Prof. WANG Jun
City University of Hong Kong, Hong Kong
Fellow of IEEE & IAPR
Jun Wang is a Chair Professor of Computational Intelligence in the Department of Computer Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and Chinese University of Hong Kong. He also held various part-time visiting positions at US Air Force Armstrong Laboratory, RIKEN Brain Science Institute, Huazhong University of Science and Technology, Dalian University of Technology, and Shanghai Jiao Tong University as a Changjiang Chair Professor. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published about 200 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial advisory board of International Journal of Neural Systems (2006-2013), and a member of the editorial board of Neural Networks (2012-2014) as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014, 2016), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.
Ying Tan is a professor of Peking University, and director of Computational Intelligence Laboratory at Peking University. He worked at Faculty of Design, Kyushu University, Japan, as a professor, and at Columbia University as senior research fellow and at Chinese University of Hong Kong in 1999 and 2004-2005 as a research associate/fellow, and at University of Science and Technology of China in 1998, 2005-2006 as a professor under the 100-talent program of CAS, etc. He is the inventor of Fireworks Algorithm (FWA). He serves as the Editor-in-Chief of International Journal of Computational Intelligence and Pattern Recognition (IJCIPR), the Associate Editor of IEEE Transactions on Evolutionary Computation (TEC), IEEE Transactions on Cybernetics (CYB), International Journal of Swarm Intelligence Research (IJSIR), International Journal of Artificial Intelligence (IJAI), etc. He also served as an Editor of Springer’s Lecture Notes on Computer Science (LNCS) for 32+ volumes, and Guest Editors of several referred Journals, including IEEE/ACM Transactions on Computational Biology and Bioinformatics, Information Science, Neurocomputing, Natural Computing, Swarm and Evolutionary Optimization, etc. He is a senior member of IEEE. He is the founder general chair of the ICSI International Conference series since 2010 and the DMBD conference series since 2016. He won the 2nd-Class Natural Science Award of China in 2009 and many best paper awards. His research interests include computational intelligence, swarm intelligence, swarm robotics, data mining, machine learning, intelligent information processing for information security and financial prediction, etc. He has published more than 300+ papers in refereed journals and conferences in these areas, and authored/co-authored 12 books, including “Fireworks Algorithm” by Springer-Nature in 2015, and “GPU-based Parallel Implementation of Swarm Intelligence Algorithms” by Morgan Kaufmann (Elsevier) in 2016, and 28 chapters in book, and received 4 invention patents.
Prof. Yao Liang
Indiana University-Purdue University Indianapolis, United States
Yao Liang received his B.S. degree in Computer Engineering and M.S. degree in Computer Science from Xi’an Jiaotong University, Xi’an, China. He received his Ph.D. degree in Computer Science from Clemson University, Clemson, USA, in 1997.
He is currently a Professor in the Department of Computer and Information Science, Purdue University School of Science, Indiana University Purdue University, Indianapolis (IUPUI), USA. His research interests include wireless sensor networks, Internet of Things, cyberinfrastructure, multimedia networking, adaptive network control and management, machine learning, neural networks, data mining, data fusion, data management and integration, and distributed systems. His research projects have been funded by NSF. Prior to joining IUPUI, he was on the faculty of Department of Electrical and Computer Engineering at Virginia Tech, USA. He also had extensive industrial R&D experiences as a Technical Staff Member in Alcatel USA. Dr. Liang has published numerous papers on various prestigious journals and international conferences, and received two US patents. He has served regularly on Program Committees for various major international conferences, and served as a reviewer for numerous prestigious journals. Dr. Liang has given invited talks and lectures at various universities in US, Europe and China. He is a Senior Member of IEEE, and a Member of ACM.
Learning: A 20-Year Perspective
Abstract--In machine learning field, one of the most crucial challenges for modeling is the generalization. Multiresolution learning paradigm has been introduced more than 20 years ago as a systematic approach to improve the generalization of neural network modeling. In this talk, I will present and review the original idea and work of multiresolution learning, its continuing development and extension, and its applications. I will provide insights on why the multiresolution learning offers a novel and systematic paradigm for constructing predictive modeling with significantly improved generalization performance for both regression and classification problems. We show that the introduced paradigm is very general and can be applied to tasks with either signal-based input space or feature-based input space. Applications in various domains are given to illustrate the underlying idea of the multiresolution learning and to demonstrate its superior performance in models’ generalization, especially for very difficult tasks.