James Tin-Yau KWOK () Professor Computer Science and Engineering

Biography

James Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He is an IEEE Fellow.

Prof Kwok received his B.Sc. degree in Electrical and Electronic Engineering from the University of Hong Kong and his Ph.D. degree in computer science from the Hong Kong University of Science and Technology. He then joined the Department of Computer Science, Hong Kong Baptist University as an Assistant Professor. He returned to the Hong Kong University of Science and Technology and is now a Professor in the Department of Computer Science and Engineering. He is serving as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, Neural Networks, Neurocomputing, Artificial Intelligence Journal, International Journal of Data Science and Analytics, and on the Editorial Board of Machine Learning. He is also serving / served as Senior Area Chairs of major machine learning / AI conferences including NeurIPS, ICML, ICLR, IJCAI, and as Area Chairs of conferences including AAAI and ECML. He is on the IJCAI Board of Trustees. He is recognized as the Most Influential Scholar Award Honorable Mention for "outstanding and vibrant contributions to the field of AAAI/IJCAI between 2009 and 2019". Prof Kwok will be the IJCAI-2025 Program Chair.

Research

Current Research Interests

Machine learning, Deep learning, Kernel methods, Artificial intelligence

Applications: Computer vision and image processing, speech processing, pervasive computing

List of publications (Google scholar)

Awards

Best Teaching Award, MSc Program in Big Data Technology, 2021-2022

Best Paper Runner-Up, Applied Research Track, International Conference on Information and Knowledge Management (CIKM), 2022

Best Review Award, International Conference on Learning Representations (ICLR), 2017

Nomination for the Best Paper Award, SIAM International Conference on Data Mining (SDM), 2010.

Second Class Award in Natural Sciences by the Ministry of Education, People's Republic of China, 2008

IEEE Transactions on Neural Networks Outstanding Paper Award, 2004

Student Supervision Achievements

Quanming Yao, First Runner-up, Ph.D. Research Excellence (HKUST Engineering School), 2018-2019

Quanming Yao, Google PhD Fellowship in Machine Learning, 2016.

Wei Bi, Google Anita Borg Fellowship, 2014.

Wei Bi, Google PhD Fellowship in Machine Learning, 2013.

Ivor Tsang, Best Paper Award, IEEE Hong Kong Chapter of Signal Processing Postgraduate Forum, 2006.

Ivor Tsang, Microsoft Fellowship, 2005.

Current Students

Weiyu Chen (PhD student)

Yunhao Gou (PhD student)

Weisen Jiang (PhD student)

Zhili Liu (PhD student)

Lifeng Shen (PhD student)

Yanbin Wei (PhD student)

Hansi Yang (PhD student)

Jincheng Yu (PhD student)

Runsheng Yu (PhD student)

Nai Chit Fung (MPhil student)

Former Students

Han Shi (PhD 2022; Huawei Noah's Ark Lab, China)

Shuai Zheng (PhD 2019; Applied Scientist at Amazon Web Services, USA)

Lu Hou (PhD 2019; Huawei Noah's Ark Lab, China)

Yaqing Wang (PhD 2019; co-supervised with Lionel Ni; Baidu Research, China)

Quanming Yao (PhD 2018; Assistant Professor, Tsinghua University, China)

Wei Bi (PhD 2015; Tencent AI Lab, China)

Leon Wenliang Zhong (PhD 2014; Ant Financial, China)

Kai Zhang (PhD 2008; Associate Professor, Temple University, USA)

Ivor Tsang (PhD 2007, MPhil 2003; Professor, University of Technology Sydney, Sydney, Australia)

Lawrence Chan (MPhil 2021)

Minsam Kim (MPhil 2020)

Zachary William Wellmer (MPhil 2019)

Xiawei Guo (MPhil 2017)

Yihai Huang (MPhil 2015)

Pak-Ming Cheung (MPhil 2006; Microsoft (Bing), Seattle, USA)

Calvin Chu (MPhil 2005; Hong Kong Bank, Hong Kong)

Teaching

MSBD5008: Introduction to Social Computing

MSBD5012: Machine Learning

MFIT5004: Financial Data Mining

SBMT5710: Artificial Intelligence (for MBA)

COMP171: Data Structures and Algorithms

COMP271: Design and Analysis of Algorithms

COMP327: Introduction to Pattern Recognition

COMP344: Image Processing

COMP3211: Introduction to Artificial Intelligence

COMP4211: Machine Learning (UG)

COMP4331: Introduction to Data Mining

COMP4641: Social Information Networks Analysis and Engineering

COMP5212: Machine Learning (PG)

COMP621I: Advanced Topics in Machine Learning

COMP621O: Kernel Methods in Machine Learning

Other Links

Matrix Reference Manual, MathWorld, Wikipedia, Alpha, Google, Lbsearch

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