Prof. Yi Pan

Shenzhen University of Advanced Technology, China

Dr. Yi Pan is currently a Chair Professor and Dean of Faculty of Computer Science and Artificial Intelligence at Shenzhen University of Advanced Technology, China and a Regents’ Professor Emeritus at Georgia State University, USA. He served as Chair of Computer Science Department at Georgia State University from 2005 to 2020. He has also served as an Interim Associate Dean and Chair of Biology Department during 2013-2017. Dr. Pan joined Georgia State University in 2000, was promoted to full professor in 2004, named a Distinguished University Professor in 2013 and designated a Regents' Professor (the highest recognition given to a faculty member by the University System of Georgia) in 2015.

Dr. Yi Pan is Fellow of American Institute for Medical and Biological Engineering, Foreign Member of Russian Academy of Engineering, Foreign member of Ukrainian Academy of Engineering Science, Member of European Academy of Sciences and Arts, Distinguished Fellow of International Engineering and Technology Institute,  Fellow of the Royal Society for Public Health, Fellow of the Institute of Engineering and Technology, and Fellow of the Japan Society for the Promotion of Science. 

His work has been cited more than 34000 times based on Google Scholar and his current h-index is 104. He was selected as a top 0.05% scholar in the world and ranked the world's No. 4 top scholar in computational biology over the past five years by ScholarGPS in 2024 and 2025.

Speech Title: AI LLMs Empower Biomedical Applications and Their Future Research Directions

Abstract: Starting from the current state of generative artificial intelligence (AIGC) and large language models (LLM), I will first discuss the basic principles and shortcomings of the latest AIGC products, such as ChatGPT and Sora, along with their future improvements and development trends. I will mainly elaborate on the important roles and value of AIGC in the biopharmaceutical field. Recently, ChatGPT outperformed 17 doctors by accurately diagnosing a rare disease in a 4-year-old boy. This demonstrates that, when applied appropriately, AI can indeed become an assistant in diagnosing and treating diseases. However, a study published in JAMA by Brigham and Women’s Hospital, affiliated with Harvard University, showed that ChatGPT's cancer treatment recommendations were only completely accurate in 62% of cases, indicating that its results should be applied cautiously. One solution to this issue is the use of content detection tools, such as AIGC-X and ZeroGPT. The vast information behind ChatGPT is an advantage, but in specialized fields, it also brings the downside of excessive interference information. To address this, our team has developed a large language model with a knowledge vector library system for autism that reduces training time and achieves similar objectives using only a small amount of training data. This lecture will also introduce the use of AIGC in designing new drug molecules. By inputting numerous small drug molecules related to the treatment of a particular disease into the AIGC system, new drug molecules can be generated. Coupled with our powerful AI drug screening capabilities, we have the potential to design new drugs suitable for specific targets. Future directions with LLMs will also be identified.

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.