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Prof. Shengyong Chen (Fellow of IET) Tianjin University of Technology, China Prof. Shengyong Chen received the Ph.D. degree from City University of Hong Kong in 2003. He worked as a guest researcher at University of Hamburg, Germany, where he received a fellowship from the Alexander von Humboldt Foundation in 2006. He was a visiting professor at Imperial College London, from 2008 to 2009. He is currently a full Professor and Vice-President in Tianjin University of Technology. He is an IET Fellow and an IEEE senior member. His research interests include machine vision and robotics. He received the National Outstanding Youth Foundation Award of NSFC. He has applied over 100 patents and published over 400 scientific papers, including 100 in IEEE Transactions, and 5 Best Paper Awards from international organizations. His work received over 15000 citations in Google Scholar. Speech Title: "Intelligent Computing for Smart Healthcare with Medical Images and Signals" Abstract: The talk presents a brief overview of Smart Healthcare, focusing on the understanding of medical images and signals. It first discusses the current state of healthcare, highlighting challenges and the need for an intelligent system. The report then delves into medical image understanding, emphasizing the importance of AI-driven techniques for diagnosis and prediction using advanced imaging modalities. Key topics include microscopic image analysis of blood cells, clump cells in pleural fluid, and cancer cells, as well as 3D analysis of cerebral vasculature and abdominal aortic aneurysms. The report also touches upon retinal image analysis for microaneurysm detection and surgical navigation techniques for abdominal aortic aneurysm surgeries. In all, the report emphasizes the potential of data-driven and AI-assisted approaches in enhancing diagnostic accuracy, facilitating early disease detection, and personalizing healthcare services.
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. Xindong Wu (Fellow of IEEE/AAAS, H-index: 87) Hefei University of Technology, China Xindong Wu is Director and Professor of the Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, and also Chief Scientist at the CEC Data Industry Group, China. His research interests include big data analytics, data mining and knowledge engineering. He received his Bachelor's and Master's degrees in Computer Science from the Hefei University of Technology, China, and his Ph.D. degree in Artificial Intelligence from the University of Edinburgh, Britain. He is a Foreign Member of the Russian Academy of Engineering, and a Fellow of IEEE and the AAAS (American Association for the Advancement of Science). Dr. Wu is the Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), and the Editor in-Chief of Knowledge and Information Systems (KAIS, by Springer). He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE) between 2005 and 2008 and Co-Editor-in-Chief of the ACM Transactions on Knowledge Discovery from Data Engineering between 2017 and 2020. He served as a program committee chair/co-chair for ICDM 2003 (the 3rd IEEE International Conference on Data Mining), KDD 2007 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), and ICBK 2017 (the 8th IEEE International Conference on Big Knowledge). Speech Title: "Unifying Large Language Models and Knowledge Graphs with a Knowledge Ocean" Abstract: Large language models (LLMs), such as ChatGPT and GPT4o, are making new waves in the fields of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs), Wikipedia, and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolve by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and, simultaneously, leverage their advantages. In this talk, we present CHACE-KO (a Connected, Hybrid, Accommodating, Contained, and Evolving Knowledge-Ocean, https://ko.zhonghuapu.com/EN) that synergizes knowledge graphs with large language models, and performs bidirectional reasoning driven by both data and knowledge.
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