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Distinguished Professor Jie Lu is a world-renowned scientist in the field of computational intelligence, best known for her contributions to fuzzy machine learning, transfer learning, concept drift, recommender systems, and decision support systems. She is an IEEE Fellow, IFSA Fellow, Australian Laureate Fellow, and Australian Industry Laureate Fellow. Professor Lu is the Director of the Australian Artificial Intelligence Institute (AAII) and Director of Australian Research Council (ARC) Research Hub in Responsible AI for a Sustainable Grain Industry (gRAIn) at the University of Technology Sydney (UTS), Australia. She has published six research books and over 500 papers in leading journals and conferences; won ten ARC Discovery Projects, one ARC Linkage Project as Lead Chief Investigator, an ARC Research Hub in Responsible AI for a Sustainable Grain Industry (gRAIn) as the Director, and over 20 industry-funded projects; and supervised 60 doctoral students to completion. Professor Lu also serves as Editor-in-Chief of Knowledge-Based Systems and the International Journal of Computational Intelligence Systems. She is a highly sought-after keynote speaker and has delivered over 40 keynote addresses at major international conferences. Her honours include three IEEE Transactions on Fuzzy Systems Outstanding Paper Awards (2019, 2022, 2025), the NeurIPS 2022 Outstanding Paper Award, Australia’s Most Innovative Engineer Award (2019), the Australasian Artificial Intelligence Distinguished Research Contribution Award (2022), the NSW Premier’s Prize for Excellence in Engineering or Information and Communication Technology (2023), and appointment as an Officer of the Order of Australia (AO) in the 2023 Australia Day.
(In-person) Title: Machine Learning for Decision Support in Complex Environments
Abstract: This talk will present how advanced machine learning can innovatively and effectively learn from complex data to support data-driven decision-making in uncertain and dynamic environments. A set of new autonomous transfer learning theories, methodologies, and algorithms will be introduced to enable knowledge transfer from multiple source domains to a target domain through the construction of latent spaces, mapping functions, and self-training mechanisms, thereby addressing substantial uncertainties in data, learning processes, and decision outputs. In addition, a new suite of theories, methodologies, and algorithms for concept drift detection, understanding, and adaptation will be discussed, focusing on how to manage continuously evolving data stream environments with unpredictable pattern changes. These approaches can detect concept drift accurately and in an explanatory manner, identifying when, where, and how drift occurs and enabling timely adaptive responses. These advanced machine learning capabilities have been applied to develop a range of real-world applications across multiple industry sectors, significantly strengthening data-driven prediction and decision support systems.
Ning Zhong received his Ph.D. from the University of Tokyo. He currently serves as Chairman of the Web Intelligence Consortium (WIC, wi-consortium.org) and as a Senior Professor of Engineering at Maebashi Institute of Technology, Japan. His research interests include Web Intelligence, Brain Informatics, machine learning, data mining, intelligent health technologies, and intelligent systems. He is the Editor-in-Chief of the journal Brain Informatics (Springer Nature). He is also a Foreign Fellow of the Engineering Academy of Japan (EAJ) and a member of the National Academy of Artificial Intelligence (NAAI).
重庆邮电大学特聘教授、日本前桥工业大学名誉教授、日本工程院外籍院士、国际网络智能协会 (WIC) 创办人并任主席、《Brain Informatics》(Springer Nature) 国际学术期刊创刊人及主编。日本东京大学工学博士 (尖端科学技术研究中心先进跨学科工学专攻，人工智能研究方向)。主要研究领域包括人工智能、网络智能、脑信息学、机器学习与数据挖掘、多粒度认知计算、智能健康技术和智能信息系统。从事网络智能与脑信息学的交叉融合研究与产业创新。
(In-person) Title: Web Intelligence Meets Brain Informatics: Building the Foundations for Future Intelligent Societies
Abstract: This keynote explores the transformative convergence of Web Intelligence and Brain Informatics, focusing on how their integration is driving the development of future intelligent societies. It highlights cutting-edge research and industrial innovations led by the International Web Intelligence Consortium (WIC), including Web Intelligence (WI) 3.0–based wisdom services, intelligent computing for brain big data, and advanced solutions for the prevention and treatment of psychiatric and neurocognitive disorders through intelligent health technologies. These initiatives address critical and societal challenges, and set the stage for the next generation of intelligent systems and services.
Yi Pan is currently a Chair Professor and the Dean with the College of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China, and a Regents' Professor Emeritus with Georgia State University, Atlanta, GA, USA. From 2005 to 2020, he was the Chair with Computer Science Department, Georgia State University. During 2013–2017, he was also the Interim Associate Dean and Chair with Biology Department. In 2000, he joined Georgia State University, 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. He has authored or coauthored more than 450 papers including more than 250 journal papers with more than 100 papers published in IEEE/ACM Transactions/Journals and has also edited/authored 43 books. His work has been cited more than 27000 times based on Google Scholar and his current H-index is 98. Dr. Pan is also the Editor-in-Chief of Big Data Mining and Analytics (a top 3% journal), Associate Editor-in-Chief of Journal of Computer Science and Technology (JCST), and Chinese Journal of Electronics (CJE). He was the Editor-in-Chief or Editorial Board Member for 20 journals including seven IEEE Transactions.
(Online) Title: Some thoughts on the generation and screening of small molecule drugs based on AI
Abstract: As we all know, the field of drug research and development generally faces three core challenges, namely long cycle, high cost and low success rate. In view of the pain points of traditional drug design, this talk puts forward a set of all-round artificial intelligence (AI)-assisted drug design strategies. Specifically, the strategy includes several small molecule generation methods, and further comprehensively evaluates multi-dimensional indicators such as affinity, potential therapeutic effect, ADMET characteristics, energy characteristics and binding conformation of small molecules to specific targets to determine their adaptability to the target. At the same time, in order to screen out small molecules with better comprehensive performance, we introduce a multi-objective optimization method, which aims to achieve the balanced performance of molecules in various key indicators from the macro level. This talk will also elaborate on the preliminary framework design and some experimental verification results of the above strategies.
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 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, and is the founding co-director of ACE3 AI Ltd.
(Online) Title: Structuring Massive-Scale Distributed Systems for Intelligent Applications
Abstract: This talk revisits our experience in designing and implementing massive-scale distributed systems, drawing lessons from real-world deployments and evolving architectural practices. It explores how next-generation distributed systems must adapt to support an AI-native world and the growing demands of increasingly intelligent, autonomous applications. The talk examines a set of powerful structuring techniques for building such systems, analyses the diverse design forces that shape them and the trade-offs they entail, and discusses the highly dynamic challenges of multi-party environments, including authentication, trust, and collaboration among AI agents and other actors.