Keynote Speakers
Prof. Daoyi Dong (IEEE Fellow, ARC Future Fellow)
University of Technology Sydney, Australia
Speech Title: Several results on quantum machine learning
Abstract: In this talk, we will introduce several results on quantum machine learning. Firstly, we will give overview of quantum machine learning. Secondly, we introduce the area of quantum reinforcement learning. Thirdly, we introduce an efficient parameter initialization strategy with theoretical guarantees to enhance the trainability of parameterized quantum circuits. Lastly, we show that noises may make quantum kernel methods to only have poor prediction capability.
Biography: Daoyi Dong (Fellow, IEEE) is currently a Professor and an ARC Future Fellow at the Australian Artificial Intelligence Institute, University of Technology Sydney, Australia and an Honorary Professor at the Australian National University. His research interests include machine learning, quantum estimation and quantum control. Prof. Dong was awarded an ACA Temasek Young Educator Award by The Asian Control Association and is a recipient of a Future Fellowship, an International Collaboration Award and an Australian Post-Doctoral Fellowship from the Australian Research Council, and a Humboldt Research Fellowship from the Alexander von Humboldt Foundation of Germany. He is a Vice President of IEEE Systems, Man and Cybernetics Society, and a member of Board of Governors, IEEE Control Systems Society. He is currently an Associate Editor of Automatica and IEEE Transactions on Cybernetics. He is a Fellow of the IEEE, and a Fellow of the Australian Institute of Physics.
Prof. Pin-Han Ho (IEEE Fellow, AAIA Fellow)
University of Waterloo, Canada
Biography: Professor Pin-Han Ho is a Full Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada, where he has served on faculty since 2002. He is also with Shenzhen Institute for Advanced Study, UESTC. He is a distinguished researcher recognized as a Fellow of the IEEE and a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA).
His research interests span next-generation wireless communications (5G/6G), optical networks, Internet of Things (IoT), artificial intelligence in networking, and integrated sensing and communications (ISAC). He is the author of two textbooks on optical networks and holds multiple U.S. patents. His academic excellence has been recognized with numerous awards, including the Early Researcher Award (2005), Google Research Award (2008), and multiple Best Paper Awards at prestigious IEEE conferences (ICC, HPSR, etc.). Professor Ho actively serves the academic community as an Associate Editor for several leading journals and has held leadership roles as a Symposium Co-Chair and Technical Program Chair for major international conferences such as IEEE ICC and WCNC.
South China University of Technology, China
Speech Title: HongWU: Hierarchical On-demand Cognitive Big Model with World Utility
Abstract: This talk introduces HongWU (Hierarchical On-demand Machine-Cognitive Model with World Utility), a unified cognitive framework designed to address fundamental bottlenecks facing contemporary large-scale models: training data depletion, insufficient alignment with human intent, and inadequate grounding in physical systems. The HongWU framework integrates physical models, multi-source data, and intelligent tools into a unified tool matrix orchestrated by the foundation model. Through parameter-efficient Fine-tuning and Human-in-the-loop feedback, the model dynamically aligns its objectives with human needs. Its federated knowledge engine, multi-level spatiotemporal reasoning, and multi-agent workflow ensure physically consistent reasoning and enable scalable management of complex engineering systems.
Biography: Professor Min Chen is a Professor and Doctoral Supervisor at the School of Computer Science, South China University of Technology. He is an IEEE Fellow, IET Fellow, and AAA Fellow, serving as Chief Scientist of a National Key Research and Development Programme. Professor Chen has been named a Clarivate Highly Cited Researcher for eight consecutive years (2018–2025). With over 56,000 citations on Google Scholar and an H-index of 104, his academic influence is globally recognized. He has published more than 200 papers in top venues including Science, Nature Communications, and CCF Class A conferences, with 34 ESI Highly Cited Papers and a single paper cited over 6,060 times. He got IEEE ICC Best Paper Award in 2012, IEEE Communications Society Fred W. Ellersick Prize in 2017, the IEEE Jack Neubauer Memorial Award in 2019, and IEEE ComSoc APB Oustanding Paper Award in 2022. His research focuses on cognitive computing, Large Language Model, big data analytics, Embodied AI, and edge intelligence, etc.
