Prof. Dapeng Wu (IEEE Fellow)
City University of Hong Kong, Hong Kong, China

Bio: Dapeng Oliver Wu (S'98--M'04--SM'06--F'13) received a B.E. degree in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 1990, an M.E. degree in electrical engineering from Beijing University of Posts and Telecommunications, Beijing, China, in 1997, and a Ph.D. degree in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, PA, in 2003.
He is Yeung Kin Man Chair Professor of Network Science, and Chair Professor of Data Engineering at the Department of Computer Science, City University of Hong Kong. Previously, he was on the faculty of University of Florida, Gainesville, FL, USA and was the director of NSF Center for Big Learning, USA. His research interests are in the areas of artificial intelligence, network science, communications, signal processing, computer vision, and biomedical engineering. He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) Outstanding Paper Award for Year 2025, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, and the Best Paper Awards in IEEE GLOBECOM 2011 and International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (QShine) 2006.
He has served as founding Editor in Chief of Transactions of Artificial Intelligence, Editor in Chief of IEEE Transactions on Network Science and Engineering, founding Editor in Chief of Journal of Advances in Multimedia, Editor-at-Large for IEEE Open Journal of the Communications Society, and Associate Editor for IEEE Transactions on Cloud Computing, IEEE Transactions on Communications, IEEE Transactions on Signal and Information Processing over Networks, IEEE Signal Processing Magazine, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Wireless Communications and IEEE Transactions on Vehicular Technology. He has served as Technical Program Committee (TPC) Chair for IEEE INFOCOM 2012, and TPC chair for IEEE International Conference on Communications (ICC 2008), Signal Processing for Communications Symposium, and as a member of executive committee and/or technical program committee of over 100 conferences. He was elected as a Distinguished Lecturer by IEEE Vehicular Technology Society in 2016. He is an IEEE Fellow.

Speech Title: AI Psychology: A New Discipline for Understanding and Cultivating Artificial Minds

Abstract: An Artificial Intelligence (AI) system can be designed with human-like consciousness and emotions. Hence, it is the right time to study the psychology behind AI’s consciousness and emotions. In this talk, I will propose and present “AI psychology”, a new discipline dedicated to the scientific study of AI minds and behaviors, encompassing conscious and unconscious phenomena, thoughts, and emotions. AI psychology centers around three core research questions: (Q1) What behavior can be generated from a given latent mental state of AI? (Q2) What latent mental state of AI can be inferred from a given behavior? and (Q3) how can an AI agent be taught to achieve a given goal?

Prof. Xudong Jiang (IEEE Fellow)
Nanyang Technological University, Singapore

Bio: Xudong Jiang (Fellow of IEEE) received the B.E. and M.Eng degrees from the University of Electronic Science and Technology of China (UESTC), and the PhD degree from Helmut Schmidt University, Hamburg, Germany. From 1998 to 2004, he was with the Institute for Infocomm Research, A*STAR, Singapore, as a lead scientist, and the head of the Biometrics Laboratory. He joined Nanyang Technological University (NTU), Singapore, as a faculty member, in 2004, where he served as the director of the Centre for Information Security from 2005 to 2011. He is currently a professor with the School of EEE, NTU and serves as the director of the Centre for Information Sciences and Systems of School of EEE, NTU. He has authored over 300 papers with over 80 papers in IEEE journals including 15 T-PAMI papers and over 20 T-IP papers. Dr Jiang has presented over 50 papers in top AI conferences CVPR/NeurIPS/ICML/ICCV/ECCV/ICLR/AAAI. His papers have been cited over 18 Thousand times with H-index 72 according to Google Scholar. He served as IFS TC member of the IEEE Signal Processing Society from 2015 to 2017, associate editor for IEEE Signal Processing Letter from 2014 to 2018 and associate editor for IEEE Transactions on Image Processing from 2016 to 2020. Currently, he is an IEEE Fellow, serves as senior area editor for IEEE Transactions on Image Processing and editor-in-chief for IET Biometrics. He also served as Area chairs for top AI conferences AAAI, NeurIPS and IEEE ICIP. His current research interests include image processing, pattern recognition, computer vision, machine learning, and biometrics.

Speech Title: The Critical Role of Convolution in Machine Learning and Attention in Artificial Intelligence

Abstract: Discovering knowledge from data has many applications in various artificial intelligence (AI) systems. Machine learning from the data is a solution to find right information from the high dimensional data. It is thus not a surprise that learning-based approaches emerge in various AI applications. The powerfulness of machine learning was already proven 40 years ago in the boom of neural networks but its successful application to the real world is just in recent years after the deep convolutional neural networks (CNN) have been developed. This is because the machine learning alone can only solve problems in the training data but the system is designed for the unknown data outside of the training set. This gap can be bridged by regularization: human knowledge guidance or interference to the machine learning. This speech will analyze these concepts and ideas from traditional neural networks to the deep CNN and Transformer. It will answer the questions why the traditional neural networks fail to solve real world problems even after 30 years’ intensive research and development and how CNN solves the problems of the traditional neural networks and how Transformer overcomes limitation of CNN and now becomes the foundations of all AI large models.

Prof. Minghua Chen (IEEE Fellow)
The Chinese University of Hong Kong, Shenzhen, China

Bio: Minghua received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California Berkeley. He currently is a Presidential Chair Professor in School of Data Science, The Chinese University of Hong Kong, Shenzhen. He received the Eli Jury award from UC Berkeley in 2007 (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and The Chinese University of Hong Kong Young Researcher Award in 2013. He also received several paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, ACM Multimedia Best Paper Award in 2012, ACM e-Energy Best Paper Award in 2023, and Nature Communications Editors’ Highlights in 2025 (as one of the 17 highlight papers in Engineering and Infrastructure). Coding primitives co-invented by Minghua have been incorporated into Microsoft Windows and Azure Cloud Storage, serving hundreds of millions of users. Prof. Chen and his team’s carbon-minimizing schemes demonstrated promising fuel-saving performance in a heavy-duty truck field test spanning over tens of thousands of kilometers, offering compelling potential for decarbonizing the trucking industry. His recent research interests include online optimization and algorithms, machine learning in power system operation, intelligent transportation, distributed optimization, and delay-critical networking. He is a Fellow of the IEEE.

Speech Title: Machine Learning for Real-Time Optimization under Hard Constraints

Abstract: Optimization problems subject to hard constraints are common in time-critical applications such as autonomous driving, communications, networking, and power grid operation. However, existing iterative solvers often face difficulties in solving these problems in real-time. In this talk, we advocate a machine learning approach -- to employ NN's approximation capability to learn the input-solution mapping of a problem and then pass new input through the NN to obtain a quality solution, orders of magnitude faster than iterative solvers. To date, the approach has achieved exciting empirical performance and promising theoretical development. A fundamental issue, however, is to ensure NN solution feasibility with respect to the hard constraints, which is non-trivial due to inherent NN prediction errors. To this end, we present two approaches, predict-and-reconstruct and homeomorphic projection, to ensure NN solution strictly satisfies the equality and inequality constraints, respectively. In particular, homeomorphic projection is a low-complexity scheme to guarantee NN solution feasibility for optimization over any set homeomorphic to a unit ball, covering all compact convex sets and certain classes of nonconvex sets. The idea is to (i) learn a minimum distortion homeomorphic mapping between the constraint set and a unit ball using an invertible NN (INN), and then (ii) perform a simple bisection operation concerning the unit ball so that the INN-mapped final solution is feasible with respect to the constraint set with minor distortion-induced optimality loss. We prove the feasibility guarantee and bound the optimality loss under mild conditions. Simulation results, including those for computation-heavy SDP problems and non-convex AC-OPF problems for grid operations, show that homeomorphic projection outperforms existing methods in solution feasibility and run-time complexity, while achieving similar optimality loss. We will also discuss future directions. This is a joint work with Enming Liang (City University of Hong Kong) and Steven Low (Caltech).