detection in Video Surveillance. ACM Multimedia 2016: 536-540.







This research project investigates novel approaches to the situation-aware social recommendation problem by fusing media content, contexts and user situations to support individuals in smart decision making. In particular, we aim to invent new theories, models, efficient indexing and query processing techniques for effective and efficient recommendation over complex and dynamic big social media streams that contain metadata and different context and user situation information. This is driven by the demands of the online global services, such as mobile smartphones and GPS, and media shared services like Youtube and Twitter, and online e-business, like Netflix, Amazon etc. This project will:

1. Build novel data model to support the high effectiveness of recommender system. Effective relevancefunctions will be learnt by analysing the data attributes.2. Determine what types of contexts, user situations and contents best suit a recommender system or a targetuser over the data streams of a particular online platform, so as to enhance the personality of the system;3. Design efficient solutions to support the efficient recommendation and dynamic update maintenance ofsystem over distributed environment like Apache Storm and Spark.




Handbook, 2011.[2] M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu: Social contextual recommendation. CIKM, 2012.[3] R. Kumar, B K Verma and S. S. Rastogi: Context-aware Social Popularity based Recommender System.

IJCA, 92(2):37-42, April 2014.[4] X. Yang, H. Steck, and Y. Liu: Circle-based recommendation in online social networks. SIGKDD, 2012.[5] Y. Huang, B. Cui, J. Jiang, K. Hong: Real-time Video Recommendation Exploration. SIGMOD 2016.[6] X. Zhou, L.Chen, Y.Zhang, etc: Enhancing online video recommendation using social user

interactions. VLDBJ. 26(5): 637-656 (2017)[7] Y. Ren, M. Tomko, F. Salim, J. Chan, M. Sanderson: A Location-Query-Browse Graph for Contextual



http://dblp.uni-trier.de/pers/hd/j/Jiang:Jie

http://dblp.uni-trier.de/pers/hd/h/Hong:Kunqian






This research project investigates novel approaches to the situation-aware social recommendation problem by fusing media content, contexts and user situations to support individuals in smart decision making. In particular, we aim to invent new theories, models, efficient indexing and query processing techniques for effective and efficient recommendation over complex and dynamic big social media streams that contain metadata and different context and user situation information. This is driven by the demands of the online global services, such as mobile smartphones and GPS, and media shared services like Youtube and Twitter, and online e-business, like Netflix, Amazon etc. This project will:

1. Build novel data model to support the high effectiveness of recommender system. Effective relevancefunctions will be learnt by analysing the data attributes.2. Determine what types of contexts, user situations and contents best suit a recommender system or a targetuser over the data streams of a particular online platform, so as to enhance the personality of the system;3. Design efficient solutions to support the efficient recommendation and dynamic update maintenance ofsystem over distributed environment like Apache Storm and Spark.




Handbook, 2011.[2] M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu: Social contextual recommendation. CIKM, 2012.[3] R. Kumar, B K Verma and S. S. Rastogi: Context-aware Social Popularity based Recommender System.

IJCA, 92(2):37-42, April 2014.[4] X. Yang, H. Steck, and Y. Liu: Circle-based recommendation in online social networks. SIGKDD, 2012.[5] Y. Huang, B. Cui, J. Jiang, K. Hong: Real-time Video Recommendation Exploration. SIGMOD 2016.[6] X. Zhou, L.Chen, Y.Zhang, etc: Enhancing online video recommendation using social user

interactions. VLDBJ. 26(5): 637-656 (2017)[7] Y. Ren, M. Tomko, F. Salim, J. Chan, M. Sanderson: A Location-Query-Browse Graph for Contextual



http://dblp.uni-trier.de/pers/hd/j/Jiang:Jie

http://dblp.uni-trier.de/pers/hd/h/Hong:Kunqian

School of Science

DR222 – PhD (Mathematical Sciences)MR222 Master of Science (Mathematical Sciences)

HDR Project 2018



Project Description

In numerical analysis measure of how much a solution is changed when the input data varies are often qualified by measures of conditioning. Depending on the problem class the condition number may be defined in numerous ways but is essential the variation of the change in output compared with the change in input. These measures have wide use in stability and sensitivity analysis.

A related but potential more general framework consist of relating the distance a given problem is from the set infeasible or ill-defined problems, in their respective problem space, to a structural measure of that problem class. This fits the format of so-called radius theorems which have the following form: given a mapping with a certain desirable property, the “radius” of this property is the “minimal perturbation” such that the perturbed mapping may lose this property. An early example in linear algebra is a radius theorem that goes back to a paper by Eckart and Young [1]: for any nonsingular matrix A, can we characterize the smallest perturbation B so that A+B is singular. This distance, measured in the norm of B, is known to be equal to the inverse of the matrix norm of the inverse of A. That is, radius = 1/regularity [4, 5].

In this project we will study radius theorem for optimization problems of various classes. We do not simply want to consider a distance to infeasibility but the distance to the loss of important structural properties of the problem. These could be the existence of a strict local minimum, a tilt stabe local minimum [3] to the failure of optimality condition [2] or to the failure of related optimization algorithms to converge. We will be in particular interested in classes of conic optimisation problems.

References

1]. Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrica 1, 211–218 (1936) [2]. A.L. Dontchev, A. Eberhard and R.T. Rockafellar, Radius Theorems for Monotone Mappings, Set Valued Analysis, (accepted 30/01/2018). [3].A. Eberhard, Y. Luo and S. Liu, On Partial Smoothness, Tilt Stability and the UV decomposition, Mathematical Programming series A., (accepted on 16/02/2018). [4]. A. Eberhard, V. Roshchina, and T. Sang, (In Press) Outer limits of subdifferentials for min-max type functions, in Optimization, Taylor and Francis, United Kingdom, pp. 1-19 ISSN: 0233-1934, 2017. [5]. Asen L. Dontchev and R.T Rockafellar Implicit Functions and Solutions Mappings, A View from Variational Analysis, second edition, Springer 2014.


Professor Anthony Eberhard (Email:[email protected]) Dr Yousong Lou (Email: [email protected])


School of Science


HDR Project 2018


Mathematical Sciences – City Project Description

This research aims to solve/compute the equilibria in smooth measurable Bayesian games, using infinite dimensional Variational Inequalities (VI), [1] in a Hilbert space and then use order theoretic-projection based methods to perform higher order distributional comparative statics/sensitivity analysis of the equilibria to the belief structures in Bayesian games.

We assume that each player’s payoff function is weakly coercive and r-concave on its own actions, which gives us a set of pseudo-monotone VIs. The VIs are solvable by constructing weakly compact subsets of possible strategy spaces for each player, including their best replies. Then, we need sufficient assumptions satisfying the conditions for applying a proper fixed-point theorem on the best reply correspondence. Related to this problem, we can also study whether the use of the so-called Noisy stochastic games approach -introduced by Duggan [2] and He & Sun [3], and recently applied in Bayesian games by Meneghel & Tourky [4], might be helpful in weakening sufficient conditions for existence in pseudo-concave Bayesian games. Here, payoff gradients have pseudo-monotonicity properties [7,8]. Given the ordinal nature of pseudo-monotonicity, we can translate the problem to one stochastic pseudo-monotone VI [5], leading to the study of infinite dimensional pseudo-monotonicity associated with Bayesian games.

References

1. T. Ui, "Bayesian Nash Equilibrium and Variational Inequalities," Journal of Mathematical Economics,pp. 139-146, 2016

2. J. Duggan, "Noisy Stochastic Games," Econometrica , 2012.3. Wei, He; Yeneng, Sun, "Stationary Markov Perfect Equilibria in Discounted Stochastic Games," Journal

of Economic Theory, 2017.4. I. Meneghel, R. Tourky, "Monotone purification in Bayesian games," 2013.5. Aswin Kannan , Uday V. Shanbhag, "The pseudomonotone stochastic variational inequality problem:

Analytical statements and stochastic extragradient schemes," in American Control Conference (ACC),Portland, OR, USA, 2014.

6. Crouzeix J-P. , Eberhard A. and Ralph D., (2010), A geometrical insight on pseudoconvexity andpseudomonotonicity, Mathematical Programming, Series B, 123, pp. 61-83.

7. Eberhard, A.C. and Crouzeix J-P (2007), Existence of Closed Graph, Maximal, Cyclic Pseudo-MonotoneRelations and Revealed Preference Theory, The Journal of Industrial and Management Optimization,Vol. 3, no. 2, pp 233—255.

Contact Details: Professor Andrew Eberhard (Email: [email protected]) Second Supervisor: To be advised.





Project Description – Changes in government funding for Home Care have necessitated providers becoming more competitive. The provision of Home Care is challenging in that some clients need to be visited by more than one carer simultaneously. Such synchronous visits have to be within a daily schedule that also requires visits to clients needing only one carer. Further challenges lie in matching client’s requirements with the carer’s capabilities. For example, some clients might require a qualified nurse to give injections while others might require a strong person to lift a patient. Matching skills with requirements within a routing and scheduling problem is not well-researched and yet is becoming of growing importance. This project will require the formulation of appropriate optimization models. These models will then have to be tested with realistic data.

References 1. “The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal

dependencies” M S Rasmussen , T Justesen, A Dohn, J Larsen, EJOR 219 (2012) 598-610.






Project Description – Changes in government funding for Home Care have necessitated providers becoming more competitive. The provision of Home Care is challenging in that some clients need to be visited by more than one carer simultaneously. Such synchronous visits have to be within a daily schedule that also requires visits to clients needing only one carer. Further challenges lie in matching client’s requirements with the carer’s capabilities. For example, some clients might require a qualified nurse to give injections while others might require a strong person to lift a patient. Matching skills with requirements within a routing and scheduling problem is not well-researched and yet is becoming of growing importance. These problems can be formulated as Mixed Integer Programming models and small instances can be solved using commercial solvers such as CPLEX and Gurobi. Unfortunately the mathematical properties of such models preclude the use of standard solution methods for large instances of the problem. In this case it is necessary to develop heuristic methods to solve the problems in times suitable for operational purposes. This project will involve the develop of these heuristics.

References 1. “The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies”

M S Rasmussen , T Justesen, A Dohn, J Larsen, EJOR 219 (2012) 598-610.






Project Description – The recent scandal featuring Facebook and Cambridge Analytica highlights the use and misuse of social media to influence elections. The ever increasing use of social networks by individuals and companies, and the harvesting of such information, indicates that there are a number of questions that need answering in this context. One such question is to understand the connections that can be deduced from the information uploaded by a user, whether it is in the form of likes or comments, thumbs-up or down, tweets or re-tweets.

The study of the effectiveness of election campaigning has been increasing, with a large number of papers (for example [1, 2]) measuring effectiveness using the bare count of likes, re-tweets etc. In [3], the analysis of this sort of bare count was strengthened, using a statistical tool from the risk analysis of heart surgery [4], to gain an understanding of the interaction between posting and the likes gathered. While the research showed that being very prolific in posting does not necessarily lead to popularity in social media or, for that matter, in actually winning an election, it allowed for a more nuanced understanding.

This project aims to use tools from complex networks and statistics to further the understanding of connections between the people posting and the ones liking, or retweeting. The project will look for tools outside of the area of data analysis to do this exploration.

The candidate should have a background in mathematics, specifically in complex networks and statistics. The candidate should also be familiar with programming using R, and will be expected to obtain publicly available data.

References 1. Caldarelli G, Chessa A, Pammolli F, Pompa G, Puliga M. A Multi-Level Geographical Study of Italian

Political Elections from Twitter Data. PLoS ONE 2014; 9(5):e95809.https://doi.org/10.1371/journal.pone.0095809 PMID: 24802857

2. Giglietto Fabio. If Likes Were Votes: An Empirical Study on the 2011 Italian Administrative Elections.In:Sixth International AAAI Conference on Weblogs and Social Media 2012; Trinity College, Dublin,Ireland;AAAI Press.

3. Khairuddin MA, Rao A (2017), Significance of likes: Analysing passive interactions on Facebook duringcampaigning. PLoS ONE 12(6): e0179435. https://doi.org/10.1371/journal.pone.0179435

4. Novick Richard J, Fox Stephanie A, Stitt Larry W, Forbes Thomas L, Steiner Stefan. Direct comparison ofrisk-adjusted and non±risk-adjusted CUSUM analyses of coronary artery bypass surgery outcomes. TheJournal of Thoracic and Cardiovascular Surgery 2006; 132(2):386-391. https://doi.org/10.1016/j.jtcvs.2006.02.053 PMID: 16872967


Prof Asha Rao ([email protected]) Jessica Liebig ([email protected])





Project Description – Location and routing problems are very widely studied in supply chain management (SCM) literature with the aim of minimising costs of satisfying demands. Drexl and Schnedier (2015) surveys over a hundred papers in that field. Unfortunately, similar problems in the public domain dealing with location of permanent or temporary healthcare, shelter, or relief facilities and routing of emergency vehicles are not studied as widely. We have seen a recent increase in the frequency and magnitude of emergencies caused by natural disasters from earthquakes, hurricanes, wildfires to floods. On top of that, the ever-growing scale of events bringing people together, i.e. Olympics, World Cup, etc. where they are exposed to emergencies caused by overcrowdedness, poor preparedness or terrorism, increases the impact of such emergencies. Abounacer et al. (2014) considered the solution of a three-objective location and transportation problem for disaster response. Rath and Gutjahr (2014) developed an evolutionary math-heuristic for locating warehouse and routing relief efforts after a disaster. Wang et al (2014) developed a multi-objective location/routing model for post-earthquake relief distribution. Rodríguez-Espíndola et al (2017) work on collaborative preparation efforts for floods with a case study in Mexico. Despite these very recent efforts, this field deserves significantly more effort to handle various types of disasters and planning and preparation for large scale events. So there clear is a need for bridging the gap in knowledge for better informing the planning and preparation for such emergencies. In this project we aim to develop models, algorithms and tools to tackle emergency facility and vehicle routing problems.In this project we aim; to implement/translate the SCM based location/routing models into the emergency facility location and vehicle routing problems and identify areas of application, solve these newly developed models that will minimise the impact of such emergencies using commercial solvers, design new algorithms to solve these problems faster compared to the commercial solvers, to package these models and algorithms into open source tools that can be used by planners all around the globe to have a real practical impact.In order to achieve our goals, we will; complete an extensive survey of the literature of existing approaches in SCM and the limited literature on emergency facility location and routing problem identify problem domains in emergency facility location that has the best potential to improve the current approaches and significantly improve the outcomes, formulate models using SCM literature and extensions to cover the emergencies and their underlying causes, develop exact and approximate algorithms to solve the models developed in practical time frames with sufficient precision, disseminate results in terms of papers, conference talks, and open source tool set that can be utilised by decision makers globally.

References 1. Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective

location–transportation problem for disaster response. Computers & Operations Research, 41, 83-93.2. Drexl, M., & Schneider, M. (2015). A survey of variants and extensions of the location-routing

problem. European Journal of Operational Research, 241(2), 283-308.3. Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in

disaster relief. Computers & Operations Research, 42, 25-39.4. Rodríguez-Espíndola, O., Albores, P., & Brewster, C. (2017). Disaster preparedness in humanitarian

logistics: A collaborative approach for resource management in floods. European Journal ofOperational Research.

5. Wang, H., Du, L., & Ma, S. (2014). Multi-objective open location-routing model with split deliveryfor optimized relief distribution in post-earthquake. Transportation Research Part E: Logistics andTransportation Review, 69, 160-179.






Project Description – In this project we focus on Multi-Objective Mixed Integer Programming (MOMIP) problem with the specific aim of developing algorithms to generate the trade-off curve and to optimise a utility function over it. We are hoping to transform these algorithms into a state-of-the-art computational tool that can then be used to make economic, environmental, safe and sustainable decisions. Current studies on multi-objective programming problems focus on either pure integer programming (MOIP) or pure linear programming (MOLP). MOIP problems involve decisions represented either by integer or binary (yes/no) variables mostly coming from the domain of combinatorial optimisation. However, MOLP problems do not have any integer/binary variables, they only have continuous decision variables. Based on this important distinction, algorithms and methods that deal with these problems are significantly different. Approaches developed for MOIP mostly take advantage of the combinatorial nature of the underlying problem [12] and/or well defined increments of the individual objective functions [7, 8, 10]. However, any solution of the MOLP problem lies on the boundary of the convex hull of extreme supported solutions, and focus is on identifying this set of solutions [2, 3, 4, 5, 6]. Although there are studies dealing with MOIP and MOLP problems independently, there is a gap in the literature for studies dealing with their hybrid, MOMIP problem. Methods developed for MOIP cannot directly be used on MOMIP as it generally lacks the purely combinatorial nature due to the existence of continuous variables. Similarly, methods developed for MOLP cannot be utilised as MOMIP has solutions that are not on the boundary of the convex hull of extreme supported solutions but rather lie inside the boundary which are classified as unsupported solutions. Within this project we are aiming; to develop new algorithms for generating the trade-off curve for MOMIP problem, to develop new algorithms for optimising a utility function over the efficient set of MOMIP problem, implement new algorithms on parallel architectures, transform algorithms into a computational tool. In order to achieve these aims we will; develop new algorithms based on existing MOLP & MOIP approaches, develop novel algorithms based on computational geometry, and global optimisation, parallelise developed algorithms to take advantage of modern computing architectures, and develop a computational tool.

References

[1] H. P. Benson, 1998a, ”Further analysis of an outcome set-based algorithm for multiple-objective linearprogramming”, Journal of Optimization Theory Applications 97(1):1-10.[2] H. P. Benson, 1998b, “Hybrid approach for solving multiple-objective linear programs in outcome space”,Journal Optimization Theory and Applications 98(1):17-35.[3] H. P. Benson., 1998c, “An outer approximation algorithm for generating all efficient extreme points in theoutcome set of a multiple objective linear programming problem”, Journal of Global Optimization 13(1):1-24.[4] H. P. Benson and E. Sun, 2000, “Outcome space partition of the weight set in multiobjective linearprogramming”, Journal of Optimization Theory and Applications 105(1):17-36.[5] H. P. Benson and E. Sun, 2002, “A weight set decomposition algorithm for finding all efficient extremepoints in the outcome set of a multiple objective linear program”, European Journal of Operational Research139(1):26-41.[6] B.A. Burton and M. Ozlen., 2010, “Projective geometry and the outer approximation algorithm formultiobjective linear programming”, arXiv:1006.3085.[7] M. Laumanns, L. Thiele, and E. Zitzler, 2006, “An efficient, adaptive parameter variation scheme formetaheuristics based on the epsilon-constraint method”, European Journal of Operational Research169(3):932-942.[8] M. Ozlen and M. Azizoğlu, 2009. “Multi-objective integer programming: a general approach forgenerating all non-dominated solutions”, European Journal of Operational Research 199(1):25-35.



[9] M. Ozlen, M. Azizoğlu and B.A. Burton, 2013, “Optimising a Nonlinear Utility Function in Multi-Objective Integer Programming”, Journal of Global Optimization, 56 (1), 93-102.

[10] M. Ozlen, B.A. Burton and C.A.G. MacRae, 2013, “Multi-Objective Integer Programming: An ImprovedRecursive Algorithm”, Journal of Optimization Theory and Applications. doi:10.1007/s10957-013-0364-y.

[11] O. Ozpeynirci and M. Koksalan, 2010, “An exact algorithm for finding extreme supported nondominatedpoints of multiobjective mixed integer programs”, Management Science 56(12):2302-2315.

[12] A. Przybylski, X. Gandibleux, and M. Ehrgott, 2010a, “A two phase method for multi-objective integerprogramming and its application to the assignment problem with three objectives”, Discrete Optimization7(3): 149-165.

[13] A. Przybylski, X. Gandibleux, and M. Ehrgott, 2010a, “A recursive algorithm for finding allnondominated extreme points in the outcome set of a multiobjective integer programme”, INFORMS Journalon Computing 22(3):371-386.


DR221 – PhD (Mathematical Sciences) MR222 –Master of Science (Mathematical Sciences)


Frictional Pebble Games in Graphs and Applications Mathematical Sciences – City

Project Description

This project relates the general topic of analysing complex systems. Rigidity percolation is originally motivated by the problem of identifying independent rigid structures in physical networks dominated by central forces (assemblies of frictionless particles, structural networks, protein folding). It is related to the number of independent structural degrees of freedom in the system and harks back to the seminal work of Maxwell [1]. The fundamental nature of this constraint problem means it is also closely related to the graph reconstruction problem, with applications in particular in sensor networks and autonomous vehicle routing, mainly for localising devices based on their relative distances (see, e.g., [3]). Whilst zero-energy perturbation methods can solve it in theory, in practice the continuous nature of the rigidity percolation problem prevents the reliable identification of rigid and floppy structures for large systems [4]. In contrast, the theorem of Laman [2] gives a purely combinatorial rigidity criteria using graph theory. However, it results in an algorithm that scales exponentially with the number n of vertices (particles). The development of the pebble game in graphs by Jacobs and Hendrickson [4] led to an O(n2) algorithm for 2D assemblies with 2 degrees of freedom and 3 trivial rigid body motions. It is difficult to overstate the impact of this algorithm, which has identified new universality classes of critical phenomena, and has rendered tractable entire classes of problems in many fields, including materials science, condensed matter physics, biophysics but also sensor networks and cyber security. However, despite these advances, rigidity percolation for the important class of friction-dominated materials like granular assemblies is poorly understood. Sliding friction constraints and additional degr es of freedom make it much more challenging. Whilst Lee and Serienu [5] have identified generic (k,l)-pebble game algorithms for such broader classes of problems, a major outstanding barrier is the lack of an equivalent of Laman’s theorem for frictional contacts. However there exist strong indications that frictional rigidity percolation is governed by a combination of (k,l)-pebble games [6], which motivates the development of a frictional pebble game. In this thesis, we propose to undertake fundamental research into (k,l)-pebble game algorithms on graphs to better solve the frictional rigidity percolation problem. The huge impact of the regular pebble game in various topics motivates us investigating the potential of this generalisation for other applications, in particular for sensor networks, and how these topics could learn from each other using this abstraction.

References [1] James Clerk Maxwell F.R.S. Letter on the calculation of the equilibrium and stiffness of frames.Philosophical Magazine, 27(182): 294-299 (1864)[2] G. Laman. On graphs and rigidity of plane skeletal structures. Journal of Engineering Mathematics, 4(4):331-340, (1970)[3] Anthony Man-Cho So and Yinyu Ye, Theory of semidefinite programming for Sensor NetworkLocalization. Mathematical Programming (B) 109(2), 367-384 (2007).[4] Donald J. Jacobs and Bruce Hendrickson, An algorithm for two-dimensional rigidity percolation: Thepebble game, Journal of Computational Physics, 137(2): 346-365 (1997)[5] Audrey Lee and Ileana Streinu. Pebble game algorithms and sparse graphs, Discrete Mathematics, 308(8):1425-1437 (2008).[6] Daniel Lester and Ruru Li, The frictional pebble game: an algorithm for rigidity percolation in saturatedfrictional assemblies, Journal of Computational Physics (2017)

Contact Details: To discuss this project further please contact: Associate Professor Marc Demange (Email:[email protected]) Dr Daniel Lester (Email:[email protected])

School of Science


HDR Project 2018


Mathematical Sciences – City Project Description

Circle graphs (also known as overlap graphs) [6] are the intersection graphs of chords of a circle. They can be equivalently defined by a set of intervals of the real line. We say that two intervals overlap if they have a non-empty intersection but none is contained in the other. In an overlap representation, vertices are the intervals and edges represent overlap of two intervals. This class of graphs appears notably in some logistics problems and in particular stacking problems (see, e.g., [1], [12]). After unloading a ship, containers are stored in the port before being uploaded again on another ship or truck. Due to space and logistics constraints, stored containers are stacked such that the container needing to leave first is preferably on the top of a stack. Minimising the space used for storing corresponds to colouring the vertices of the related graph such that two adjacent vertices are of different colours and the total number of colours is minimum. Colouring overlap graphs has also applications in VLSI design and for the book-embedding problem of a general graph [10]. Natural generalizations include, in particular, sub-tree filament graphs [7]. Colouring overlap graphs with a minimum number of colours is known as NP-hard [10] and our project is essentially motivated by the question of finding efficient algorithms for colouring such graphs and their generalisations. We consider in particular the possibility to approximate this problem with guaranteed approximation ratio but also the online setting where intervals are revealed over the time and one needs to assign colours when intervals are revealed and without any knowledge about the next intervals. This set-up is very natural in stacking problems. Few results are known about approximation and online competitive algorithms for colouring overlap graphs [3, 4,5,8,9]. The structure of overlap graphs is still only very partially understood [6], and recent results (see, e.g., [5]) show how structural results can be turned into efficient algorithms. This project aims to investigate in a more systematic way the structure of overlap graphs and in particular hereditary subclasses and use it for the design of new efficient algorithms for generalised overlap graph colouring problems. We will consider both an offline set-up (all intervals are known in advance) and online one. We plan to consider new domains of applications that will motivate variants of our problems.

References 1. M. Avriel, M. Penn, and N. Shpirer. Container ship stowage problem:

complexity and connection to the coloring of circle graphs. Discrete Applied Mathematics , 103(1-3): 271-279, 2000.

2. J. Cerny. Coloring circle graphs. Electronic Notes in Discrete Mathematics 29:457-461, 2007.3. S. Cornelsen and G. Di Stefano. Track assignment. Journal of Discrete Algorithms, 5(2):250-261, 2007.4. M. Demange, G. Di Stefano, and B. Leroy-Beaulieu. On the online track assignment problem. Discrete

Applied Mathematics, 160(7-8): 1072-1093, 2012.5. M. Demange and M. Olsen. A Note on Online Colouring Problems in Overlap Graphs and

TheirComplements. In: Rahman M., Sung WK., Uehara R. (eds) WALCOM: Algorithms and Computation.WALCOM 2018. Lecture Notes in Computer Science 10755: 144-155, 2018, Springer, Cham

6. M.C. Golumbic. Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics 57). North-Holland Publishing Co., Amsterdam, The Netherlands, 2004.

7. J.M. Keil and L. Stewart. Approximating the minimum clique cover and other hard problems in subtreefilament graphs. Discrete Applied Mathematics, 154(14): 1983-1995, 2006.

8. F. G. Konig and M. E. Lubbecke. Sorting with complete networks of stacks. In ISAAC 2008, Proceedings:895-906, Berlin, Heidelberg, 2008. Springer Berlin Heidelberg.

9. F. Shahrokhi. A new upper bound for the clique cover number with applications.arXiv preprint arXiv:1502.06168 , 2015.

10. W. Unger. On the k-colouring of circle-graphs. In STACS 88, Proceedings: 61-72, Berlin, Heidelberg, 1988.Springer Berlin Heidelberg.

Contact Details: Associate Professor Marc Demange (Email: [email protected]) Dr Martin Olsen (Email: martino.btech.au.dk) Dr Graham Clarke (Email: [email protected])

School of Science


HDR Project 2018

Modelling of anaerobic digester reactor incorporating delays Mathematical Sciences – City

Project Description

The aim of this project is to mathematically model methane production from sewage, vegetable or animal wastes in an anaerobic reactor, promoting sustainability by replacing fossil fuel generated methane by the more sustainable methane from wastes. The mathematical modelling of such reactor dynamics is complex and can involve large systems of nonlinear differential equations whose analysis can require sophisticated numerical processes - see [1], which involves 32 state variables. A greatly simplified model [2] involves 4 state variables and this reduced complexity allows some general analytical analysis of the behaviour of the reactor. Finally, a greatly simplified model based on [3] reduces the number of state variables to 2 - two species of bacteria: the acidogenic bacteria which consume the initial complex solids substrates to produce volatile fatty acids and the methanogenic bacteria which feed on these volatile fatty acids to produce methane - but preserves the nonlinearity of the species' interaction and much of the complexity of the reactor's behaviour. In all the models described above, values of the state variables are determined instantaneously in time. However, it is possible that such values can involve delays, so that variable values draw on information from some set previous time. Moreover, the delays and the defining model parameters themselves may in fact change with time. With these two modifications, the simplified two species model described above can be expected to display very complex behaviour, with possible significant instabilities.indeed. Thus, this project aims to use analytic methods to study the behaviour of the components of this nonlinear two species model when it involves varying delays and varying model parameters. Some preliminary work [4] by the project proposers involving a constant delay has proved to be encouraging.

References

1. D.J.Batstone, J.Keller, R.I.Angelidaki,S.V. Kalyuzhny, S.G. Pavlostathis, A.Rozzi, et al. The IWAanaerobic digestion model No 1 (ADM1). Water Science and Technology, 45(10), 6573,(2002)

2. D.T. Hill, Simplified Monod kinetics of methane fermentation of animal wastes. Agricultural wastes, 5.1(1983): 1-16

3. J. F. Andrews. A mathematical model for the continuous culture of microorganisms utilizing inhibitorysubstrates, Biotechnology and Bioengineering, 10(6),pp. 707-723, (1968).

4. Khan, A. Shepherd, J. and Stacey, A. 2016, 'Numerical modelling of anaerobic digestion reactordynamics with a delay', in ANZIAM Journal, Cambridge University Press, United Kingdom, vol. 57, pp.142-159 ISSN: 1446-8735

Contact Details: Associate Professor John J Shepherd (Email: [email protected]) Dr Andrew Stacey (Email: [email protected]) Dr Ashfaq Khan (Email: [email protected])




Mathematical Science Discipline/ Applied Statistics Research Group -City

Project Description – The deaths of a new-born or a pregnant woman, during, at and after delivery continues to be a serious and sensitive issue to every country in the world. The aim of this project is to examine the application of multivariate quality control and quality assurance procedures to identify and monitor the key performance indicators responsible for neonatal and maternal mortality. This will provide timely out of control signals to the condition of the foetus or mother for the medical intervention to ensure safe delivery. The project will assess the performance of antenatal care system from both administration and data recording and analysis aspects. Traditionally, multivariate quality control monitoring have been applied in manufacturing areas, however, only in the recent years researches have been discussing the possibility of implementing the Statistical process control systems into medical area. Therefore the project is highly demanded and yet very new in the field of medical investigation. The finding can easily be applied into other medical area including the performance analysis of patients with Heart problems and Diabetes.

In the medical area often the wellbeing of the patients depends on several correlated key performance indicators and can only be modelled, monitored and assessed through multivariate quality control techniques including development of the appropriate multivariate quality control charts, multivariate capability index, multivariate fault diagnostics and multivariate profile monitoring charts.

References 1. Veena SR, Gale CR, Krishnaveni GV, Kehoe SH, Srinivasan K, Fall CH. Association between

maternal nutritional status in pregnancy and offspring cognitive function during childhood andadolescence: a systematic review. BMC Pregnancy and Childbirth 2016, 16(1):220.

2. A. Huang, K. Wu, W. Zhao, H. Hu, Q. Yang, and D. Chen, "Attendance at prenatal care andadverse birth outcomes in China: A follow-up study based on Maternal and Newborn's HealthMonitoring System," Midwifery, vol. 57, pp. 26-31, 2018.

3. WHO U, UNFPA, World Bank Groun and the United Nations Population Division: MeauringMaternal Mortality, key facts. In: Geneva Foundation for Medical Education and Research. vol.Fact sheet No 348. Geneva 27, Switzerland: World Heath Organization (WHO); 2016: 20.

4. Chou D, Tuncalp O, Firoz T, Barreix M, Filippi V, von Dadelszen P, van den Broek N, Cecatti JG,Say L, Maternal Morbidity Working G: Constructing maternal morbidity - towards a standardtool to measure and monitor maternal health beyond mortality. BMC pregnancy and childbirth2016, 16:45.

Contact Details: To discuss this project further please contact: 1st supervisor :Dr Mali Abdollahian ([email protected]) Supervisor – Office +61 3 99252248 2nd supervisor: Dr Yan Wang ([email protected]) Supervisor – Office +61 3 99252381





Improving the process performance through Fault diagnosis in Multivariate process monitoring and performance analysis for high

dimensional data Mathematical Sciences – City

Project Description

The quality and performance of a product or service is assessed based on a large number of correlated key performance indicators KPIs (high dimensional data). Multivariate quality control charts and multivariate capability indices are capable of such monitoring. Multivariate performance analysis will play a greater role in monitoring and improving multivariate processes and services. One of the hurdles in designing optimal multivariate control charts/capability indices for large dimensional data is the enormous computing resources and time that is required by the simulation algorithm to estimate the charts parameters/ capability indices. This project aims to develop novel algorithms to improve the ability of the Multivariate charts and capability indices in assessing the performance of systems with large dimensional correlated KPIs in a computationally efficient way.

One of the main challenges in deploying multivariate control charts is to identify which KPIs are responsible for the out-of-control signals detected by the charts and what is the extent of their contribution to the signals. In assessing the capability of the processes with high dimensional data the current research is only capable of identifying whether the KPIs are falling within the specified market tolerance limits by estimating the multivariate capability indices. However, the current research is unable to carry out the task of source identification and ranking of responsible KPIs with respect to the shift in process mean, spread and yield. The project aims to develop smart diagnostic techniques based on both statistical and machine learning techniques to effectively identify the variables that are responsible for the process being unable to meet the market expectations and to classify the percentage of their contribution to the faults. The proposed techniques can be applied in monitoring and assessing the performance of industrial products, clinical processes and service industries.

References

1. Nadeera Gunaratne, Malihe Abdollahian, Shamsul Huda & John Yearwood ”Exponentially weightedcontrol charts to monitor multivariate process variability for high dimensions” International Journal ofProduction Research, 2017 , http://dx.doi.org/10.1080/00207543.2016.1278081

2. Shamsul Huda, Mali Abdollahian, Musa Mammadov, John Yearwood, Shafiq Ahmed, Ibrahim Sultan “Ahybrid Wrapper-Filter approach to detect the source(s) of out-of-control signals in MultivariateManufacturing Process” European Journal of Operational Research, 2014,http://dx.doi.org/10.1016/j.ejor.2014.02.032

Contact Details:


Dr Mali Abdollahian Email: [email protected] Dr Vural Aksakalli Email: [email protected] Dr Yan Wang Email: [email protected]

http://dx.doi.org/10.1080/00207543.2016.1278081




Dissimilarity Vectors as a Paradigm for Biometric Template Protection Mathematical Science – City Campus

Project Description – Biometric Template Protection is a research area that has been important since the large scale advent of biometric authentication in areas as diverse as border security to health management to financial services. The use of biometric templates as tokens in an authentication scenario brings security implications if biometric template comparison is done when the templates are unencrypted. To solve this issue, several approaches to template protection have been proposed and broadly classified into non invertible transforms, salting and biometric cryptosystems[1]. However a solution that can be adopted on commercial systems has not been developed as often protected templates cause a drop in accuracy.

The dissimilarity representation is a technique to represent an object or image as a vector of dissimilarities from a chosen set of prototype objects or images, first proposed by Pekalska and Duin[2]. It has previously been used as a classification technique for fingerprint templates by Riesen and Bunke[3] and for offline signature verification by Batista et al[4] and Eskander et al[5]

The feasibility of the use of dissimilarity vectors as a non-invertible transform approach to create a template protected biometric system was initially proposed by Arakala et al[6]. This project will aim to build on the exploratory study by Arakala et al[] to develop a high accuracy and high security template protection system using the dissimilarity vector paradigm. It will require testing on several types of biometric modalities using publicly available data and rigorous security analysis to answer the following questions:

1. How does the dissimilarity vector based system compare to existing implementations of template protectedbiometric systems in accuracy and security?

2. What kinds of biometric template representations can be protected using a dissimilarity vector basedsystem?

3. What is the tradeoff between information leakage and security in the system?4. What are the different approaches to compromise the system and how can they be quantified?5. Can the system be used across multiple instances and are these template instances unlinkable?

The student will need to be skilled in writing software algorithms and have excellent mathematical skills. A background in information security will be highly valued.


Science and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI 48824,USA.

2. E. Pekalska[ and R. P. W. Duin, The Dissimilarity Representation for Pattern Recognition- Foundations andApplications, ser. Series in Machine Perception and Artificial Intelligence. World Scientific, 2005.

3. K. Riesen and H. Bunke, “Approximate graph edit distance computation by means of bipartite graphmatching,” Image and Vision Computing, vol. 27, no. 7, pp. 950–959, 2009.

4. L. Batista, E. Granger, and R. Sabourin, “Applying dissimilarity


Dr Arathi Arakala ([email protected]) Dr Stephen Davis ([email protected]) Prof Kathy Horadam ([email protected])




Mathematical Sciences Discipline / Statistics Research Group – City Campus

Project Description – As a renewable energy source, solar radiation is attracting the attention of researchers. Estimation of the amount of solar radiation reaching the ground in predefined time intervals is required for all kinds of planning activities around solar radiation. For example, to synchronize solar electricity generators with microgrids, we need to estimate the amount of solar radiation in very short time intervals. For a solar farm investment, it would be of interest to forecast the yearly amount of solar radiation at a certain location. Due to the cost of taking solar radiation measurements over an area of interest, use of readily available predictors like meteorological or terrestrial covariates is focused on. Although there are a lot of models that have been proposed to achieve this aim, most of the available models are developed for restricted locations over the globe and generalizability of the models is questionable.

In this project, proposed models for the estimation of solar radiation in the literature will be explored, they will be compared to each other in terms of accuracy over a real dataset, and a new statistical methodology will be focused on for the estimation of solar radiation.

A strong knowledge of R and statistical modelling are required for this research. Knowledge of machine learning techniques and their computer implementation is highly desirable.

References 1. Baser, F., Demirhan, H., 2017, A fuzzy regression with support vector machine approach to the

estimation of horizontal global solar radiation, Energy, 123, 229-204.2. Demirhan, H., Atilgan Kayhan, Y., 2015, New Horizontal Global Solar Radiation Estimation

Models for Turkey based on Robust Coplot Supported Genetic Programming Technique, EnergyConversion and Management, 106, 1013-1023.

3. Demirhan, H., 2014, The problem of multicollinearity in horizontal solar radiation estimationmodels and a new model for Turkey, Energy Conversion and Management, 84, 334-345.

4. Demirhan, H., Menteş, T., Atilla, M., 2013, Statistical comparison of global solar radiationestimation models over Turkey, Energy Conversion, and Management, 68, 141-148.

Contact Details: To discuss this project further please contact: Dr Haydar Demirhan ([email protected]) Supervisor – Office 8.9.83

School of Science


HDR Project 2018



Project Description

Motivation

Arterial wall compliance (i.e. deformation during a cardiac cycle) is essential to the proper function of the cardiovascular system. It is recognised that the computational modelling of arterial wall compliance can introduce difficulties, as it involves a strong coupling between blood flow and the arterial wall structure (termed fluid-structure interaction). In attempt to bypass this difficulty, many studies of the cardiovascular system have often assumed arterial wall compliance to be negligible. However, whilst the influence of wall compliance on the transport of blood flow has been well studied, its influence on the transport of blood-borne species is not yet fully understood.

Scope

In this research project, the student will develop computational models of compliant and non-compliant arterial walls for a variety of arteries within the cardiovascular system. The influence of wall compliance will then be investigated on the transport of blood-borne species, such as low density lipoproteins (LDL), which are critical to the development of atherosclerosis (a precursor of leading cardiovascular diseases). It is expected that the outcomes of this study will be used to inform future studies on the permissibility of the non-compliant wall assumption.


Expected outcomes


Keywords


References

[1] Roustaei, M., et al. (2018), J. Biomech., 67, p114. DOI: 10.1016/j.jbiomech.2017.11.029[2] Chung, S., et al. (2012), J. Biomech. 45, p371. DOI: 10.1016/j.jbiomech.2011.10.002[3] Lantz, J., et al. (2011), Int. J. Appl. Mech. 3, p759. DOI: 10.1142/S1758825111001226[4] Koshiba, N., et al. (2007), J. Biomech. Eng. 129, p374. DOI: 10.1115/1.2720914




School of Science


HDR Project 2018



Project Description

Motivation

Pulsatility is essential for the transport of blood flow and blood-borne species throughout the cardiovascular system. However, when modelling blood flow throughout the cardiovascular system, the action of pulsatility introduces difficulties associated with the temporal nature of the flow. This is particularly the case for the transport of blood-borne species, such as low density lipoproteins (LDL), which are critical to the development of atherosclerosis (a precursor of leading cardiovascular diseases).

Scope

In past studies, the research group has developed a computational model for evaluating the transport of blood-borne species in pulsatile blood flow. The model was validated within a coronary artery, and was shown to be superior to approximations made with a steady flow model. In this research project, the student will use the model to study the transport of blood-borne species in arteries larger than that of a coronary, such as the carotid arteries and aorta, where the influence of blood flow pulsatility is more significant. If the current model is found to require further development, the student will be given the opportunity to make these developments.


Expected outcomes


Keywords


References

[1] De Nisco, G., et al. (2018), J. Biomech. 68, p33. DOI: 10.1016/j.jbiomech.2017.12.009[2] Li, X., et al. (2017), J. R. Soc. Interface 14, p1. DOI: 10.1098/rsif.2017.0140[3] Liu, X., et al. (2011), J. Biomech. 44, p1123. DOI: 10.1016/j.jbiomech.2011.01.024




School of Science


HDR Project 2018



Project Description

Motivation

Disturbances in blood flow are recognised to be influential in the genesis of cardiovascular diseases, such as atherosclerosis. At the present, there are many haemodynamic measures, such as the Oscillatory Shear Index (OSI) and Transverse Wall Shear Stress (transWSS), that can quantify various characteristics of flow disturbances. However, the haemodynamic significance of these measures is still not fully understood.

Scope

In this research project, the student will use industry-leading computational tools to study the emergence of flow disturbances in major arteries of the cardiovascular system. An assessment of existing haemodynamic measures will be made, and these will be related to the localisation of atherosclerosis. If existing haemodynamic measures are found to be lacking, the student will also be given the opportunity to develop novel haemodynamic measures, or build upon existing ones.


Expected outcomes


Keywords


References

[1] Gabriel, S.A., et al. (2017), J. Theor. Biol. 430, p195. DOI: 10.1016/j.jtbi.2017.07.008[2] Peiffer, V., et al. (2013), J. Biomech. 46, p2651. DOI: 10.1016/j.jbiomech.2013.08.003[3] He, X., et al. (1996), J. Biomech. Eng. 118, p74. DOI: 10.1115/1.2795948




School of Science


HDR Project 2018



Project Description

Motivation

Atherosclerosis is a degenerative disease of the arterial wall, and is a precursor of leading cardiovascular diseases. Governing the growth of atherosclerosis, is an inflammatory process involving multiple biochemical species. The inflammatory process occurs within the arterial wall, below the endothelium, where low density lipoprotein (LDL) particles aggregate, and macrophages (type of white blood cells) are recruited to consume them. Following a cascade of processes, involving the accumulation of macrophages that have taken up oxidized LDL, a lesion forms from the net mass aggregate. This lesion can be problematic to its host artery, as it can critically impede blood flow from reaching downstream tissue, causing starvation and even death of the tissue. If this is to occur in the arteries supplying the heart, the result is called a heart attack. If this is to occur in the arteries supplying the brain, the result is called a stroke. Both these diseases represent the leading causes of death worldwide.

Scope

In this research project, the student will develop a mathematical model of the inflammatory process governing atherosclerosis. This will involve the construction of a system of differential equations that describe the spatial and temporal evolution of the biochemical species present in the inflammatory process. The student will then be given the opportunity to develop the solutions of this system of equations, and compare these to numerical solutions from equivalent computational models. It is expected that the outcomes of this study will be used to better understand the inflammatory process governing atherosclerosis.


Expected outcomes


Keywords


References

1. Thon, M.P., et al. (2018), Bull. Math. Biol. 80, p175. DOI: 10.1007/s11538-017-0367-12. Chalmers, A.D., et al. (2015), Math. Biol. 71, p1451. DOI: 10.1007/s00285-015-0864-53. Friedman, A., et al. (2015), Bull. Math. Biol. 77, p758. DOI: 10.1007/s11538-014-0010-3








Project Description

Most statistical tests rest upon the normality assumption and this assumption needs to be checked for parametric statistical procedures, because their validity heavily depends on it. Normality checks on the sample are done by using visual inspection (i.e. histogram, boxplot, probability-probability plot and the quantile quantile plots) and through the normality tests. There are many normality tests proposed so far in the literature. D'Agostino and Stephens [1] provided a detailed description of these normality tests. Extensive Monte-Carlo simulation studies on the Type I error and power comparisons of these normality tests are also given in [2–9]. According to these studies, most of the normality tests do not perform well and have low powers for symmetric short-tailed and symmetric long-tailed distributions, because the mentioned tests are not able to distinguish between normal distribution and any other symmetric distributions (i.e. t -distribution, double exponential, and logistic distribution).

The goal of this project is to study on the adaptation of normality tests for the symmetrical short-tailed and symmetrical long-tailed distributions which will improve the power performance of these normality tests.

Background in computational statistics is a must. The candidate should also be familiar with programming in R, Python or similar.

References

[1] D’Agostino, R. B. and Stephens, M. A.1986. Goodness-of-fit Techniques, NewYork: Marcel Dekker.[2] Shapiro, S. S., Wilk, M. B. and Chen, H. J.1968. A comparative study of various test for normality. J.Amer. Statist. Assoc., 63(324): 1343–1372.[3] Pearson, E. S., D'Agostino, R. B. and Bowman, K. O. 1977. Test for departure from normality:Comparison of powers. Biometrika, 64(2): 231–246.[4] Wong, W. K. and Sim, C. H. 2000. Goodness-of-fit based on empirical characteristic function. J. Statist.Comput. Simul., 65: 243–269.[5] Keskin, S. 2006. Comparison of several univariate normality tests regarding Type I error rate and power ofthe test in simulation based on small samples. J. Appl. Sci. Res., 2(5): 296–300.[6] Oztuna, D., Elhan, A. H. and Tuccar, E.2006. Investigation of four different normality tests in terms ofType I error rate and power under different distributions. Turk. J. Med. Sci., 36(3): 171–176.[7] Farrell, P. J. and Rogers-Stewart, K. 2006. Comprehensive study of tests for normality and symmetry:Extending the Spiegelhalter test. J. Statist. Comput. Simul., 76(9): 803–816.[8] Thadewald, T. and Buning, H. 2007. Jarque–Bera test and its competitors for testing normality – a powercomparison. J. Appl. Statist., 34(1): 87–105.[9] Yazici, B. and Yolacan, S. 2007. A comparison of various tests of normality. J. Statist. Comput. Simul.,77(2): 175–183.


Dr Anil Dolgun (Email: [email protected]) Dr Alice Johnstone (Email: [email protected]) Dr Haydar Demirhan (Email: [email protected])




Assessing inter-rater agreement based on arcsine transformation Mathematical Sciences – City

Project Description

In biomedical and behavioural sciences, the reliability of a rating system is usually evaluated by analysing inter-rater agreement data. The inter-rater agreement coefficient (a.k.a, inter-rater reproducibility or concordance coefficient) is a statistical measure that quantifies the extent of agreement among observers. It gives a score to measure the degree of homogeneity or consensus in the ratings given by observers. There are number of statistics used to determine the inter-rater agreement and different statistics are appropriate for different types of measurements [1-2]. Among them the Kappa statistic is widely used to assess the inter-rater agreement for nominal data. However, there are two specific cases for the Kappa statistic that cause a paradox: i) low kappa values despite a high observed agreement under highly symmetrically imbalanced marginals, and (2) higher kappa values for asymmetrical imbalanced marginal distributions [3-4].

The goal of this project is to study on assessing inter-rater agreement under the two specific paradoxes previously defined. Using an appropriate arcsine transformation, the negative effect of marginal distributions can be eliminated on the Kappa statistic, hence it will make the Kappa statistic less sensitive to the imbalanced marginal distributions.

Background in computational statistics and categorical data analysis are required. The candidate should also be familiar with programming in R.

References

[1] Banerjee, M., Capozzoli, M., McSweeney, L., & Sinha, D. (1999). Beyond kappa: A review of interrateragreement measures. Canadian journal of statistics, 27(1), 3-23.[2] Yilmaz, A. E., & Saracbasi, T. (2017). Assessing agreement between raters from the point of coefficientsand log-linear models. Journal of Data Science, 15(1), 1-24.[3] Feinstein, A. R., & Cicchetti, D. V. (1990). High agreement but low kappa: I. The problems of twoparadoxes. Journal of clinical epidemiology, 43(6), 543-549.[4] Cicchetti, D. V., & Feinstein, A. R. (1990). High agreement but low kappa: II. Resolving the paradoxes.Journal of clinical epidemiology, 43(6), 551-558.


Dr Anil Dolgun (Email: [email protected]) Dr James Baglin (Email: [email protected]) Dr Haydar Demirhan (Email: [email protected])


School of Science


HDR Project 2018



Project Description

In a two-dimensional square lattice, a self-avoiding walk (SAW) is a path beginning at the origin which does not pass through the same lattice point twice. Counting the number of SAWs, on the two-dimensional lattice, is a fundamental problem in combinatorics and statistical physics. The history of counting SAWs goes back at least six decades to a paper by Orr [3], who considered the problem of counting SAWs of lengths n=1,…,6, on a three dimensional lattice. Since then, a plethora of papers have been published on this research topic, largely concerned with either the effective algorithmic calculation of SAW’s of increasing lengths, or the determination of exact closed-form expressions for counting SAWs on a subset of the two-dimensional lattice containing the origin. Recently the problem of counting all SAWs, on the finite lattice strips of widths one and two, via an exact formula, was completely solved [1], [2].

The aim of this project is to investigate whether the arguments and techniques used to produce the closed-form expressions for the finite lattice strip of widths one and two, can be extended to produce closed-form expressions for enumerating SAWs on finite lattice strips of varying widths, greater than or equal to three. Alternatively, if such exact formulas are not possible, then the project will turn attention to the production of summation formule, for the effective computation of SAWs on these subsets of the two-dimensional square lattice.

The candidate should have a good mathematical background in the area of discrete mathematics, particularly in the area of enumerative combinatorics. Some experience in implementing recursive algorithms in any programming language, would also be an advantage.

References

[1]. M. A. Nyblom, “Counting all self-avoiding walks on a finite lattice strip of width one”, J.. Alg. Num. Th. Appl, 39 (2017), 875–882. [2]. M. A. Nyblom, “Counting all self-avoiding walks on a finite lattice strip of width one and two”, (to appear) Rocky Mountain Journal of Mathematics (2018). [3]. W. J. C. Orr, “Statistical treatment of polymer solutions at finite dilution”, Trans. Faraday Soc. 43 (1947), 12-27.


Dr Michael Anthony Nyblom (Email: [email protected]) Professor Asha Rao (Email: [email protected])


DR222 – PhD (Mathematical Science)

MR222 – Master of Science (Mathematical Science)


Integrated model for joint species distributions

Mathematical Science– City Campus


Species Distribution Models (SDMs) have become an increasingly important research tool in the ecological

and environmental sciences. These models are used to predict maps of the spatial distribution of species

presence or abundance based on the locations of individuals observed in a sample. Regression types models

fitted to location-specific measurements of environmental covariates such as temperature, rainfall or elevation

are the basis for making predictions of species presence or abundance at a given location. These predictions

can be readily computed for different environmental scenarios by modifying the SDM’s environmental inputs

in a manner that reflect hypothetical climate-change scenarios, changes in landscape characteristics or other

changes through time. The predictive ability of these models gives them a wide range of conservation

applications including the management of threatened species, conservation planning, as well as predicting the

likely ranges of invasive species.

Often the data available for species distribution modelling is referred to as presence-only (PO) or presence-

background (PB) data (Pearce and Boyce, 2006), where a set of locations where individuals of the species

under investigation have been observed, but there is usually no information on absences. It is commonly

recognized that the presence-only data can only be used to estimate the distribution of species sighting, where

the sightings distribution is typically biased toward regions more frequented by whoever is collecting the data

l (Aarts et al., 2012; Warton and Shepherd, 2010; Renner and Warton, 2013; Fithian and Hastie, 2013; Wang

and Stone, 2018). Presence-absence and other data sets collected via systematic surveys do not typically suffer

from such bias. Unfortunately, the presence-a

References

1. G. Aarts, J. Fieberg, and J. Matthiopoulos (2012). Comparative interpretation of count, presence–absence

and point methods for species distribution models. Methods in Ecology and Evolution 3, 177–187.

2 J. Elith, S.J. Phillips, T. Hastie, M. Dud´ık, Y.E. Chee, and C.J. Yates (2011). A statistical explanation of

maxent for ecologists. Diversity and Distributions 17, 43-57.

3. William Fithian and Trevor Hastie (2013). Finite-sample equivalence in statistical models for presence-only

data. The Annals of Applied Statistics 7(4), 1917-1939.

4. Jennie L Pearce and Mark S Boyce (2006). Modelling distribution and abundance with presence-only data.

Journal of Applied Ecology, 43(3), 405–412.

5. Ian W Renner and David I Warton (2013). Equivalence of maxent and poisson point process models for

species distribution modeling in ecology. Biometrics 69, 274-281.

6. Ian W Renner, Adrian Baddeley, Jane Elith, William Fithian, Trevor Hastie, Steven Phillips, Gordana

Popovic, and David I Warton (2014). Point process models for presence-only analysis — a review.

Methods in Ecology and Evolution 6, 366-379.

Contact Details:


Dr Yan Wang ([email protected])

Prof Lewi Stone ([email protected])

Dr Ascelin Gordon ([email protected])

DR222 – PhD (Mathematical Science)

MR222 – Master of Science (Mathematical Science)


Mining tropical cyclones using Market Basket Analysis Techniques



Cyclone modelling and prediction are key elements of an early warning system to assist society in adaptation

to and mitigation of risks caused by land-falling cyclones. In this project, we propose to apply the association

rule mining to model the tropical cyclones. Informative association rule mining is fundamental for knowledge

discovery from transaction data, for which brute-force search algorithms, e.g., the well-known Apriori

algorithm, were developed. However, operating these algorithms becomes computationally intractable in

searching the large rule space such as that of the 4-dimensional tropical cyclone data. The stochastic search

algorithm will be developed in the project to tackle this challenge by using the idea of annealing Gibbs

sampling. Large rule space of exponential order can still be randomly searched by this algorithm to generate

an ergodic Markov chain of viable length. The ergodic chain contains the most informative rules with

probability 1, creating a much reduced rule space for subsequent mining without information loss. By using

the informative association rule, we aim to identify the key meteorological and environmental features that

influence the near-surface winds, and develop an innovative statistical approach for cyclone seasonal

prediction. The project aims to provide ground-breaking statistical mining methodology for research on

tropical cyclone climatology in the Southern Hemisphere. This project is a collaborative research in statistical

climatology with researchers from Associate Professor Guoqi Qian from the University of Melbourne. The

World Meteorological Organization (WMO) version of the IBTrACS global TC database is used for our

study, which consists of observations from WMO-designated forecast centers only. This global TC best track

database is endorsed by the WMO Tropical Cyclone Program. The database is used to obtain the time and

position of TC genesis for each TC observed on a global domain.

References

1. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large

databases. ACM SIGMOD Rec 22(2):207–216.

2. Hahsler M, Grün B, Hornik K (2005) arules – A computational environment for mining association rules

and frequent item sets. J Stat Softw 14(15):1–25.

3. Bayardo RJ, Agrawal R (1999) Mining the most interesting rules. 5th ACM SIGKDD International

Conference on Knowledge Discovery and Data Mining (ACM, New York), pp 145–154.

4. Qian G, Field C (2002) Using MCMC for logistic regression model selection involving large number of

candidate models. Monte Carlo and Quasi-Monte Carlo Methods 2000, eds Fang K-T, et al. (Springer,

Berlin), pp 460–474.

5. Qian G, Rao C. R., Sun, X. Y. and Wu, Y. H. (2016). Boosting association rule mining in large datasets via

Gibbs sampling. PNAS, 4958-4963.

6. Amatya A, Demirtas H (2015) MultiOrd: An R package for generating correlated ordinal data. Commun

Stat Simul Comput 44(7):1683–1691.

7. Chaganty NR, Joe H (2006) Range of correlation matrices for dependent Bernoulli random variables.

Biomet

Contact Details:


Dr Yan Wang ([email protected])

Ass Pro Guoqi Qian ([email protected])

Dr Mali Abdollahian ([email protected])




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2018 School of science - RMIT University · 2018-06-14 · Applied Physics . Professor Gary Bryant . Mimicking atomic potentials with colloids: ... 3D atomic structure of amorphous