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Curriculum Vitae of Man Wai MAK Oct, 2015

Personal Particulars Qualification: BEng(Hons), PhD, CEng, Sr. MIEEE, MIET Address : Department of Electronic and Information Engineering The Hong Kong Polytechnic University, Hung Hom, Hong Kong Telephone : (852)2766-6257 Fax : (852)2362-8439 Email : enmwmak@polyu.edu.hk URL : http://www.eie.polyu.edu.hk/~mwmak Post : Associate Professor Fields of Research : Speaker recognition, machine learning, and bioinformatics Highlight

§ 162 publications (include two books) § Associate Editor of IEEE Transactions on Audio, Speech and Language Processing § Associate Editor of Journal of Signal Processing Systems § Two Research Grant Achievement Awards § Programme Committee Member of over 30 International Conferences or Workshops § Area Chair of Interspeech’2014 § Steering Committee Member of ISCSLP § Programme Leader of BEng in Electronic Engineering

Brief Technical Profile Dr Man-Wai Mak joined the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University in 1993, and presently he is an Associate Professor. He has authored more than 160 technical articles in speaker recognition, machine learning, and bioinformatics. Dr. Mak also coauthored a postgraduate textbook “Biometric Authentication: A Machine Learning Approach, Prentice Hall, 2005” and a research monograph “Machine Learning for Protein Subcellular Localization Prediction, De Gruyter, 2015”. He served as a member of the IEEE Machine Learning for Signal Processing Technical Committee in 2005-2007 and a member of Technical Committee Member, IEEE Computation Intelligence Society, Intelligent Systems Applications in 2008. He has served as an associate editor of IEEE Trans. on Audio, Speech and Language Processing. He is currently an editorial board member of Journal of Signal Processing Systems and Advances in Artificial Neural Systems. He has also served as Technical Committee Members of a number of international conferences. Research Journal Publications (Since 2008): No. Paper Impact

Factor# Grade No. of

Citations* 1 M.W. Mak, J. Guo, and ((S.Y. Kung)), "PairProSVM: Protein Subcellular

Localization Based on Local Pairwise Profile Alignment and SVM", IEEE/ACM Trans. on Computational Biology and Bioinformatics, vol. 5, No. 3, pp. 416 - 422, 2008.

1.605 A 45

2 S.B. Wan, M.W. Mak, and (S.Y. Kung), "GOASVM: A Subcellular Location Predictor by Incorporating Term-Frequency Gene Ontology into the General Form of Chou’s Pseudo-Amino Acid Composition", J. of Theoretical Biology, vol. 323 pp. 40–48 2013.

2.239 B 45

3 S.B. Wan, M.W. Mak, and (S.Y. Kung), "mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines", BMC Bioinformatics, 2012, 13:290.

3.452 A 42

4 M.W. Mak and W. Rao, "Utterance Partitioning with Acoustic Vector Resampling for GMM-SVM Speaker Verification", Speech Communication, vol. 53, no. 1, Jan. 2011, pp. 119-130.

1.786 B 28

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5 M.W. Mak and H.B. Yu, " A Study of Voice Activity Detection Techniques for

NIST Speaker Recognition Evaluations", Computer Speech & Language, vol. 28, No. 1, Jan 2014, pp. 295-313.

1.789 B 22

6 W. Rao and M.W. Mak, "Boosting the Performance of I-Vector Based Speaker Verification via Utterance Partitioning", IEEE Trans. on Audio, Speech and Language Processing, vol. 21, no. 5, pp. 1012-1022, May 2013.

2.604 A 21

7 S.B. Wan, M.W. Mak, and (S.Y. Kung), "HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins", PLoS ONE, vol. 9, no. 3, March 2014.

3.702 12

8 M.W. Mak and (S.Y. Kung), "Fusion of Feature Selection Methods for Pairwise Scoring SVM", Neurocomputing, vol. 71, no. 16-18, pp. 3104-3113, Oct. 2008.

2.292 B 17

9 S.B. Wan, M.W. Mak, and (S.Y. Kung), "R3P-Loc: A compact multi-label predictor using ridge regression and random projection for protein subcellular localization", J. of Theoretical Biology, vol. 360, pp. 34–45, 2014.

2.239 B 8

10 S.X. Zhang and M.W. Mak, "Optimized Discriminative Kernel for SVM Scoring and its Application to Speaker Verification", IEEE Trans. on Neural Networks, vol. 22, no. 2, Feb. 2011.

4.720 A 8

11 S.B. Wan, M.W. Mak, and (S.Y. Kung), "mPLR-Loc: An Adaptive-decision Multi-label Classifier Based on Penalized Logistic Regression for Protein Subcellular Localization Prediction", Analytical Biochemistry: Methods in the Biological Sciences, Vol. 473, March 2015, Pages 14-27

2.564 6

12 (S.Y. Kung) and M.W. Mak, "Feature Selection for Self-Supervised Classification with Applications to Microarray and Sequence Data", IEEE Journal of Selected Topics in Signal Processing, Special Issue on Genomic and Proteomic Signal Processing, vol. 2, No. 3, pp. 297-309, 2008.

3.681 A 5

13 S.X. Zhang and M.W. Mak, "A New Adaptation Approach to High-Level Speaker-Model Creation in Speaker Verification", Speech Communication, vol. 51, no. 6, pp. 534-550, 2009.

1.786 B 3

14 X.M. Pang and M.W. Mak, "Noise Robust Speaker Verification via the Fusion of SNR-Independent and SNR-Dependent PLDA", International Journal of Speech Technology, Oct. 2015.

0

15 S.B. Wan, M.W. Mak and (S.Y. Kung), "Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Issue 99, 2015 (14 pages).

1.605 A 0

16 S.B. Wan, M.W. Mak and (S.Y. Kung), "mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor", J. of Theoretical Biology, vol. 382, Oct 2015, pp. 223–234.

2.239 B 1

17 Na Li and M.W. Mak, "SNR-Invariant PLDA Modeling in Nonparametric Subspace for Robust Speaker Verification", IEEE/ACM Trans. on Audio Speech and Language Processing, vol. 23, no. 10, pp. 1648-1659, Oct. 2015.

2.604 A 1

18 Baiying Lei and M.W. Mak, "Robust scream sound detection via sound event partitioning", Multimedia Tools and Applications, March 2015, pp. 1-19, DOI: 10.1007/s11042-015-2555-z.

1.374 C 0

19 (S.Y. Kung), Y.H. Luo, and M.W. Mak, "Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios, Journal of Signal Processing Systems for Signal, Image, and Video Technology, Vol. 61, Issue 1, Special Issue, pp. 3–20, Oct. 2010.

0.675 C 9

3

20 (S.Y. Kung) and M.W. Mak, "PDA-SVM Hybrid: A Unified Model for Kernel-

Based Supervised Classification, Journal of Signal Processing Systems for Signal, Image, and Video Technology, May 2011.

0.675 C 3

21 M.W. Mak, X.M. Pang and (J.T. Chien), "Mixture of PLDA for Noise Robust I-Vector Speaker Verification", IEEE/ACM Trans. on Audio Speech and Language Processing, to appear.

2.604 A 0

22 S.B. Wan and M.W. Mak, "Predicting Subcellular Localization of Multi-Location Proteins by Improving Support Vector Machines with an Adaptive-Decision Scheme", International Journal of Machine Learning and Cybernetics, Special Issue on Semantic Data Analytics and Bioinformatics, to appear.

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# 5-Year Impact Factor From Web of Science * Google Scholar Authors with brackets are my research collaborators. Others are my students/RA/PostDoc Conference Papers (Since 2010) No. Paper No. of

Citations* 1 M.W. Mak, "Fast Scoring for Mixture of PLDA in I-Vector/PLDA Speaker Verification", Asia-Pacific

Signal and Information Processing Association, Annual Summit and Conference (APSIPA ASC), Dec. 2015, Hong Kong.

0

2 Zhili Tan and M.W. Mak, "Bottleneck Features from SNR-Adaptive Denoising Deep Classifier for Speaker Identification", Asia-Pacific Signal and Information Processing Association, Annual Summit and Conference (APSIPA ASC), Dec. 2015, Hong Kong.

0

3 N. Li and M.W. Mak, "SNR-Invariant PLDA Modeling for Robust Speaker Verification", Interspeech'15, Sept. 2015, Dresden, Germany, pp. 2317 - 2321.

1

4 W. Rao, M.W. Mak and (K.A. Lee), "Normalization of Total Variability Matrix for I-Vector/PLDA Speaker Verification", ICASSP'2015, Brisbane, 2015, pp. 4180 - 4184.

0

5 X.M. Pang and M.W. Mak, "Fusion of SNR-Dependent PLDA Models for Noise Robust Speaker Verification", Int. Sym. on Chinese Spoken Language Processing (ISCSLP14), Sept. 2014, Singapore, pp. 64-68.

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6 W. Rao and M.W. Mak, "Relevance Vector Machines with Empirical Likelihood-Ratio Kernels for PLDA Speaker Verification, Int. Sym. on Chinese Spoken Language Processing (ISCSLP14), Sept. 2014, Singapore, pp. 64-68.

0

7 M.W. Mak, "SNR-Dependent Mixture of PLDA for Noise Robust Speaker Verification", Interspeech 2014, Sept. 2014, Singapore, pp. 1855-1859.

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8 (Jinghua Zhong), (Weiwu Jiang), Wei Rao, Man-Wai Mak, and (Helen Meng), "PLDA Modeling in the Fishervoice Subspace for Speaker Verification", Interspeech2014, Sept. 2014, Singapore, pp. 1130-1134.

0

9 B.Y. Lei and M.W. Mak, "Sound-Event Partitioning and Feature Normalization for Robust Sound-Event Detection", International Conference on Digital Signal Processing, 2014

0

10 W. Rao and M.W. Mak, "Construction of Discriminative Kernels from Known and Unknown Non-targets for PLDA-SVM Scoring", ICASSP'2014, pp. 4040-4044, Florence, 2014.

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11 S.B. Wan, M.W. Mak, B. Zhang, Y. Wang and (S.Y. Kung), "Ensemble Random Projection for Multi-label Classification with Application to Protein Subcellular Localization", ICASSP'2014, pp. 6040-6044, Florence, 2014.

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12 S.B. Wan, M.W. Mak, B. Zhang, Y. Wang and (S.Y. Kung), An Ensemble Classifier with Random Projection for Predicting Multi-label Protein Subcellular Localization", Workshop on Integrative Data Analysis in System Biology, IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM'2013), Dec.

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2013, Shanghai, pp. 35-42.

13 M.W. Mak and W. Rao, "Likelihood-Ratio Empirical Kernels for I-Vector Based PLDA-SVM Scoring",IEEE Int. Conf. on Acoustic Speech and Signal Processing (ICASSP'13), May 2013, Vancouver, pp. 7702-7706.

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14 S.B. Wan, M.W. Mak and (S.Y. Kung), "Adaptive Thresholding for Multi-Label SVM Classification with Application to Protein Subcellular Localization Prediction", IEEE Int. Conf. on Acoustic Speech and Signal Processing (ICASSP'13), May 2013, Vancouver, pp. 3547-3551

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15 W. Rao and M.W. Mak, "Alleviating the Small Sample-Size Problem in I-Vector Based Speaker Verification", Int. Sym. on Chinese Spoken Language Processing (ISCSLP'12), Dec. 2012, Hong Kong,pp. 335-339.

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16 W. Rao and M.W. Mak, "Utterance Partitioning with Acoustic Vector Resampling for I-Vector Based Speaker Verification", Odyssey 2012: The Speaker and Language Recognition Workshop, Singapore, Jun 2012, pp. 165-171.

5

17 S.B. Wan, M.W. Mak, and (S.Y. Kung), "GOASVM: Protein Subcellular Localization Prediction Based on Gene Ontology Annotation and SVM", ICASSP'12, Kyoto, 2012, pp. 2229-2232.

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18 M.W. Mak and (S.Y. Kung), "Low-Power SVM Classifiers for Sound Event Classification on Mobile Devices", ICASSP'12, Kyoto, 2012, pp. 1985-1988.

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19 S.B. Wan, M.W. Mak, and (S.Y. Kung), "Protein Subcellular Localization Prediction Based on Profile Alignment and Gene Ontology", 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP'11), Beijing, Sept. 2011.

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20 H.B. Yu and M.W. Mak, "Comparison of Voice Activity Detectors for Interview Speech in NIST Speaker Recognition Evaluation", Interspeech'11, pp. 2353-2356, pp. 2353-2356, Florence, Aug. 2011.

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21 W. Rao and M.W. Mak, "Addressing the Data-Imbalance Problem in Kernel-based Speaker Verification via Utterance Partitioning and Speaker Comparison", Interspeech'11, pp. 2717-2720, Plague, July 2011.

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22 (W. Jiang), M. W. Mak, W. Rao, (H. Meng), The HKCUPU System for the NIST 2010 Speaker Recognition Evaluation, ICASSP' 2011. pp. 5288 - 5291, Florence, Sept. 2011.

1

23 M.W. Mak, W. Wang, and (S.Y. Kung), "Truncation of Protein Sequences for Fast Profile Alignment with Application to Subcellular Localization", Proc. IEEE International Conference on Bioinformatics & Biomedicine (BIBM'2010), pp. 115-120, Dec. 2010, Hong Kong.

1

24 M.W. Mak and H.B. Yu, "Robust Voice Activity Detection for Interview Speech in NIST Speaker Recognition Evaluation, "Proc. APSIPA ASC 2010, pp. 64-71, Dec. 2010, Singapore.

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25 M.W. Mak and W. Rao, "Acoustic Vector Resampling for GMMSVM-Based Speaker Verification, Interspeech 2010. Sept. 2010, Makuhari, Japan, pp. 1449-1452.

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26 W. Wang, M.W. Mak and (S.Y. Kung), "Speeding up Subcellular Localization by Extracting Informative Regions of Protein Sequences for Profile Alignment", Proc. Computational Intelligence in Bioinformatics and Computational Biology (CIBCB'10), Montreal, May, pp. 147-154.

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* Google Scholar

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Book Publications (Since 2008): No. Book No. of

Citations* 1 S.B. Wan and M.W. Mak, Machine Learning for Protein Subcellular Localization

Prediction, De Gruyter, 2015.

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2 (S.Y. Kung), M.W. Mak and S.H. Lin, Biometric Authentication: A Machine Learning Approach, Prentice Hall, 2005.

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* Google Scholar Invited Talks (Since 2011): No. Talk 1 “PLDA and Mixture of PLDA for Robust Speaker Verification,” National Chiao Tung University, Taiwan, July

2014

2 “Utterance Partitioning for GMM-SVM Speaker Verification,” JAIST, Japan, Nov 2011.

3 “Utterance Partitioning for GMM-SVM Speaker Verification,” Tokyo Institute of Technology, Japan, Dec 2011.

4 "Machine Learning for Computational Biology", National Chung Hsing University, Taiwan, Jan 2008.

5 "Supervised Feature Selection for Genomic Applications", Feng Chia University, Taiwan, Jan 2008.

Citation Analysis:

https://scholar.google.com/citations?hl=en&user=Q2nNMycAAAAJ

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http://academic.research.microsoft.com/Author/507892/man-wai-mak

https://www.researchgate.net/profile/Man_Wai_Mak/reputation

Editorial Board Membership:

1. Associate Editor, IEEE Trans. on Speech and Language Processing 2. Associate Editor, Journal of Signal Processing Systems 3. Associate Editor, Advances in Artificial Neural Systems 4. Guest Editor, Special Issue on Machine Learning for Microarray and Sequence Analysis 5. Guest Editor, Special Issue on Advances in Machine Learning and Neural Networks

Breakdown of All Publications

Type of Publication Lifetime Since 2010 Journal 48 16 Conference 105 26 Book Chapter 4 0 Book 2 1 Journal Editorial 2 0

Total: 161 43 External Research Grants (Since 2010)

Project Title Investigator Funding Source

Compe-titive

Role Duration Amount (HKD)

1. Speaker-Adaptive Denoising Deep Networks for SNR-Aware PLDA Speaker (GRF)

M.W. Mak and B. Mak

RGC Y PI 36 months 2015 – 2018

695,861

2. Utterance-Length Dependent PLDA and Discriminative Models for I-Vector Speaker Verification (GRF)

M.W. Mak and J.T. Chien

RGC Y PI 36 months 2014 – 2017

500,000

3. Audio Surveillance Systems on Mobile Devices

M.W. Mak and S.Y. Kung

Motorola Solution

Foundations

N PI 24 months 2013 – 2015

193,000

4. Discriminative Models for Biological Sequence Labeling and Segmentation (CERG)

M.W. Mak and S.Y. Kung

RGC Y PI 36 months 2010 – 2013

558,024

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Research Student Supervised (Since 2008): Student Role Graduate Year No. of Paper

Published Destination of

Student WAN Shibiao Chief

Supervisor 2014 (PhD) 6 Journal, 5

conferences PostDoc Fellow,

HKPolyU RAO Wei Chief

Supervisor 2015 (PhD) 2 Journals, 10

conferences PostDoc Fellow, NTU, Singapore

YU Hon Bill Chief Supervisor

2012 (MPhil) 1 journal, 2 conferences

ASTRI, Science Park

WANG Wei Chief Supervisor

2011 (MPhil) 1 journal, 3 conferences

Shenzhen

ZHANG Shixiong Chief Supervisor

2008 (MPhil) 3 journals, 6 conferences

Speech Scientist, Microsoft,

Washington, USA

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One Page Summary of M.W. Mak’s Research: M. W. Mak has been working extensively on machine learning and i- vector/PLDA speaker verification, e.g., (1) “SNR-Invariant PLDA Modeling in Nonparametric Subspace for Robust Speaker Verification”, IEEE/ACM Trans. on Audio, Speech and Language Processing, 2015; (2) “Boosting the Performance of I-Vector Based Speaker Verification via Utterance Partitioning”, IEEE Trans. on Audio, Speech and Language Processing, 2013; (3) Mixture of PLDA for Noise Robust I-Vector Speaker Verification", EEE/ACM Trans. on Audio Speech and Language Processing, 2016; (4) “Biometric Authentication: A Machine Learning Approach”, Prentice Hall, 2005. In these studies, the PI has developed an utterance partitioning and resampling technique to address the data imbalance problem in GMM-SVM and i-vector based speaker verification. More recently, the PI has proposed SNR-dependent mixture of PLDA model and SNR-invariant PLDA that outperform the conventional PLDA based on multi- condition training. To further improve the robustness of speaker verification systems, the PI propose extracting bottleneck features from a DNN classifier formed by stacking RBMs on top of a denoising DNN. The PI has also developed a voice activity detector for segmenting speech files in NIST speaker recognition evaluations. M.W. Mak has also been working extensively on protein subcellular localization prediction and membrane protein type prediction, e.g., (1) Machine Learning for Protein Subcellular Localization Prediction, De Gruyter, ISBN 978-1-5015-0150-0, 2015; (2) Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015; (3) mLASSO-Hum: A LASSO-Based Interpretable Human-Protein Subcellular Localization Predictor, Journal of Theoretical Biology, 2015; (4)HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins, PLoS ONE, 2014. In these studies, the PI has developed a term-frequency based gene-ontology (GO) vector construction method to take full advantage of GO information for predicting protein subcellular localization. Later, the PI has proposed various multi-label classifiers, e.g., multi-label SVM, adaptive-decision based SVM, multi-label ridge regression, adaptive-decision based multi-label logistic regression to deal with problems of predicting subcellular localization of both single- and multi-location proteins. To exploit the information of inter-term relationships among GO terms, the PI has proposed a predictor to hybridize both GO term-frequency of occurrences and the semantic similarity between GO terms. Besides, the PI has developed an interpretable predictor, namely mLASSO-Hum that can not only give prediction results on query proteins, but also give reasons why proteins are located there. To extend the impact of the research, the PI has developed a series of online web-servers for these proposed predictors, which are imperative tools for biologists. More recently, the PI has successfully applied the GO-based approaches to predicting functional types of membrane proteins. The PI has also written a book [cite the book, 2015] summarizing series of these predictors and approaches. M.W. Mak’s work on machine learning, speaker recognition, and bioinformatics has resulted in 160 publications and 1720 citations (according to Google Scholar). Three Representative Publications: No. Paper Impact

Factor# Grade No. of

Citations* 1 Na Li and M.W. Mak, "SNR-Invariant PLDA Modeling in Nonparametric

Subspace for Robust Speaker Verification", IEEE/ACM Trans. on Audio Speech and Language Processing, vol. 23, no. 10, pp. 1648-1659, Oct. 2015. Remark: This paper is my recent work on speaker recognition. I consider this as my best work in this area. As it was publish in Oct. this year, it does not have citation.

2.604 A 1

2 S.B. Wan, M.W. Mak, and (S.Y. Kung), "mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines", BMC Bioinformatics, 2012, 13:290. Remark: This paper is my best work in bioinformatics. It also receives the Highly Accessed status in BMC Bioinformatics website.

3.452 A 42

3 (S.Y. Kung), M.W. Mak and (S.H. Lin), Biometric Authentication: A Machine Learning Approach, Prentice Hall, 2005. Remark: This book represents my best work in biometrics. It has 476 pages. I also use it for my teaching.

N/A N/A 176

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Research Awards Faculty of Engineering Research Grant Achievement Award, 2003. Faculty of Engineering Research Grant Achievement Award, 2009 Professional Service to the Community Editorship:

1. Associate Editor, IEEE Trans. on Speech and Language Processing (2011 - 2014) 2. Associate Editor, Journal of Signal Processing Systems 3. Associate Editor, Advances in Artificial Neural Systems 4. Associate Editor, Conference Papers in Biology 5. Guest Editor, Special Issue on Machine Learning for Microarray and Sequence Analysis 6. Guest Editor, Special Issue on Advances in Machine Learning and Neural Networks

Technical Programmes:

1. Member of Steering Committee (2015-2018), ISCSLP 2. Area Chair, Interspeech’2014 3. Technical Program Committee Member of a number of conferences and workshops, including Interspeech 2014,

ISCSLP 2014, ICSPCC 2013, IIBM 2012, ICMLC 2008-2012, APSIPA'09-11, IEEE MLSP'05-11, IIBM 2011, ISCSLP'08-11, ICONIP’04, 06, etc.

4. Technical Committee Member, Machine Learning for Signal Processing, IEEE Signal Processing Society, 2005-2007.

5. Vice Chair, ISCSLP’2012 6. Reviewer of a number of journals, including IEEE Trans. on Neural Networks, IEEE Signal Processing Letters,

IEEE Trans. on SMC, IEEE Transactions on Speech and Audio Processing, Speech Communication, Computer Speech and Language, ACM Transactions on Intelligent Systems and Technology, etc.

Contribution to PolyU:

1. Programme Leader of BEng(Hons) in Electronic Engineering (42479) 2. DLTC member 3. DMC member (elected) 4. Departmental Undergraduate Programme Committee (DUPC) member 5. Scholarship Liaison Officer

Consultancy Work 1. Client-Server Computing, 2013 2. Introduction to Information Technologies and Applications, 2012 3. Object-Oriented Design and Programming using Java, 2012 4. Object-Oriented Design and Programming using Java, 2011 5. Object-Oriented Design and Programming using Java, 2010 Software Packages and Demo Systems 1. Speaker Verification System (http://www.eie.polyu.edu.hk/~mwmak/SpeakerVerSys.htm) 2. Sound Detection System (http://www.eie.polyu.edu.hk/~mwmak/SoundDetector.html) 3. Protein subcellular localization Web Servers:

http://bioinfo.eie.polyu.edu.hk/Book_website/servers.html

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Teaching SFQ Scores

Mean SFQ Score: 3.94

Standard deviation: 0.363 Brief Statements: Since 2010, I have developed the materials of four subjects. In the first semester of 2015/16, I have also developed the materials and syllabus of a brand new subject: Multimodal Human Computer Interaction Technologies. In this subject, I include some latest HCI and machine learning technologies (e.g., SVM, deep learning and deep neural networks) so as to equip students with sufficient fundamental knowledge in machine learning. This knowledge is not only useful for HCI but also essential for big data analytics. The elective subjects that I developed also attracted a lot of students to enroll. For example, although the subject EIE424 and EIE4108 are elective subjects, they attract over 80 students. Teaching comprises lectures, tutorials, and lab sessions. Because most of the subjects involve programming, tutorials will also be conducted in lab. Students are requested to develop computer programs to meet some specific requirements. Through these hands-on experiences, students will be equipped with sufficient programming skill when they join the software industry. For the MSc subject, I always bring the materials I obtained from conference attendances to the lectures to let students know what is the current state-of-the-art in the field. I have made use of Blackboard for teaching. Also, I have set up Discussion Forum in Blackboard for students to post questions and share the discussions or my answers. All teaching materials can be downloaded from my website http://www.eie.polyu.edu.hk/~mwmak/myteaching.htm (Username: student Password: learning)

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Feedback From Students

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