C. L. Philip Chen

Character introduction

C. L. Philip Chen is the Chair Professor and Dean of the College of Computer Science and Engineering, South China University of Technology. Being a Program Evaluator of the Accreditation Board of Engineering and Technology Education (ABET) in the U.S., for computer engineering, electrical engineering, and software engineering programs, he successfully architects the University of Macau’s Engineering and Computer Science programs receiving accreditations from Washington/Seoul Accord through Hong Kong Institute of Engineers (HKIE), of which is considered as his utmost contribution in engineering/computer science education for Macau as the former Dean of the Faculty of Science and Technology. He is a Fellow of IEEE, AAAS, IAPR, CAA, and HKIE; a member of Academia Europaea (AE), European Academy of Sciences and Arts (EASA), and International Academy of Systems and Cybernetics Science (IASCYS). He received IEEE Norbert Wiener Award in 2018 for his contribution in systems and cybernetics, and machine learnings. He is also a  highly cited researcher in 2018 and 2019 by Clarivate Analytics.
His current research interests include systems, cybernetics, and computational intelligence. Dr. Chen was a recipient of the 2016 Outstanding Electrical and Computer Engineers Award from his alma mater, Purdue University, after he graduated from the University of Michigan at Ann Arbor, Ann Arbor, MI, USA in 1985. He was the IEEE Systems, Man, and Cybernetics Society President from 2012 to 2013, and currently, he is the Editor-in-Chief of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, and an Associate Editor of the IEEE Transactions on Fuzzy Systems, and IEEE Transactions on Cybernetics. Currently, he is a Vice President of Chinese Association of Automation (CAA).

Topic: New Technologies and Trends in Artificial Intelligence: Computational Algorithms, Data Modelling, and Applications

Abstract  This talk will review new technologies and trends in artificial intelligence (AI) from algorithmic, data modeling, and applications perspective. Subjects include algorithms and technologies being developed from specific AI to general AI, explainable AI, small sample learning and zero-shot learning, non-deep-structure learning, and computational brain science. Also, design of reliable  and robust AI algorithms and its applications will be discussed.  



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