Prof. Simon X. Yang

University of Guelph, Canada

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.

Assoc. Prof. FUJISAWA Kei

Yokohama National University, Japan

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.