Prof. Xiangen HU began his academic journey in applied mathematics, earning his Bachelor's and Master's degrees from Huazhong University of Science and Technology in 1982 and 1985, respectively. He then moved to the United States to further his education, obtaining a Master's in social sciences in 1991 and a Ph.D. in cognitive psychology in 1993.
Before his current position as a chair professor in learning sciences and technologies at PolyU, Prof. HU held several positions. He was a professor in the Departments of Psychology, Electrical and Computer Engineering, and Computer Science at The University of Memphis (UofM) for 30 years, where he also worked as a senior researcher at the Institute for Intelligent Systems (IIS). His leadership roles included serving as a professor and Dean of the School of Psychology at Central China Normal University (CCNU), leading the Advanced Distributed Learning (ADL) Partnership Laboratory at UofM, and working as a senior researcher at the Key Laboratory of Adolescent Cyberpsychology and Behavior, backed by the Chinese Ministry of Education.
Prof. HU's research focuses on four key areas: developing mathematical models to decode human cognitive behavior, specializing in research design and statistical analysis particularly for categorical data using general processing tree models, delving into artificial intelligence for knowledge representation, creating computerized tutoring systems, and enhancing distributed learning technologies.
His work has attracted significant funding from prestigious bodies like the US National Science Foundation, the US Institute of Education Sciences, the Advanced Distributed Learning initiative of the US Department of Defense, the US Army Medical Research Acquisition Activity, the US Army Research Laboratories, and the US Office of Naval Research. As the lead principal investigator, Prof. HU has managed projects with over $10 million in funding, and as a co-principal investigator, he has been involved in projects amassing more than $30 million in grants.
LIM Cher Ping is a Chair Professor of Learning Technologies and Innovation, and the Co-Director of the Global Institute for Emerging Technologies at The Education University of Hong Kong. He is also a Visiting Professor at the UNESCO International Centre for Higher Education Innovations. He has served for more than 10 years as the Editor-in-Chief of The Internet and Higher Education until 2025, and is now its Advisory Editor. Over the last two decades, he has engaged major education stakeholders at the national and international levels as his research and development partners for enhancing equity, quality and efficiency in the education sector enabled by emerging technologies.
Prof. Ming Li is currently a Distinguished Professor at the Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, China. He received his Ph.D. in Computer Science and Information Technology from La Trobe University, Australia. Following that, he completed two postdoctoral fellowships: one in the Department of Mathematics and Statistics at La Trobe University, Australia, and the other in the Department of Information Technology in Education at South China Normal University, China. He has published more than 100 papers in top-tier journals and conferences, such as IEEE TPAMI, Artificial Intelligence, IEEE TKDE, Computers & Education, BJET, NeurIPS, ICML, and AAAI. He is a member of IEEE, the China Computer Federation (CCF), the Chinese Association for Artificial Intelligence (CAAI), and the Australian Mathematical Society (AustMS). He is the recipient of the AAAI 2026 Outstanding Paper Award.
He served as the lead guest editor for the special issue "Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications" organized in IEEE TNNLS, the special session "Design and Theory of Deep Graph Learning" at the 2025 IEEE International Joint Conference on Neural Networks (IJCNN 2025), and the special session on "Recent Advances in Deep Learning for Graphs" at the 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science (LOD 2022). He has served as an AC/SPC/PC member for top conferences such as ICML, NeurIPS, IJCAI, KDD, and ICLR, and is also a member of the IEEE Task Force on Learning for Structured Data. He currently serves as an Associate Editor or Editorial Board Member for several international journals, including Pattern Recognition, Neural Networks, Machine Learning, Applied Intelligence, Alexandria Engineering Journal, Soft Computing, Network: Computation in Neural Systems, Neural Processing Letters, and Education and Information Technologies.