ICAIBD 2020 | Chengdu, China | May 28-31, 2020

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Keynote Speakers 主题报告专家


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.

Prof. Huajin Tang
ZheJiang University, China

Huajin Tang received the B.Eng. degree from Zhejiang University, China in 1998, received the M.Eng. degree from Shanghai Jiao Tong University, China in 2001, and received the Ph.D. degree from the National University of Singapore, in 2005. He was an R&D engineer with STMicroelectronics, Singapore from 2004 to 2006. From 2006 to 2008, he was a Post-Doctoral Fellow with the Queensland Brain Institute, University of Queensland, Australia. He was Head of the Robotic Cognition Lab at Institute for Infocomm Research, Singapore from 2008 to 2015. Since 2014 he is a professor with Sichuan University. He is currently a professor with Zhejiang University, China. His research interests include neuromorphic computing, neuromorphic hardware and cognitive systems, robotic cognition, etc. His research work on Brain GPS has been reported by MIT Technology Review in 2015. He received 2011 Role Model Award of Institute for Infocomm Research Singapore, 2016 IEEE Trans. on Neural Networks and Learning Systems Outstanding Paper Award, 2019 IEEE Computational Intelligence Magazine Outstanding Paper Award. He has served as an Associate Editor of IEEE Trans. on Neural Networks and Learning Systems, IEEE Trans. on Cognitive and Developmental Systems and Frontiers in Neuromorphic Engineering, and Neural Networks (2020-). He was the Program Chair or General Chair for IEEE CIS-RAM (2015, 2017), ISNN 2019 and IEEE Symposium on Neuromorphic Cognitive Computing. From 2019 he is elected as a Board-of-Governor member of International Neural Network Society (INNS).

 

Invited Speaker 特邀报告专家

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.

Prof. Zhiwei Zhu
Purdue University, United States

With 20 years of industry practice experiences, served as
• Senior Vice President of Consumer Data and Analytics at Swiss Re (A Global Leader),
• Vice President of Risk Modeling and Analytics at SCOR Global Life (A Global Leader),
• Vice President of Advanced Analytics at Assurant Health (A S&P 500 company),
Professor Zhu is currently sharing (teaching) his Data Science related application, learning, and vision with cross-disciplinary researchers and future business leaders (business school students) at Purdue University’s Krannert School of Management.
Prior to his industry adventure, Professor Zhu taught in Mathematics, Statistics, and Business departments at universities in the US and in China. He collaborated with researchers in Medical School, College of Education, and government agencies, with numerous publications.
Professor Zhu earned his Ph.D. degree in Statistics from Michigan State University and Master’s degree in Mathematics from the Central South University (China). He is a frequent speaker or panelist on data science strategy and application in industry and academic venues.

Speech Title: A Data Science Trajectory in the Artificial Intelligence Era
Abstract: As the popularity of Big Data and Artificial Intelligence rises, so does the complexity of defining, specifying, and clutching them. Experts from different fields, such as a data scientist, a computer engineer, and a business executive, are likely to have varying perspectives and descriptions. Because businesses are the ultimate value generators for new concepts or technology, in this discussion, I’ll first present an insightful business view of Data Science’s role in empowering Big Data and AI and how they together will inevitably alter a current insurance business model. And then exhibit some of the demands, challenges, and opportunities coming with this type of alternations, which is applicable to almost any industry and benefiting Big Data and Artificial Intelligence related researchers in identifying and targeting applications of their works.