Agenda · Agenda Workshops 1. Modeling Diversity in Machine Learning ... He is currently head of High-tech Lab in Fanap Co. We live in turbulent and yet amaz - ing era, the paradoxical

AgendaWorkshops

1. Modeling Diversity in Machine Learning . . . . . . . . . . 42. Sensor / Data Fusion, Theoretical and Practical issues . . 53. Discovering Latent Patterns in Collaboration Network . . . . 64. Wizard or a Muggle? Journey in an Exponential world . . . 75. Social Network Analysis with Gephi . . . . . . . . . . . . 86. Adversarial Machine Learning . . . . . . . . . . . . . . . 97. Incidence Theorem and Its Applications . . . . . . . . . . 10

Seminars

1. The Future of Cryptography . . . . . . . . . . . . . . . . . 122. Random testing of distributed systems with guarantees . 133. Computational Concentration of Measure and Robust Learning . . . 144. Online Learning . . . . . . . . . . . . . . . . . . . . . . . 155. Microservice Architecture . . . . . . . . . . . . . . . . . . 166. Animation Synthesis using Machine Learning . . . . . . . 177. Nonlocal Correlations in Networks . . . . . . . . . . . . . 188. Algorithms and Games in Blockchain . . . . . . . . . . . . 199. Price of Competition and Dueling Games . . . . . . . . . . 2010. Learning via Non-Convex Min-Max Games . . . . . . . . 2111. Addressing several biomedical problems using deep learning . . 2212. Personalized Assortment Optimization for Online Retailer . . 2313. Streaming and Massively Parellel Algorithms for Edge Coloring . 2414. Cloud Computing, Edge Computing and Beyond . . . . . . 2515. Edit Distance and LCS: Beyond Worst Case . . . . . . . . . 2616. Coordinated Influence Operation Content on Social Media . . . 2717. A Journey into Media Studies . . . . . . . . . . . . . . . . . 2818. Registration-Based Encryption . . . . . . . . . . . . . . . 2919. Data Fusion: an AI approach for decision making . . . . . . 3020. Fairness in Clustering Algorithms . . . . . . . . . . . . . . 3121. Extremal Configurations in Point-Line Arrangements . . . . 3222. Adversarial Perturbations in Learning from Incomplete Data . . 3323. Recommender Systems Research . . . . . . . . . . . . . 3424. Quantum computing in chemistry . . . . . . . . . . . . . 35

Workshops

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Modeling Diversity in Machine Learning Using Determinantal Point Processes

AlirezaRezaei

I am a PhD candidate in the Allen School of Computer Sci-ence and Engineering at the University of Washington, where I am very fortunate to be advised by Shayan Oveis Gharan. My primary research interests are determinantal point pro-cesses and their applications in machine learning and spec-tral graph theory. Prior to joining the University of Washing-ton, I earned my BS degrees in Mathematics and Computer Engineering at Sharif University of Technology.

A wide variety of machine learning tasks can be cast as an instance of the “subset selection” problem. Giv-en a large dataset of items, the goal of subset selection is to find a small subset which is a good representa-tive of the original data. In particular, the selected subset is expected to preserve the diversity of data. As an example, consider a search task: Giv-en a large number of images or doc-uments, we want to select a subset that are relevant to a user query and also diverse. Note that, diversity is important because many queries can have multiple meanings and aspects, e.g. “apple” can refer to the name of a company or a fruit. Another application is product rec-ommendation where retailers with a large inventory need to pick a small subset of their products which are likely to attract customers. To this end, this selected subset not only should

contain highly rated products, but also needs to include diverse items.In this workshop, we consider differ-ent notions of diversity and will see how they can be employed to math-ematically formulate the problem of choosing a diverse subset of items. In particular, we study determinan-tal point processes (DPP) as a family of probabilistic models which have recently gained a lot of attention to model diversity. We begin with their applications, and then discuss algo-rithms for several fundamental tasks related to DPPs, focusing on the prob-lem of sampling from DPPs. In the final part, I introduce strongly Rayleigh measures, and discuss their basic properties. This family of distribu-tions are in fact generalization of DPPs which are very well-studied in the math-ematics community and understanding their properties can be very helpful to gain more insights about DPPs.

28th December (6th Dey)1600 - 1900

BiographyDate

5

Abstract

BehzadMoshiri

Sensor / Data Fusion, Theoretical and Practical issues


Multi-sensor array, usually referred to as Sensor/Data Fusion,is one of the ab-sorbing topics in Artificial Intelligence and Machine Learning studies. The ad-vantages of multiple-sensor data fusion in terms of cost, accuracy, and reliabil-ity will be explained in this workshop. Generally, “Data Fusion” deals with the synergistic combination of data pro-vided by various knowledge sources or sensors to provide a clear percep-tion of a given scene or environment. The use of sensor/data fusion concept has advantages such as “Redundan-cy”, “Complementary”, “Timeliness” and “Less Costly Information.”Fusion char-acterization addressing the application domain, fusion objective, fusion pro-cess input-output (I/O) characteristics, and sensor suite configuration will be shown. In this workshop the different models and levels of Data Fusion will be presented. Different data fusion meth-ods, including the conventional and intelligent approaches with their appli-cation in Sensor/Data Fusion, Industrial

Automation, Information Technology, Bioinformatics, Transportation Systems (ITS), and Financial Engineering will be presented. Typical examples using data fusion toolboxes will be shown.

The contents of this workshop are given below:Data Fusion Principles & Practice:*Background (Decision Supporting Systems)*Sensor/Data fusion overview*Definition & Formulation*Fusion: A Fission inversion model*Fusion characterization: Application domain, Fusion objective, Fusion process input/output characteristics, Sensor suite configuration.*Different Level Fusion Architectures*Different Fusion Model Architectures*Decision Fusion in a Parallel Sensor suite*Detection (Binary) Decision Analysis*Multihypothesis Decision Analysis*Comparison of Mathematical Tools in Data Fusion*Different Techniques of Sensor fusion: Conventional Approaches Knowledge-based Systems/Intelligent Approaches*Conventional Approaches: OWA (Ordered Weighted Averaging Method) Kalman Filter Bayesian Method Dempster Shafer Method *Knowledge-based Systems / Intelligent Approaches: Fuzzy Logic (Integral Operators) Neural Network* Typical examples using available toolboxes.

Prof. of Control Systems Engineering, School of ECE, University of Tehran, IEEE Senior member, Chair of Control Systems chapter, IEEE Iran Section,Adjunct Prof. of Dept. of ECE, University of Waterloo, CanadaSenior Research Fellow, WISE, Waterloo Institute of Sustainable Energy, Waterloo, Canada.

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Discovering Latent Patterns in Academic Collaboration Network based on Community Detection Approach

Mohammad Heydari


Network Science has a broad de-mand in various academic fields and industries. This workshop is a primary introduction to Social Net-work Analysis using Python and NetworkX, a powerful and mature python library for the creation, ma-nipulation, and study of the struc-ture, dynamics, and functions of complex networks. This graph li-brary is suitable for operation on large real world graphs: e.g., graphs in excess of 10 million nodes and 100 million edges. Due to its depen-

dence on a pure Python “dictionary of dictionary” data structure, Net-workX is a reasonably efficient, very scalable, highly portable framework for network and social network analysis. Based on professionals experience in the network science field, the combination of mentioned tools could be effectively beneficial. It is crucial for contributors in this workshop to have a fundamental knowledge of Python and Network Science methods.

Mohammad is a Msc degree student in the School of Indus-trial and Systems Engineering at Tarbiat Modares University. His primary research interests are Big Data Analytics Tech-niques and their Application in Large Scale Social Networks. Previously He was a Msc degree student in Information Technology Engineering at Shahid Beheshti University, finished his Bsc degree in Software Engineering at Technical and Vocational University of Tehran and got his official diploma in Computer Software.

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7

Abstract

MasoudZamani

Masoud is an AmirKabir MSc. Graduate and a futurist. He continuously researches and works on futures study field, especially on technological singularity. In this regard, he contributes to recognition of this concept by publishing on Journals, Newspapers, his website and also podcasts. Moreover, Masoud studied and researched on the future ethical and legal sides of technology as well. He has done divers projects in high-tech that varied from convergence of biotech, nanotech and cognitive science to blockchain and AI. He is currently head of High-tech Lab in Fanap Co.

We live in turbulent and yet amaz-ing era, the paradoxical co-ex-ist of opportunities and threats. Truth is nobody (literally Nobody) in the world could predict the fu-ture anymore. Accessing to vast amount of data and knowledge, a way better distributed opportu-nities and resources led to a state that innovation paces cannot be contained or predicted no more?so what should we do? How to

choose our career path? How to upgrade our skills to match the market requirement? And the most important question is: how to predict the Future?I have no clear answer! but I can show how the best people in the world solve this problem and make great contribution.Join me on this workshop and we might create something that no one ever has.

Choose to be a Wizard or a Muggle? Journey towards an

Exponential world.


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Social Network Analysis with Gephi

NedaSoltani

1th January (10th Dey)1600 - 1900

Neda is a Ph.D. candidate at Amirkabir University of Technology, currently working on Quality of Experience in a Pervasive Computing Environment. She is interested in Data Science, which led her to SNA and has done research in this area. ررر

Social Networks have grown into our lives so that we may not be able to live properly without them. However, speaking about Social Network Analysis is not limited to studying online websites and mobile applications we use for networking. SNA covers also converting complex problems to graphs and analyzing them as entities (nodes) and relationships (edges). This workshop aims at first, covering a brief introduction to SNA and then applying that brief knowl-edge within one of the most known software: Gephi. Gephi is free multi-platform software written

in Java. It is called Photoshop for graphs. Therefore, we expect a visually convenient way of analyzing graphs.

Gephi is a powerful and user friendly software for visualizing and analyzing complex

networks. In this workshop you will learn the basics of Social Network Analysis as a trend-ing method for problem solv-ing and then you will learn to work with Gephi and use it for network analysis.

BiographyDate

9

Abstract

Mohammad Khalooei

Mohammad Khalooei is a Ph.D candidate at Amirkabir Uni-versity of Technology (Tehran Polytechnic) in the department of computer engineering. He works at the Laboratory of In-telligence and Multimedia Processing of AUT. He is interest-ed in artificial intelligence fields and working on vulnerability of deep neural network, adversarial machine learning and unsupervised learning in theoretical and also deployment phases. He also has some experiences in counseling on us-ing deep neural networks for real data processing and joint international project.

Nowadays, Deep neural networks are the most popular approach which we see its usage in different applications and tasks. As day growth its usage in different tasks, checking the vul-nerability of these networks is being a very important fundamental issue.Therefore, analyzing of each machine learning model (such as neural network) for its vulnerability, is a useful task to assess the usage of that in critical situations.

In this session, We try to cover the key definition step’s of vulnerability of deep neural networks and its defense strategies against

simplest vulnera-bility at first.Then when the minds are boiled, we try to imple-ment and test them in a prac-

tical manner. Also, covering a teamwork remote session for more collaboration is available at the end of the session.

Robustness of Deep Neural Networks


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MozhganMirzaei

I am a Ph.D. student in the Department of Mathematics at UC San Diego. My adviser is Prof. Andrew Suk . My primary interests are combinatorics and combinatorial geometry. I got my B.Sc degree in Mathematics from Sharif University of Technology under supevision of Prof. Ebadollah Mahmood-ian .

Szemerédi-Trotter theorem, one of the Erdős-like cornerstone in geometry, states that any arrange-ment of n points and n lines in the plane determines O(n4/3) incidences. In this workshop, we go over some proofs of Szemerédi-Trotter theo-rem and also review some appli-

cations in combinatorial geom-etry and additive combinatorics such as unit distance problem and sum-product theorem. We also discuss some major open problems in this direction.

Incidence Theorem and Its Applications

1st January (10th Dey)1600 - 1900

Seminars

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The Future of Cryptography:a Case-study of Lattice-based ones

ShahriarEbrahimi

Shahriar have received his BSc and MSc degrees both from Sharif University of Technology (SUT), where he is currently a PhD Candidate of Computer Engineering under supervision of Dr. S. Bayat-Sarmadi. He is a full-time researcher in Secure and Smart Systems (3S) Lab in CE department of SUT. He was also a visiting researcher in Mainz (ZDV center) and Hamburg (DKRZ center) universities back in 2014 and 2016. His research interests include: Post-quantum cryptography, Lattice-based cryptography, Internet of Things (IoT) and Computer/Network Architecture and Security.

Public key encryption (PKE) cryp-tography plays a big role in securing communication channels of internet. The security of every PKE scheme is usually based on a hard problem that has no polynomial time solution using any computa-tional structure. However, widely used classic PKE schemes such as RSA or elliptic curve cryptography (ECC), are based on hard problems that have polynomial solutions using a quantum computer. There-fore, such PKE schemes will not be secure in post-quantum era. There exist different post-quantum PKE schemes, which rely on hard prob-

lems that do not have polynomial time solutions even using a quan-tum computer. Among post-quan-tum schemes, lattice-based cryp-tography and especially learning with errors (LWE) problem have gained high attention due to their low computational complexity. In addition, special cryptographic problems such as fully homomor-phism have solutions based on LWE problem. In this talk, we are going to provide a general over-view of lattice-based cryptography schemes and evaluate them compared to other PKE schemes.

1st day, 3th talk1330 - 1415

BiographyDate

13

Abstract

Random testing of distributed systems with guarantees

Simin is a PhD student at Max Planck Institute for Software Systems (MPI-SWS), working under supervision of Rupak Majumdar. She is broadly interested in testing and model checking of distributed and concurrent systems. Before she joined MPI-SWS, she did an internship on symbolic model checking of reactive systems at Institute of Science and Technology (IST Austria) under supervision of Krishnendu Chatterjee.

Distributed and concurrent appli-cations often have subtle bugs that only get exposed under specific schedules. While these schedules may be found by systematic model checking techniques, in practice, model checkers do not scale to large systems. On the other hand, naive ran-dom explora-tion techniques often require a very large

number of runs to find the specific interactions needed to expose a bug. In recent years, several random testing algorithms have been proposed that, on the one hand, exploit state-space reduction strategies from mod-

el checking and, on the other, provide guarantees on the probability of hitting bugs of certain Kinds.

SiminOraee

1st day, 2nd talk1100 - 1145

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Computational Concentration of Measure and Robust Learning

OmidEtesami

Omid is a researcher at Institute for Research in Fundamental Sciences (IPM) in Iran. Previously he did his undergraduate studies at Sharif University of Technology, his PhD at University of California, Berkeley, and a postdoc at EPFL, Switzerland. As an undergraduate, he participated in programming com-petitions. During his PhD, he worked at Microsoft research and was supported by their PhD fellowship. Recently, his paper has been selected as a Best of 2014 paper in Computer Science by ACM Computing Reviews. His research interest is the use of probability and random-ness in computer science, including pseudorandomness, error-correcting codes and one-way functions based on random graphs, using randomness in auction and differential privacy mechanisms, and aspects of average-case analysis.

I will talk about the algorithmic version of the phenomenon of measure concentration: in many high dimensional probability spaces, for subsets of the space of non-negligible mass, most of the points in the space are very close to at least one point in the subset. We will give an algorithm called MUCIO (MUltiplicative Conditional Influence Optimizer) that efficient-

ly finds one such point with high probability. Then we discuss the implication of this algorithm for the security of machine learning: how it may help an adversary change the distribution of the training set so that a classifier makes an error. This is joint work with Mohammad Mahmoody and Saeed Mahloujifar, and appears in SODA 2020.

1st day, 1st talk1000 - 1045

BiographyDate

15

Abstract

Online Learning

Ehsan received his B.Sc in Computer Engineering at Sharif University of Technology in 2013 and his Ph.D. in Computer Science at University of Southern California in 2019. In his Ph.D., under supervision of David Kempe and Shaddin Dughmi, his research was focused on theoretical computer science. More specifically, he has worked on game theory and online learning. Since August 2019, he is a research scientist at Facebook Inc.

In several games, information is asymmetric. Consider, for example, an auctioneer who knows more about the item than the bidders. In such scenarios, the parties who have additional information may benefit by “signaling” other parties some or all the information they have. Signaling, however, is not limited to auctions. The news each news agency broadcast to its audience and the recommenda-tion letters each professor writes for her/his students are some other more relatable(!) examples of signaling in our life.

In this talk, we will model signal-ing more formally and, as much as time permits, discuss a simple probabilistic proof technique which is useful in a this context and many more. If you like brain teasers, we solve some together as well.

EhsanEmamjomeh

Zadeh

2nd day, 5th talk900 - 945

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Microservice Architecture

AfraAbnar

Afra graduated (B.Sc.) in 2009 and continued her graduate studies in theoretical algorithms with Dr. Safari at SUT.She continued her graduate studies in Data Mining at University of Alberta in Canada. She is now working as a Senior Software Developer at SAP, a software company that builds enterprise software for businesses.

It’s hard to scale an application if we stick to monolithic architecture where all your business logic lives in a giant piece of software component. In this talk we are

going to explain the microservice architecture and the trend of building more scalable and main-tainable software.

1st day, 2nd talk1100 - 1145

BiographyDate

17

Abstract

Animation Synthesis using Machine Learning

Amin is a Ph.D. candidate at Department of Computer Sci-ence, Aalto University, Finland. He works under supervision of Prof. Perttu Hämäläinen. Amin is also a visiting researcher at Imager lab, University of British Columbia, Canada, where he works with Prof. Michiel van de Panne. His current research focuses on developing efficient, creative movement artificial intelligence (AI) for physically-simulated characters in multi-agent settings.

Prior to his Ph.D., Amin had ten years of experience in the video game industry. Specifically, he has worked on several commercial games from various genres including first-person shooter, two-player football, and classic adventure. In these projects, he has been responsible for different programming disciplines including AI, animation, gameplay, and physics.

Automating character control in vir-tual environments is an increasing-ly popular approach for synthesiz-ing procedural animation in video games. This requires a method that outputs, for each timestep, simulation actuation parameters such as joint torques or angles such that the character performs some desired move-ment. This poses a continuous optimi-zation problem with high dimensionality and a large num-ber of physics- based constraints.

This talk gives an overview of AI research for synthesizing proce-dural animations in games. The main focus of the talk will be on physics-based control; however, some of the kinematic-based methods will also be covered. The topic can be useful for people interested in online/offline optimi-

zation, su-p e r v i s e d learning and reinforcement learning.

AminBabadi

1st day, 1st talk1000 - 1045

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Nonlocal Correlations in Networks

SalmanAbolfath

Beygi

His research interests include quantum computation and quantum information theory. He received B.Sc. from the Department of Mathematics at Sharif University of Technology in 2004. He received Ph.D. from the Department of Mathematics of MIT in 2009 under the supervision of Peter Shor. Before joining IPM, he was a postdoc at Institute for Quantum Information at Caltech. He is a member of the edi-torial advisory board of the Journal of Mathematical Physics.

Nonlocality is one of the fascinating features of quantum mechanics. In particular, Bell’s nonlocality completely changes our under-standing of locality in physics and correlations in nature. In this talk, first Bell’s nonlocality and its consequences in algorithms and complexity theory is reviewed. Then the general problem of non-locality in networks is introduced. A main question here is whether

network nonlocality results is correlations beyond Bell’s setting or not. This question is answered affirmatively by presenting an explicit example in the triangle network. This talk is based on joint works with Marc-Olivier Renou, Yuyi Wang, Elisa Baumer, Sadra Boreiri, Nicolas Brunner and Nicolas Gisin. No particular knowledge of quantum mechanics is required to follow this talk.

1st day, 4th talk1430 - 1515

BiographyDate

19

Abstract

Algorithms and Games in Blockchain: Designing Verifable Systems

Arash is a Ph.D. student at the New York University. His research involves Algorithmic Game Theory and other algorithmic problems. He also received his Bachelor degree from the Sharif University of Technology in Computer Engineering.

In several games, information is asymmetric. Consider, for example, an auctioneer who knows more about the item than the bidders. In such scenarios, the parties who have additional information may benefit by “signaling” other parties some or all the information they have. Signaling, however, is not limited to auctions. The news each news agency broadcast to its audience and the recommenda-tion letters each professor writes

for her/his students are some other more relatable(!) examples of signaling in our life.

In this talk, we will model signal-ing more formally and, as much as time permits, discuss a simple probabilistic proof technique which is useful in a this context and many more. If you like brain teasers, we solve some together as well.

ArashPourdamghani

2nd day, 8th talk1500 - 1545

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Price of Competition and Dueling Games

SinaDehghani

Sina is currently a postdoc at Mathematics department in IPM. He received his PhD in computer science from University of Maryland. He works on the boundary of computer science and economics, and on designing and analysis of algorithms and games.

We study competition in a general framework introduced by Im-morlica, Kalai, Lucier, Moitra, Pos-tlewaite, and Tennenholtz and answer their main open question. Immorlica et al. considered classic optimization problems in terms of competition and introduced a general class of games called dueling games. They model this competition as a zero-sum game, where two players are competing for a user’s satisfaction. In their main and most natural game, the ranking duel, a user requests a webpage by submitting a query and players output an ordering over all possible webpages based on the submitted query. The user

tends to choose the ordering which displays her requested webpage in a higher rank. The goal of both players is to maximize the probability that her ordering beats that of her opponent and gets the user’s attention. Immorlica et al. show this game directs both players to provide suboptimal search results. However, they leave the following as their main open question: “does competition between algorithms improve or degrade expected performance?” In this talk, we resolvethis question for the ranking duel and a more general class of duel-ing games.

2nd day, 6th talk1000 - 1045

BiographyDate

21

Abstract

Learning via Non-Convex Min-Max Games

Meisam is an assistant professor of Industrial and Sys-tems Engineering and Computer Science at the University of Southern California. Prior to joining USC, he was a post-doctoral research fellow in the Department of Electrical Engineering at Stanford University. He received his PhD in Electrical Engineering with minor in Computer Science at the University of Minnesota under the supervision of Professor

Tom Luo. He obtained his MS degree in Mathematics under the supervision of Pro-fessor Gennady Lyubeznik. Meisam is the recipient of IEEE Data Science Workshop Best Paper Award in 2019, the Signal Processing Society Young Author Best Paper Award in 2014, and the finalist for Best Paper Prize for Young Researcher in Contin-uous Optimization in 2013 and 2016. His research interests include the design and analysis of large scale optimization algorithms arise in modern data science era.

Recent applications that arise in machine learning have surged significant interest in solving min-max saddle point games. This problem has been extensively studied in the convex-concave regime for which a global equi-librium solution can be computed efficiently. In this talk, we study the problem in the non-convex regime and show that an ϵfirst

order stationary point of the game can be computed when one of the player’s objective can be optimized to global optimality efficiently. We applied our algorithm to a fair classification problem of Fashion-MNIST dataset and observed that the proposed algorithm results in smoother training and better generalization.

MeisamRazaviyayn

2nd day, 8th talk1500 - 1545

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Addressing several biomedical problems using deep learning

AliSharifiZarchi

Ali received his Bachelor and Master degrees from the Sharif University of Technology in Computer Engineering, and his Ph.D. degree in bioinformatics form the Institute of Biochem-istry and Biophysics in the University of Tehran under the supervision of Dr. Mehdi Sadeghi and Dr. Hamid Pezeshk. Now he is doing bioinformatics research at Royan Institute and is an assistant prof. of bioinformatics at the department of computer engineering at the Sharif University of Technology.

In this talk, I will review the most recent findings of a group of great students in my lab, who employed deep learning techniques to

address several important prob-lems in single cell sequencing, cancer, and beyond.

2nd day, 6th talk1000 - 1045

BiographyDate

23

AbstractPersonalized Assortment Optimiza-tion for Online Retailer Considering

Risk of Customers Churning

Morteza is currently a Lecturer (Assistant Professor) at the School of Information, Systems and Modelling, UTS, Syd-ney. Prior to joining UTS, He was a Lecturer at the UNSW, Business school. He has an outstanding research record and significant capabilities in the area of business intelligence, data mining and applied machine learning. He has pub-lished more than 180 papers in reputable academic journals

and conference proceedings such as Future Generation Computer Systems, Knowledge-Based Systems, Computers & Industrial Engineering, Fuzzy Sets and systems, IEEE-ACCESS, Applied Soft Computing, Enterprise Information Systems, Business Process Management Journal, Journal of Manufacturing Systems, Journal of Loss Prevention in the Process Industries, Technological Forecasting and Social Change, Process Safety and Environmental Protection, Neurocomputing, Safety science, Quality & Quantity, Energy Policy, Applied Energy, Energy, AAA-I, WSDM & IJCAI.

In this work, we consider an e-tailer with heterogeneous customers both on their preference and loy-alty. Customer attrition plagues e-tailer and they should find a wayto address this issue since it has been studied that attracting new customers is more costly rather than keeping the current one. However keeping unhappy cus-tomers is not an easy task and sometime approaching them make the situation worst. Thus animplicit approach in saving the unhappy and at risk customer is

chosen in this paper.We proposed a heuristic person-alized assortment planning model by reserving items with low inventory levels or high demand for at risk customers who may have a stronger preference for those items. The survival model has been used to predict at risk customers and their lifetime. The output of the survival model has been used in the proposed dynamic programming model for the personalized assortment planning.

MortezaSaberi


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Streaming and Massively Parellel Algorithms for Edge Coloring

HamedSaleh

I am a PhD student in the Computer Science Department at the University of Maryland, advised by Prof. Hajiaghayi. I work on combinatorial problems in distributed/parallel models, in particular models with sublinear memory such as MPC. Prior to joining the University of Maryland, I earned my BS degrees in Computer Engineering at Sharif University of Technology.

A valid edge-coloring of a graph is an assignment of “colors” to its edges such that no two incident edges receive the same color. The goal is to find a proper coloring that uses few colors. (Note that the maximum degree, ∆, is a trivial lower bound.) In this paper, we revisit this fundamental problem in two models of compu-tation specific to massive graphs, the Massively Parallel Computations (MPC) model and the Graph Stream-ing model:

Massively Parallel Computation: We give a randomized MPC algorithm that with high probability returns a ∆ + Õ(∆^3/4) edge coloring in O(1) rounds using O(n) space per machine and O(m) total space. The space per machine can also be further improved

to n^1−Ω(1) if ∆ = n^Ω(1). Our algo-rithm improves upon a previous result of Harvey et al. [SPAA 2018].

Graph Streaming: Since the output of edge-coloring is as large as its input, we consider a standard variant of the streaming model where the output is also reported in a streaming fashion. The main challenge is that the algorithm cannot “remember” all the reported edge colors, yet has to out-put a proper edge coloring using few colors. We give a one-pass Õ(n)-space streaming algorithm that always returns a valid coloring and uses 5.44∆ colors with high probability if the edg-es arrive in a random order. For adver-sarial order streams, we give another one-pass Õ(n)-space algorithm that requires O(∆^2) colors.


BiographyDate

25

Abstract

Cloud Computing, Edge Computing and Beyond

Mohammad is a Ph.D. student at Department of Computer Science, University of Toronto. He has received his bachelor and master degrees in Computer Engineering from Sharif University of technology. His research interests include Distributed Systems, Cloud Computing and Software Systems.

Cloud computing has revolutionized the way computation is provided to users in terms of flexibility, agility, reliability and economies. The cloud architecture generally means to con-solidate computation and storage re-sources in centralized big data centers, put them under a single administration and then make them available as dif-ferent kinds of well-defined services.

Edge computing expands the tradi-tional cloud architecture with addi-tional data center layers that provide computation and storage closer to the end user. For example, a wide-area cloud data center which serves a

large country can be augmented by a hierarchy of data centers that provide coverage at the city, neighborhood, and building level. Edge Computing facilitates the next generation of mobile and IoT applications that require low latency or produce large volumes of data.

In this talk, we will review the evolu-tion of cloud computing and edge computing over the last few years, present some recent work in the area of edge computing and discuss the opportunities that are predicted to become available in near future.

Mohammad Salehe

2nd day, 8th talk1500 - 1545

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Edit Distance and LCS: Beyond Worst Case

MahdiSafarnejad

Mahdi is a Ph.D. student at the Sharif University of Technology. He is supervised by Dr. Mohammad Ghodsi. His Re-search involves approximation algorithms for edit distance and similar problems. He also received his Bachelor and Master degrees from the Sharif University of Technology in Computer Engineering.

In the worst-case analysis of algo-rithms, the overall performance of an algorithm is summarized by its worst performance on any input. This approach has countless suc-cess stories. However, there are also major computational prob-lems (like linear programming, clustering, and online caching) where the worst-case analysis framework does not provide any helpful advice on how to solve the problem.

In this talk, we study improved algorithms for edit distance and longest

common subsequence, which are among the most fundamen-tal problems in combinatorial optimization, beyond their worst-case. The proposed algorithms obtain $1+o(1)$ approximate solu-tions for both problems in tru-ly subquadratic time if the input satisfies a mild condition. In this setting, first, an adversary choos-es one of the input strings. Next, this string is perturbed by a ran-

dom procedure. Then the adversary chooses the second string arbitrarily after observing the per-turbed one.

2nd day, 7th talk1100 - 1145

BiographyDate

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Abstract

Detecting Coordinated Influence Operation Content on

Social Media

Mesysam has been harnessing the wealth of network and human social data available through social media platforms to understand the roots and spread of extremist ideology. In recent projects during his Ph.D. at George Mason University and postdoctoral fellowship at Indiana University Bloom-ington, Alizadeh has explored the moral and emotional fac-tors underlying political extremism. He is also studying how

extremism spreads on social media by analyzing the information sharing behav-ior of political extremists on Twitter. Currently, Meysam is a postdoctoral research associate at the Empirical Studies of Conflict Project at Princeton University and is studying foreign influence efforts on democratic elections.

We study a platform-agnostic method of using available activi-ty by coordinated influence oper-ations on social media to detect and assess their ongoing activities. Our approach classifies the post-URL pair based on human-inter-pretable features without relying on user-level behavioral data. We test on data from all publicly avail-able Twitter datasets of Chinese, Russian, and Venezuelan troll activity targeting the United States from late-2015 through 2019, and Reddit dataset of Russian influ-ence effort during 2015 and 2016. Instead of following the conven-tional approach to train a classifier based on the entire dataset, we

train classifiers on a monthly basis across each campaign to capture how changes in trolls activities impact the performance of our classifier over time. Prediction per-formances vary by month, country, platform, and experimental de-sign, ranging from average F1 score of 0.75 to 0.94, and is robust to 1% false negative and false pos-itive rates. Additional diagnostics test and policy implications and challenges will be discussed.In this project, advised by Professor Jacob Shapiro, Meysam is using pub-licly available verified data sets of for-eign online influence operations to train classifiers that can identify suspicious activities on social media.

MeysamAlizadeh

1st day, 3rd talk1330 - 1415

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28

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A Journey into Media Studies from the Perspective of a Technical Person

RezaMohammadi

Reza received his B.Sc. in Computer Science from Sharif Uni-versity of Technology in 2009. After multiple failed businesses, with his friends they launched Bazaar Android application store, which now serves more than 38 million users. In 2012 they launched Divar, a classified advertisement internet ser-vice on which hundreds of thousands of ads are published every day. In 2017, he left the company to study New Media and Digital Culture at the University of Amsterdam. Now, he is a Product Owner in an Amsterdam-based company called Machine2Learn.

Setting objectives, measuring key results, and acting accordingly is a very effective method of making internet products. You can use today’s data analysis tools to understand what your users want from your product, and act even in real time to deliver that to them. The problem is that the key metrics which are measured are usually indicators of short-term behaviour of the product users, and they don’t answer important

questions about what you are building, such as whether your product design is ultimately good for society.

I applied for a Master’s pro-gramme in humanities to

find a way to measure such important qual-ities in product de-sign. In this talk, I will share my experience;

how I didn’t find an easy method for the afore-

mentioned problem, and what was changed in me instead.

2nd day, 6th talk1000 - 1045

BiographyDate

29

Abstract

Registration-Based Encryption

Mohammad’s research focuses on the foundations of Cryptography and its interplay with Computational Complexity and Adversarial Learning. He obtained his BS degree from the Computer Engineering Department of Sharif University in 2004 and got his Ph.D. from Princeton University in 2010. He then spent a few years at Cornell University as a postdoctoral associate before joining the University of Virginia as an assistant professor in 2010, where he is currently an associate professor of Computer Science.

Public-key encryption (PKE) has revolutionized cryptography, by allowing remote parties to com-municate secretly. The down side of PKE is to maintain long lists of public keys in public-key direc-tories. Identity-based encryption (IBE) gives a way around such lists by allowing one master public key and people’s unique names (e.g., their email addresses) to be their public-key. The down side of IBE is that the issuer of the master public- key (and decryption keys) can decrypt all the messages, leading to the well-known key escrow problem with IBE. In this talk, I

will introduce *registration* based encryption, which is a hybrid encryption method connecting IBE and PKE, getting (most) the benefits of both, without suffering from long key directories nor the key escrow problem.

Based on joint (TCC and PKC 2019) works with Sanjam Garg, Ahmadreza Rahimi, Mohammad Hajiabadi, and Sruthi Sekar.

Mohammad Mahmoudi

1st day, 3rd talk1330 - 1415

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30

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Data Fusion: an AI approach for decision making

BehzadMoshiri

Behzad received his B.Sc. degree in mechanical engineering from Iran University of Science and Technology (IUST) in 1984 and M.Sc. and Ph.D. degrees in control systems engineering from the University of Manchester, Institute of Science and Technology (UMIST), U.K. in 1987 and 1991 respectively. He joined the school of electrical and computer engineering, university of Tehran in 1992 where he is currently professor of control systems engineering. He has been the member of International Society of Information Fusion (ISIF) since 2002 and senior member of IEEE since 2006. He is now serving as the chair of IEEE control system chapter in Iran section since March 2019. He is the author/co-author of more than 360+ articles including 120+ journal papers and 20+ book chapters. His fields of research include advanced industrial control, advanced instrumentation systems and applications of data/information fusion in areas such as robotics, process control, mechatronics, information technology (IT), bio-informatics, intelligent transportation systems (ITS) and financial engineering.

Multi-sensor array, usually referred to as Sensor/Data Fusion, is one of the absorbing topics in Artificial Intelli-gence and Machine Learning studies. Data fusion is a significant technique for detection, estimation and decision making. A decision support system based on multi-sensor data fusion can be an effective solution to over-come the problems such as uncer-tainties in the context of situation awareness and threat analysis. The advantages of multiple-sensor data fusion in terms of cost, accuracy, and reliability will be explained in this talk. Generally, “Data Fusion” deals with the synergistic combination of data provided by various knowledge sources or sensors to provide a clear percep-tion of a given scene or environment.

The use of sensor/data fusion concept has advantages such as “Redundancy”, “Complementary”, “Timeliness” and “Less Costly Information”. Fusion char-acterization addressing the applica-tion domain, fusion objective, fusion process input-output (I/O) character-istics, and sensor suite configuration will be shown. In this presentation the different models and levels of Data Fusion will be presented. Among different data fusion methods, includ-ing the conventional and intelligent approaches, the various techniques coupled with their applications in Industrial Automation, Information Technology, Bioinformatics, Intelligent Transportation Systems (ITS) and Fi-nancial Engineering will be presented.

1st day, 1st talk1000 - 1045

31

Fairness in Clustering Algorithms

Mohammad is a research scientist at the Google Research lab in New York, specializing in market algorithms. He has a Ph.D. from MIT, an M.Sc. from University of Toronto, and a B.Sc. from Sharif University of Technology. Prior to Google, he has worked at Yahoo! Research and Microsoft Research.

Clustering is a fundamental prob-lem in unsupervised machine learning. The goal of this problem is to partition a set of given data points so that similar points are grouped together. In this talk, I’ll give an overview of several ways to formalize this problem and algo-

rithms that have been proposed to solve them. Many applications of clustering require the solution to satisfy additional desirable prop-erties such as fairness. I will give a formal definition of clustering with fairness, and present a number of recent results in this area.

Mohammad Mahdian

1st day, 2nd talk1100 - 1145

BiographyAbstract

Date

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Extremal Configurations in Point-Line Arrangements

MozhganMirzaei

Mozhgan is a last year PhD student at the University of California at San Diego, working under supervision of prof. Andrew Suk. She is interested in Combinatorics and Combinarial Geometry. She got her B.Sc degree in Mathematics from Sharif University of Technology under supervision of prof. Ebadollah Mahmoodian.

The famous Szemerédi-Trotter theorem states that any arrange-ment of n points and n lines in the plane determines O(n4/3) inci-dences, and this bound is tight. Al-though there are several proofs for the Szemerédi-Trotter theorem, our knowledge of the structure of the point-line arrangements max-imizing the number of incidences is severely lacking. In this talk, we present some Turán-type results for point-line incidences. Let L1 and L2 be two sets of t lines in the plane and let P = l1 ϵ l2 : l1 ϵ L1,l2 ϵ L2 be the set of intersection points

between L1 and L2. We say that (P,L1 ϵ L2) forms a natural t × t grid if |P| = t2, and conv(P) does not con-tain the intersection point of some two lines in Li, for i = 1,2. For fixed t > 1, we show that any arrangement of n points and n lines in the plane that does not contain a natural t×t grid determines O(n-43ϵ) incidences, where ϵ = ϵ(t). We also provide a con-struction of n points and n lines in the plane that does not con-tain small cycles and determines superlinear number of incidences. This is joint work with Andrew Suk and Jacques Verstraete.

1st day, 4th talk1430 - 1515

BiographyDate

33

AbstractRobustness to Adversarial Perturbations in Learning

from Incomplete Data

Amir received his B.Sc. and M.Sc. degrees in Electrical Engi-neering from Sharif University of Technology, Tehran, Iran, in 2012 and 2015, respectively. He is currently a Ph.D. student at Computer Engineering Dept. of Sharif University of Technology. He was with the Broad Institute of MIT and Harvard, Boston, MA, in 2016 as a visiting research scholar, and interned at Preferred Networks Inc., Tokyo, Japan, in 2018. His research interests include machine learning theory, information theory and bioinformatics.

What is the role of unlabeled data in an inference problem, when the presumed underlying distri-bution is adversarially perturbed? In this talk, I explain how we answer to this question by unifying two major learning frame-works: Semi-Supervised Learning (SSL) and Distributionally Robust Optimization (DRO). We develop a generalization theory for our framework based on a number of novel complexity measures, such as an adversarial extension of Rademacher complexity and its

semi-supervised analogue. More-over, our analysis is able to quantify the role of unlabeled data in the generalization process under a more general condition compared to existing works in SSL. Based on our framework, we also present a hybrid of DRL and EM algorithms that has a guaranteed conver-gence rate. When implemented with deep neural networks, our method shows a comparable per-formance to those of the state-of-the-art on a number of real-world benchmark datasets.

AmirNajafi

2nd day, 7th talk1100 - 1145

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Recommender Systems Research:Advances, Pitfalls andOpportunities

ZahraNazari

Zahra is a Research Scientist at Spotify, New York. She has a Ph.D. in computer science from the University of Southern California. Her research interests include understanding and modeling human behavior in complex situations, in partic-ular as applied to search and recommendation systems. Zahra has received her B.Sc. degree in Computer Science from the Amirkabir University of Technology and her M.Sc. degree from the University of Louisiana.

This talk covers the evolution of recommender systems research, its current emphasis on short term engagement and the opportunities for providing long-term fulfilling

experiences. I will ground this talk with the work conducted within Home and Search personalization efforts at Spotify.

2nd day, 7th talk1100 - 1145

BiographyDate

35

Abstract

An application of quantum computing in chemistry

Moslem received his bachelor’s degrees in electrical engi-neering and applied mathematics from Amirkabir University of Technology in 2005 and 2006, respectively. He then joined the Electrical Engineering Department at the University of Alberta where he obtained his M.Sc. in 2008 and his PhD in 2012. Moslem later finished two postdoctoral programs at the University of British Columbia and the University of Alberta

and also spent a visiting period at Nokia Bell Labs. He was the recipient of several scholarships and awards including the Vanier Canada graduate scholarship, NSERC postdoctoral fellowship, and Alberta Innovates fellowship. He is currently a senior research scientist at 1QBit, a quantum computing company headquartered in Vancouver, and is also affiliated with the Microsoft Quantum team in Redmond.

Quantum computing is likely to have far-reaching impact on different fields such as chem-istry, optimization and artificial intelligence. In this talk, we start by a brief overview of quantum computing and then present an application of quantum computing to solve an important problem, called “conformational search”, in chemistry. For the conformational search problem, we propose a

variable neighbourhood search heuristic where using the struc-ture of a molecule, neighbour-hoods are chosen to allow for optimization using a binary qua-dratic optimizer. The proposed method is well-suited for the use of devices such as quantum annealers. After carefully defining neighbourhoods, the method easily adapts to the size and topology of these devices.

MoslemNoori

1st day, 4th talk1430 - 1515

Documents

Agenda · Agenda Workshops 1. Modeling Diversity in Machine Learning ... He is currently head of High-tech Lab in Fanap Co. We live in turbulent and yet amaz - ing era, the paradoxical