Prof. Zhi-Hua Zhou (IEEE/ACM/AAAI/AAAS Fellow, member of the Academia Europaea)
Nanjing University, China

Bio: Zhi-Hua Zhou is Professor of Computer Science and Artificial Intelligence, Vice President of Nanjing University. His research interests are mainly in machine learning and data mining, with significant contributions to ensemble learning, multi-label and weakly supervised learning, etc. He has authored the books "Ensemble Methods: Foundations and Algorithms", "Machine Learning", etc., and published more than 200 papers in top-tier journals or conferences, with more than 90,000 citations according to Google Scholar. Many of his inventions have been successfully deployed in industry. He founded ACML (Asian Conference on Machine Learning), serves as series editor of Springer Lecture Notes in Artificial Intelligence, advisory board member of AI Magazine, editor-in-chief of Frontiers of Computer Science, associate editor of AIJ, MLJ, etc. He is President of IJCAI Trustee, Fellow of the ACM, AAAI, AAAS, IEEE, member of the Academia Europaea, and recipient of the National Natural Science Award of China, the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the CCF-ACM Artificial Intelligence Award, etc.

Speech Title: "Abductive Learning"

Abstract: To develop a unified framework which accommodates and enables machine learning and logical knowledge reasoning to work together effectively is a well-known holy grail problem in artificial intelligence. This talk will briefly introduce Abductive Learning, a recently proposed paradigm which leverages machine learning and logical reasoning in a balanced and mutually supporting loop, which has exhibited promising results in real tasks where both data and domain knowledge are available.

Prof. Ryuji Kohno (IEEE Life/IEICE Fellow)
Yokohama National University, Japan

Bio: Ryuji Kohno received the Ph.D. degree from the University of Tokyo in 1984. He was a Professor and the Director of Centre on Medical Information and Communication Technology, in Yokohama National University (YNU) in Japan for 1998-2021 and then Professor Emeritus of YNU teaching in Toyo University. In his currier he played a part-time role of a director of Advanced Telecommunications Laboratory of SONY CSL during 1998-2002, directors of UWB Technology and medical ICT institutes of NICT during 2002-2012. For 2012-2020 he was CEO of University of Oulu Research Institute Japan - CWC-Nippon Co. and since 2020 Vice-President of YRP International Alliance Institute. The meanwhile for 2007-2020 a distinguished professor in University of Oulu in Finland and since 2006 a member of the Science Council of Japan. In IEEE he was a member of the Board of Governors of Information Theory Society in 2000-2009, and editors of Transactions on Communications, Information Theory, ITS, IEEE802.15 standardization TG6ma Chair, and IEEE Life Fellow. In IEICE he was a vice-president of Engineering Sciences Society of IEICE during 2004-2005, Editor-in chief of the IEICE Trans. Fundamentals during 2003-2005, and IEICE Fellow. He is a founder and a chair of steering committee of international symposia of medical information and communication technologies (ISMICT) since 2006. He has played a role of member in radio regulatory committee of the Ministry of Internal affairs and Communications (MIC) Japan and ITU-R.

Speech Title: "Sustainable R&D and Business Promotion of the Universal Platform among Interactive Machine Learning, 6G, and Dependable Wireless BAN for Human, Vehicular, Robotic and Other Bodies"

Abstract: In a medical healthcare field, wireless body area network (BAN) has a huge potential to create innovation by promoting integrated research and development with cloud networks and data science such as integrated BAN/6G/AI platform. A new international standard of WBAN with enhanced dependability, IEEE802.15.6ma has been extended to car and robotic bodies from human body to promote a global social service and business toward goals of SDGs. To achieve the goals it is necessary to approach any other technologies such as data science, metaverse, security, quantum, AI/ML computing, chat GPT, DX, etc. with WBAN. This talk focuses on comprehensive research, development, standard, regulation, field trials, business, and social services of the universal platform with advanced information communication technology (ICT) and AI data science to achieve sustainable medical healthcare and other SDGs. 6G infrastructure networks could be applied with dependable WBAN and machine-learning with data mining for medical social platform using interactive reliable data and cognitive control. Particularly some projects on brain-machine-interface (BMI) and elderly people day care using ultra-wide band(UWB) WBAN and multimodal machine-learning with various sensed data are introduced. To manage make comprehensive design and operation of such a universal platform is not so easy but a key for sustainable success. This talk addresses latest business promotion with clinical trials, latest activity of IEEE802 Dependable BAN and ETSI Smart BAN, and regulation update with regulatory scientific approach, and bigger market of the universal platform in automotive industry, social infrastructure maintenance, etc. Moreover, education of such a balanced expert for multidisciplinary fields could be covered.

Prof. Guoping Qiu
The University of Nottingham, UK & The University of Nottingham Ningbo, China

Bio: Professor Guoping Qiu researches neural networks and their applications in image processing. He pioneered application of neural networks to image feature extraction, introducing one of the earliest representation learning methods that leveraged unsupervised competitive neural networks for image representation. He also spearheaded learning-based super-resolution techniques and developed early neural network solutions for compression artifact removal, well before deep learning became mainstream in these applications. Professor Qiu has been at the forefront of HDR imaging, pioneering tone-mapping methods that have fundamentally transformed how HDR content is processed and displayed. Innovations from his research group have been successfully transferred to award-winning digital photo editing software such as HDR Darkroom and Fotor, which are used by hundreds of millions of consumers worldwide. His recent research focuses on deep learning, visual-language modeling, and large language models (LLMs), applying these cutting-edge technologies to some of the most complex challenges in digital imaging. As Chief Scientist at Everimaging (www.everimaging.com), the company behind HDR Darkroom and Fotor, he is driving advancements in imaging technologies to solve real-world problems. With a distinguished career spanning academia and industry, Professor Qiu’s contributions have had a lasting impact on both fundamental research and real-world applications in imaging technology.
Professor Qiu currently holds the position of Chair Professor of Visual Information Processing at the School of Computer Science, University of Nottingham, UK. Additionally, he serves as the Vice Provost for Education and Student Experience at the University of Nottingham Ningbo China (UNNC), overseeing the education and student experience of a diverse academic community of over 10,000 students and 1,000 staff from more than 70 countries and regions. UNNC delivers all its teaching in English and offers undergraduate, Master's, and PhD programs across business, humanities, social sciences, and science and engineering, awarding degrees from the University of Nottingham.

Speech Title: "From Camera to AI: The Future of Visual Content Creation"

Abstract: Vision plays a fundamental role in human perception, learning, and cognition, with over 80% of our interactions with the world being mediated through sight. It is no surprise, then, that computational visual content creation—from capturing the beauty of the natural world with digital cameras to generating entirely artificial scenes using AI—has seen remarkable advancements in recent decades.
However, despite significant progress in digital imaging and artificial intelligence, critical challenges remain. Traditional digital photography still struggles with mismatches between the dynamic range of natural scenes and the limitations of display and print media, leading to compromised visual fidelity. Meanwhile, ensuring that AI-generated content (AIGC) adheres to physical realism while maintaining creative flexibility remains an ongoing challenge. Moreover, even when breakthroughs are made in visual content creation theory, translating these advancements into practical, user-friendly tools that cater to real-world needs is an equally formidable task.
In this talk, I will explore key technical hurdles in both natural and AI-driven visual content creation, discussing state-of-the-art solutions that bridge the gap between theory and practice. I will also introduce an AI-powered digital creativity platform designed to empower users in seamlessly crafting high-quality visual content, blending the precision of science with the boundless possibilities of artificial intelligence.

Prof. Minghua Chen (IEEE Fellow)
City University of Hong Kong, Hong Kong, 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 is a Professor of Det. of Data Science, City University of Hong Kong and an Associate Dean (internationalization and industry) of College of Computing. 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 Gradient AI Research Award in 2024. Coding primitives co-invented by Minghua have been incorporated into Microsoft Windows and Azure Cloud Storage, serving hundreds of millions of users. 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 an ACM Distinguished Scientist and an IEEE Fellow.

Speech Title: "Machine Learning for Real-Time Constrained Optimization"

Abstract: Optimization problems subject to hard constraints are common in time-critical applications such as autonomous driving and real-time 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 promising empirical performance and exciting 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 open problems and future directions.