 | Prof. Yi PanShenzhen University of Advanced Technology, China Dr. Yi Pan is the Founding Dean and Chair Professor of the Faculty of Computer Science and Control Engineering at Shenzhen University of Advanced Technology; Chief Scientist of the High Performance Computing Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Director of Guangdong Key Laboratory for Intelligent Analysis of Biomedical Big Data and Director of Shenzhen Key Laboratory of Intelligent Bioinformatics. He is Fellow of the American Institute for Medical and Biological Engineering, Fellow of the US National Academy of Artificial Intelligence, Member of the European Academy of Sciences and Arts, Foreign Member of the Russian Academy of Engineering, Foreign Member of the Ukrainian Academy of Engineering Sciences, and Fellow of the Royal Society for Public Health. He is also a Distinguished Fellow of the International Engineering and Technology Institute, Fellow of the Institution of Engineering and Technology, Fellow of Asia-Pacific Artificial Intelligence Association, Fellow of Asian Computational Intelligence Society, Fellow of International Artificial Intelligence Industry Alliance, Fellow of the Japan Society for the Promotion of Science, Yangtze River Scholar, and National Distinguished Expert. He has been selected as one of the world’s top 0.05% scholars and included in the list of the world’s top 1000 computer scientists. He was ranked the world’s No. 4 top scholar in computational biology over the past five years by ScholarGPS. His academic works have been cited over 30,000 times with a current h-index of 106. |
| Prof. Gang WangKagoshima University, Japan Dr. Gang Wang is a Professor in the Department of Information Science and Biomedical Engineering at Kagoshima University, Japan. His research focuses on the neural mechanisms of visual information processing, object recognition, and brain-inspired artificial intelligence. Using techniques such as intrinsic signal optical imaging, electrophysiology, and machine learning, he investigates how cortical neural activity represents visual objects and categories. His recent work explores the application of artificial intelligence to neural data analysis and the decoding of visual information from brain activity. He has authored numerous publications in the fields of neuroscience and computational intelligence. Speech Title: Neural mechanism underlying object recognition Abstract: Object recognition is one of the fundamental functions of both biological and artificial intelligence systems. In the brain, visual information is processed through hierarchical cortical pathways that progressively transform simple visual features into invariant object representations. Recent advances in neuroscience have revealed important mechanisms underlying object recognition, including population coding, recurrent processing, predictive coding, and large-scale neural dynamics. At the same time, deep neural networks have achieved remarkable success in computer vision and have provided new opportunities to compare biological and artificial systems. This talk introduces current understanding of the neural mechanisms of object recognition and discusses how neuroscience and artificial intelligence are increasingly influencing each other. Particular emphasis will be placed on hierarchical visual processing, similarities and differences between brains and deep learning models. |
 | Prof. Jie XuUniversity of Leeds, UK He is the leader for a Research Peak of Excellence at Leeds, the director of the EPSRC-funded White Rose Grid e-Science Centre, involving the three White Rose Universities of Leeds, Sheffield and York, and the head of the Distributed Systems and Services (DSS) Theme (with a total of over 40 theme members) at Leeds. He has worked in the field of Distributed Computing Systems for over thirty-five years, engaging closely with industrial leaders such as Rolls-Royce, BAE Systems, and JLR. He is an executive member of UKCRC (UK Computing Research Committee) and a Turing Fellow in AI and Data Science. He has served as an academic expert for numerous governments and industries, such as Singapore IDA, Lenovo, UK EPSRC, and UK DTI (InnovateUK). In addition, he has extensive editorial experience, having served as an editor for IEEE Distributed Systems from 2000 to 2005, and currently acting as an associate editor of IEEE Transactions on Parallel and Distributed Systems and ACM Computing Surveys. His main research interests are on massive-scale distributed computing systems, complex resource management for Edge-Cloud computing, and data-centric system engineering, focusing on investigating, designing and implementing system components and mechanisms to tackle performance, efficiency, dependability and cost-effectiveness challenges and manage their trade-offs. He is also working on system and software dependability, including software fault tolerance, and rapid error recovery in Cloud data centres. |