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Prof. Zhen Wang (Fellow of IEEE/AAIA/IOP) Northwestern Polytechnical University, China Zhen Wang is a Distinguished Professor at Northwestern Polytechnical University (NPU), China, the Chair of School of Cybersecurity, an elected member of Academia Europaea/The Academy of Europe (AE), European Academy of Sciences and Arts (EASA), the National Science Fund of Distinguished Young Scholars, and a Fellow of IEEE/AAIA/IOP, a Highly Cited Researcher ranked by Clarivate Analytics. Focusing on artificial intelligence, multi-agent games, behavior patterns, he has published more than 100 papers, including PNAS, Nature Communications, Science Advances, Physical Review Letters, IEEE TPAMI, IEEE TNNLS, IEEE TCYB, IEEE TKDE, WWW, IJCAI, AAAI, NeurIPs, ICLR, ICML with total 28700 citations and H-index 69. Prof. Wang obtained the National Innovation Medal, National Five-ONE Medal, XPLORER Prize, and won the Most Downloaded Articles, the Most Cited Articles with Elsevier and Nature Publishing Group journals. He also serves as the editors of 10 scientific journals. Speech Title: "On the Advancement of Multi-Agent Games: From Theoretical and Experimental Perspectives" Abstract: One of the most elusive scientific challenges for over 150 years has been to explain why cooperation survives despite being a seemingly inferior strategy from an evolutionary point of view. Over the years, various theoretical scenarios aimed at solving the evolutionary puzzle of cooperation have been proposed, eventually identifying several cooperation-promoting mechanisms. Here, we will systematically survey the recent theoretical research combining game theory and reinforcement learning. In addition, we will also explore how human behaviors evolve in multi-agent games (including human-robot games).
Prof. Dongrui Wu (Fellow of IEEE, H-index: 60) Huazhong University of Science and Technology, China Dongrui Wu (IEEE Fellow) received a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Professor at School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. Prof. Wu's research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (13000+ Google Scholar citations; h=60). He received the IEEE Computational Intelligence Society Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, the Chinese Association of Automation (CAA) Early Career Award in 2021, the Ministry of Education Young Scientist Award in 2022, and First Prize of the CAA Natural Science Award in 2023. His team won National Champion of the China Brain-Computer Interface Competition in two successive years (2021-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems. Speech Title: "Active Learning for Affective Computing" Abstract: Due to the subtleness, uncertainty and diversity of emotions, it is very challenging to label emotions, impacting the performance of emotion recognition models in affective computing. Active learning studies how to effectively select the most beneficial unlabeled samples to label, so that better learning performance can be obtained from a small number of labeled samples. It is very suitable for affective computing. This talk will introduce advances of active learning in affective computing.
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