Prof. Yi Pan

Shenzhen 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 Wang

Kagoshima 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 Xu

University of Leeds, UK

Jie Xu is Chair of Computing at the University of Leeds, Director of the UK White Rose Grid e-Science Centre, involving the three White Rose Universities of Leeds, Sheffield and York, a co-Leader of the EPSRC-funded UK National Hub in Clouds and Distributed Computing, and Head of the Distributed Systems and Services (DSS) Theme at Leeds. Xu has worked in the field of Distributed Computing Systems for over forty years, engaging closely with industrial leaders in the field. He received a PhD in Computing Science from the University of Newcastle upon Tyne, and was Professor of Distributed Systems at the University of Durham before joined Leeds in 2003.

Professor Xu 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, UK DTI (InnovateUK), and Research Ireland. 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. Professor Xu is currently the Steering Committee Chair of IEEE ISADS, a Steering Committee member for several IEEE conferences, such as SRDS, ISORC, HASE, SOSE, JCC, and CISOSE, as well as serving on the steering board of IEEE TC on BIS. He has also been a General Chair/PC Chair for various IEEE international conferences. With over 300 academic publications, including papers in top-ranked IEEE and ACM Transactions, Professor Xu has received international research prizes, such as the BCS/AT&T Brendan Murphy Prize and EU HiPEAC Transfer Award 2025, and led or co-led more than 20 research projects worth over £30M. He is also the co-founder of two university spinouts specializing in data analytics and AI software for optimizing data-centre performance, as well as in co-simulation and digital-twin technologies. In addition, he is now the founding co-director of ACE3 AI Ltd.

Speech Title: Optimising Large Language Models: Scaling, Performance, and Support for AI Agents

Abstract: In this presentation, we will share our recent experience in designing and implementing a distributed system to support the full lifecycle of large language models (LLM). We will focus on the challenges of balancing key system requirements and design objectives, including model sizes, model performance, efficiency, and the mitigation of hallucinations. The presentation will also examine the critical system infrastructure and architectural support required for the development and deployment of AI agents.