Biography: Simon X. Yang received the B.Sc. degree in Engineering Physics from Peking University, Beijing, China, the first of two M.Sc. degrees in Biophysics from the Chinese Academy of Sciences, Beijing, the second M.Sc. degree in Electrical Engineering from the University of Houston, Houston, TX, and the Ph.D. degree in Electrical and Computer Engineering from the University of Alberta, Edmonton, AB, Canada. Dr. Yang is currently a Professor and the Head of the Advanced Robotics and Intelligent Systems Laboratory at the University of Guelph, Guelph, ON, Canada. He is a Fellow of Canadian Academy of Engineering.
Prof. Yang’s research interests include robotics, intelligent systems, control systems, sensors and multi-sensor fusion, wireless sensor networks, bio-inspired intelligence, neural networks, machine learning, fuzzy systems, evolutionary computation, intelligent agriculture, intelligent communications, intelligent transportation, and computational neuroscience. Prof. Yang has been very active in professional activities. He has been served as the Editor-in-Chief and Associate Editor of several international journals, and has involved in the organization of many international conferences. He was on the grant selection panel of NSERC, CIHR and several other grant agencies.
Biography: Graziano Chesi is a full professor at the Department of Electrical and Electronic Engineering of the University of Hong Kong. He received the Laurea in Information Engineering from the University of Florence and the PhD in Systems Engineering from the University of Bologna. He served as associate editor for various journals, including Automatica, the European Journal of Control, the IEEE Control Systems Letters, the IEEE Transactions on Automatic Control, the IEEE Transactions on Computational Biology and Bioinformatics, and Systems and Control Letters. He founded the Technical Committee on Systems with Uncertainty of the IEEE Control Systems Society. He also served as chair of the Best Student Paper Award Committees of the IEEE Conference on Decision and Control and the IEEE Multi-Conference on Systems and Control. He authored the books "Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems" and "Domain of Attraction: Analysis and Control via SOS Programming". He is a Fellow of the IEEE, AAIA and AIIA.
Biography: Prof. Liang Song has been a Chair Professor at Fudan University. He also concurrently serves as President of the International Institute on Networking Systems of AI, Chairman of the Shanghai 5G-VR Industry Alliance, Chief Scientist for multiple listed companies, among other services. Prof. Song’s work integrates artificial intelligence with communication networks within practical communication application systems to empower a wide range of industries. It enables online evolutive learning and real-time sensing and control in intelligent networking systems, serving as a vital pathway to constructing full-scenario, universal Networked AI.
Speech Title: Networked AI: A Leap in Data and Intelligence Efficiency
Abstract: Through an analysis of cutting-edge research in Networking Systems of AI (NSAI), we explore the evolving direction and key challenges of shifting from end-to-end large models to networked, distributed, and autonomous intelligence. The presentation highlights the benefits that Networked AI offers in data and intelligence efficiency compared to end-to-end models. It also discusses its applications in pioneering fields such as embodied intelligence, spatial intelligence, and industrial intelligence.
Biography: Ir Prof Pai Zheng is currently an Associate Professor, Wong Tit-Shing Endowed Young Scholar in Smart Robotics, and Director, National Centre of Technology Innovation for Intelligent Design and Numerical Control (NCDC) – Hong Kong Branch, in the Department of Industrial and Systems Engineering, at The Hong Kong Polytechnic University. His research interests include human-robot collaboration, smart product-service systems, and industrial embodied intelligence. Dr Zheng is a recipient of the Clarivate High Cited Researchers (2025 - ), HKIE Young Engineer of the Year Award (2025), SME Outstanding Young Manufacturing Engineers Award (2024), NSFC Excellent Young Scientist Fund (2024), and Global Top 50 AI+X Chinese Scholars by Baidu (2022). He is a Fellow of ASME, Young Member of HKAE, Senior Member of IEEE/CMES, and Member of HKIE/SME, Associate Member of CIRP, and serves as the Chair of IEEE Hong Kong Section SMC Chapter, Co-chair of IEEE TC on DMHCA, Scientific Committee Member of SME | NAMRI, Associate Editor of Journal of Manufacturing Systems, IEEE Transactions on Automation Science and Engineering, Journal of Intelligent Manufacturing, and International Journal of Interactive Design & Manufacturing.
Biography: Kei Fujisawa is an Associate Professor at Yokohama National University, Japan. His research focuses on intelligent condition monitoring, machine vision, and data-driven modeling for industrial systems. His recent work includes machine-vision-based observation and transition detection of transient abrasive wear, cloud-based image analysis using structure-tensor coherence, and high-speed liquid impingement erosion. His research aims to integrate imaging, state estimation, and data-driven models to provide interpretable and practical tools for online condition monitoring and predictive maintenance. At ICCR 2026, he will present recent advances in intelligent condition monitoring through machine vision and data-driven modeling.
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