Prof. Yew-Soon Ong
IEEE Fellow
Nanyang Technological University, Singapore

Yew-Soon Ong (Fellow of IEEE) received the Ph.D. degree in artificial intelligence in complex design from the University of Southampton, U.K., in 2003. He is President’s Chair Professor in Computer Science at Nanyang Technological University (NTU), and serves as Chief Artificial Intelligence Scientist of the Agency for Science, Technology and Research in Singapore. At NTU, he also serves as co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab. He was Chair of the School of Computer Science and Engineering at NTU from 2016-2018. His research interest is in artificial and computational intelligence, learning and evolution, transfer and multi-task optimization. He is the inaugural Editor-in-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence and serves as associate editor of IEEE TNNLS, IEEE TEVC, IEEE TAI, IEEE Cybernetics and others. He has received several four IEEE outstanding paper awards, Nanyang Education Excellence Award and was listed as a Thomson Reuters highly cited researcher and among the World's Most Influential Scientific Minds. He also volunteers on the Board of Association for Persons with Special Needs (APSN) and the APSN Infocomm Committee since 2019.

Speech Title: Towards ‘General Optimization Intelligence’
Abstract: Traditional Optimization tends to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of classical optimization solvers do not automatically grow with experience. In contrast however, humans routinely make use of a pool of knowledge drawn from past experiences whenever faced with a new task. This is often an effective approach in practice as real-world problems seldom exist in isolation. Similarly, practically useful artificial systems are expected to face a large number of problems in their lifetime, many of which will either be repetitive or share domain-specific similarities. This view naturally motivates advanced optimizers that can replicate human cognitive capabilities, leveraging on lessons learned from the past to accelerate the search towards optimal solutions of never before seen tasks. With the above in mind, this talk aims to shed light on recent research advances in the field of global black-box optimization that champion the general theme of ‘General Optimization Intelligence’. Recent algorithmic developments in transfer, multitask and Bayesian optimization and multifactorial evolutionary algorithm shall be presented.


 

Prof. Kwang-Cheng Chen
IEEE Fellow
University of South Florida, USA

Kwang-Cheng Chen has been a Professor at the Department of Electrical Engineering, University of South Florida, since 2016. From 1987 to 2016, Dr. Chen worked with SSE, Communications Satellite Corp., IBM Thomas J. Watson Research Center, National Tsing Hua University, HP Labs., and National Taiwan University in mobile communications and networks. He visited TU Delft (1998), Aalborg University (2008), Sungkyunkwan University (2013), and Massachusetts Institute of Technology (2012-2013, 2015-2016). He founded a wireless IC design company in 2001, which was acquired by MediaTek Inc. in 2004. He has been actively involving in the organization of various IEEE conferences and serving editorships with a few IEEE journals, together with various IEEE volunteer services to the IEEE, Communications Society, Vehicular Technology Society, and Signal Processing Society, such as founding the Technical Committee on Social Networks in the IEEE Communications Society. Dr. Chen also has contributed essential technology to various international standards, namely IEEE 802 wireless LANs, Bluetooth, LTE and LTE-A, 5G-NR, and ITU-T FG ML5G. He has authored and co-authored over 300 IEEE publications, 4 books published by Wiley and River (most recently, Artificial Intelligence in Wireless Robotics, 2020), and more than 24 granted US patents. Dr. Chen is an IEEE Fellow and has received a number of awards including 2011 IEEE COMSOC WTC Recognition Award, 2014 IEEE Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP Outstanding Paper Award. Dr. Chen’s current research interests include wireless networks, quantum communications and computing, IoT/CPS, multi-agent systems and social networks, and cybersecurity.


Speech Title: From Quantum Computing to Post-Quantum Cryptographic Systems
Abstract: Quantum entanglement that puzzled great minds like Einstein enables the extreme parallelism of quantum computing that makes many problems of high computational complexity feasible. Starting from Dirac formulation of quantum mechanics and quantum logic-gate based quantum computer, quantum algorithms will be introduced, particularly the Shor’s algorithm to effectively attack the prime factorization that serves the foundation of modern public key cryptography and infrastructure. In light of such quantum computing threats to classic cryptography, post-quantum cryptography (PQC) emerges as a very wanted technology. In July 2022, NIST announced the first wave of selections and further candidates for PQC algorithms. The mathematical principles and engineering perspectives to design post-quantum cryptographic systems will be therefore presented toward the foundation of future information communication technology.


 

Prof. Zhongfei Zhang
IEEE Fellow
Binghamton University, State University of New York, USA

Zhongfei Zhang is a professor at Computer Science Department, Binghamton University, State University of New York (SUNY), USA. He received a B.S. in Electronics Engineering (with Honors), an M.S. in Information Sciences, both from Zhejiang University, China, and a PhD in Computer Science from the University of Massachusetts at Amherst, USA. His research interests are in the broad areas of machine learning, data mining, computer vision, and pattern recognition, and specifically focus on multimedia/multimodal data understanding and mining. He was on the faculty of Computer Science and Engineering at SUNY Buffalo, before he joined the faculty of Computer Science at SUNY Binghamton. He is the author or co-author of the very first monograph on multimedia data mining and the very first monograph on relational data clustering. He has published over 200 papers in the premier venues in his areas. He holds more than twenty inventions, has served as members of organization committees of several premier international conferences in his areas including general co-chair and lead program chair, and as editorial board members for several international journals. He served as a French CNRS Chair Professor of Computer Science at the University of Lille 1 in France, a JSPS Fellow in Chuo University, Japan, a QiuShi Chair Professor in Zhejiang University, China, as well as visiting professorships from many universities and research labs in the world when he was on leave from Binghamton University years ago. He received many honors including SUNY Chancellor’s Award for Scholarship and Creative Activities, SUNY Chancellor’s Promising Inventor Award, and best paper awards from several premier conferences in his areas. He is a Fellow of IEEE, IAPR, and AAIA.

Speech Title: On Knowledge Distillation
Abstract: Learning with knowledge distillation is a hot topic in today’s machine learning literature, with the motivation of equipping a student, typically much lighter-weight network with the knowledge from a teacher, typically much larger network. This research has a wide spectrum of real-world applications of developing powerful but light-weight and resource-limited front end devices. In this talk, I will address several interesting and important questions on the further studies of knowledge distillation on top of the existing literature, with the ultimate goal of substantially improving the learning effectiveness of knowledge distillation.