Keynote Speakers 专家报告(2020)

Prof. Benjamin W. Wah
The Chinese University of Hong Kong, Hong Kong
Fellow of the American Association for the Advancement of Science

Fellow of 
IEEE; Fellow of the Association for Computing Machinery

Wah was born in Hong Kong and graduated from Queen Elizabeth School, Hong Kong. He received his BS and MS in Electrical Engineering and Computer Science from Columbia University, USA, then furthered his studies at the University of California, Berkeley, obtaining an MS in Computer Science and a PhD in Databases.[1] Wah began his teaching career in Purdue University in 1979. He later joined the University of Illinois, Urbana-Champaign, in 1985, which he served until his retirement at the end of 2011.

In 1985-2011, he was the Franklin W. Woeltge Endowed Professor of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign, USA. In 2008-2009, he also served as Director of the Advanced Digital Sciences Center in Singapore, a US$50 million research center established by the University of Illinois in Singapore in collaboration with the Singapore government's Agency for ScienceTechnology and Research. In 1998–1999, Wah was Chair Professor of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK), and in that year received an Exemplary Teaching Award. From 1999 to 2003, he served as Adjunct Professor in the Department of Computer Science and Engineering at CUHK. Between 2009-2019, he served as Provost of the Chinese University of Hong Kong.

Wah is an expert on non-linear programmingmultimedia signal processing and artificial intelligence. He has published numerous research articles in top professional journals, such as Artificial Intelligence, IEEE Trans. in Computers, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Multimedia, IEEE Trans. on Parallel and Distributed Technology, IEEE Trans. on Software Engineering, Journal of Global Optimization, Journal of Artificial Intelligence Research. He is also the author of two books, and Editor-in-Chief of Wiley's Encyclopedia of Computer Science and Engineering (published in 2008), and has contributed to many edited books and book chapters. He has served on many journal editorial boards.

He also holds many Endowed Professorships and Honorary Professorships in leading universities in the United States of America and in Asia. Professor Wah was elected President of IEEE Computer Society in 2001. He was a member of the Research Grants Council of Hong Kong between 2005 and 2009 and Chairman of its Engineering Panel between 2006 and 2009. He has been a member of the HK Research Grants Council since 2011.

Professor Wah has received numerous honors and awards for his distinguished academic and professional achievements, including the Tsutomu Kanai Award, the W. Wallace McDowell Award, and the Richard E. Merwin Distinguished Service Award, all from the IEEE Computer Society, the Pan Wen Yuan Foundation Outstanding Research Award, and the IEEE Third Millennium Medal. In 2011, he received the Distinguished Alumni Award in computer science from the University of California, Berkeley. He has been elected: Fellow of the American Association for the Advancement of Science, Fellow of IEEE, Fellow of the Association for Computing Machinery.

Prof. Dacheng Tao,
The University of Sydney, Australia

BEng (USTC)| MPhil (CUHK) |PhD (London)
IEEE Fellow, ACM Fellow, AAAS Fellow

Dacheng Tao is Professor of Computer Science and ARC Laureate Fellow in the School of Computer Science and the Faculty of Engineering, and the Inaugural Director of the UBTECH Sydney Artificial Intelligence Centre, at The University of Sydney. His research results in artificial intelligence have expounded in one monograph and 200+ publications at prestigious journals and prominent conferences, such as IEEE TPAMI, TIP, TNNLS, TCYB, TEC, IJCV, JMLR, AIJ, AAAI, IJCAI, NeurIPS, ICML, CVPR, ICCV, ECCV, ICDM, and KDD, with several best paper awards. He received the 2018 IEEE ICDM Research Contributions Award and the 2015 Australian Museum Scopus-Eureka prize. He is a Fellow of the IEEE, AAAS, ACM and Australian Academy of Science.

Prof. Xiaodong LI, IEEE Fellow,
RMIT University, Melbourne, Australia

Bio: Xiaodong Li (M’03-SM’07-Fellow’20) received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. He is a Professor with the School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia. His research interests include machine learning, evolutionary computation, neural networks, data analytics, multiobjective optimization, multimodal optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award". He is an IEEE Fellow.

Speech Title: Seeking multiple solutions: multi-modal optimisation using niching methods
Abstract: Population or single solution search-based optimization algorithms (i.e., meta-heuristics) in their original forms are usually designed for locating a single global solution. Representative examples include among others evolutionary and swarm intelligence algorithms. These search algorithms typically converge to a single solution because of the global selection scheme used. Nevertheless, many real-world problems are "multi-modal" by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate many such satisfactory solutions, or even all of them, so that a decision maker can choose one that is most proper in his/her problem context. Numerous techniques have been developed in the past for locating multiple optima (global and/or local). These techniques are commonly referred to as "niching" methods, e.g., crowding, fitness sharing, derating, restricted tournament selection, clearing, speciation, etc. In more recent times, niching methods have also been developed for meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE). In this talk I will introduce niching methods, including its historical background, the motivation of employing niching in EAs, and the challenges in applying it to solving real-world problems. I will also describe a niching competition series run annually by the IEEE CIS Taskforce on Multimodal Optimization. Niching methods can be applied for effective handling of a wide range of problems including static and dynamic optimization, multiobjective optimization, clustering, feature selection, and machine learning. I will provide several such examples of solving real-world multimodal optimization problems.