Keynote Speech Ⅰ
Prof. Lei Guo
Academician of Chinese Academy of Sciences
IEEE Fellow and IFAC Fellow
Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), China
Brief Introduction to Prof. Lei Guo: Lei Guo is a Professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS). He is a Fellow of IEEE, a Fellow of IFAC, a Member of the CAS, a Foreign Member of the Royal Swedish Academy of Engineering Sciences, and a Fellow of The Third World Academy of Sciences (TWAS). In 2014, he received an honorary doctorate from the Royal Institute of Technology (KTH), Sweden, and in 2019, he was awarded the Hendrik W. Bode Lecture Prize by the IEEE Control Systems Society. His current research interests include adaptive (learning, filtering, control and games) theory of stochastic systems, control of uncertain nonlinear systems, learning based-intelligent control systems, game-based control systems, multi-agent complex systems, judicial sentencing, and man-machine integration systems, etc.
Speech Title: Convergence of Adaptive MPC
Abstract: We will discuss the convergence of an adaptive model predictive control (MPC) algorithm for discrete-time linear stochastic systems with unknown parameters. The proposed adaptive MPC is designed by solving a finite horizon constrained linear-quadratic optimal control problem of online estimated models, which are built on the weighted least-squares (WLS) estimates modified by both the random regularization and attenuating excitation methods introduced earlier in stochastic adaptive control. By using the Markov chain ergodic theory, it is shown that the adaptive MPC performance will converge asymptotically to the ergodic MPC performance with known parameters.
Keynote Speech Ⅱ
Prof. Yaochu Jin
Member of Academia Europaea, IEEE Fellow
Westlake University, China
Brief Introduction to Prof. Yaochu Jin: Yaochu Jin received the BSc, MSc and PhD degrees from the Electrical Engineering Department, Zhejiang University, Hangzhou, China in 1988, 1991 and 1996, respectively. He received the Dr.-Ing. from the Institute of Neuroinformatics, Ruhr University Bochum, Germany in 2001.
He is presently Chair Professor of AI, Director of the Trustworthy and General AI Laboratory, School of Engineering, Westlake University, Hangzhou, China. Prior to that, he was “Alexander von Humboldt Professor for Artificial Intelligence” endowed by the German Federal Ministry of Education and Research, with the Faculty of Technology, Bielefeld University, Germany from 2021 to 2023, and Surrey Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. from 2010 to 2021. He was also “Finland Distinguished Professor” with University of Jyväskylä, Finland, and "Changjiang Distinguished Visiting Professor" with the Northeastern University, China from 2015 to 2017. His main research interests include trustworthy AI for industry, embodied AI, and brain-like intelligence.
Prof. Jin is presently the President of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He is the recipient of the 2025 IEEE Frank Rosenblatt Award. He has been named "Highly Cited Researcher" by Clarivate since 2019. He is a Member of Academia Europaea and Fellow of IEEE.
Speech Title: Data-driven Optimization of Complex Systems Assisted by Small and Large Models
Abstract: This talk starts with a brief introduction to data-driven optimization of complex systems, including the motivation, main challenges and existing approaches. Then, it presents a few recent advances in this research field, such as large-scale optimization, privacy-preserving optimization, graph neural network-based end-to-end combinatorial optimization, diffusion model-based optimization, and LLM-assisted optimization. Finally, remaining challenges and open questions are discussed.
Keynote Speech Ⅲ
Prof. Hideaki Ishii
IEEE Fellow and IFAC Fellow
The University of Tokyo, Japan
Brief Introduction to Prof. Hideaki Ishii: Hideaki Ishii received the M.Eng. degree from Kyoto University in 1998, and the Ph.D. degree from the University of Toronto in 2002. He was a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign in 2001-2004, and a Research Associate at The University of Tokyo in 2004-2007. He was an Associate Professor and then a Professor at the Tokyo Institute of Technology in 2007-2024. Currently, he is a Professor at the Department of Information Physics and Computing, The University of Tokyo. He was a Humboldt Research Fellow at the University of Stuttgart in 2014--2015. His research interests include networked control systems, multiagent systems, distributed algorithms, and cyber-security of control systems.
Dr. Ishii has served as an Associate Editor for several journals including Automatica and the IEEE Transactions on Automatic Control. He was a Vice President for the IEEE Control Systems Society (CSS) in 2022-2023, the Chair of the IFAC Coordinating Committee on Systems and Signals in 2017-2023, and the IFAC Technical Committee on Networked Systems for 2011-2017. He served as the IPC Chair for the IFAC World Congress 2023 held in Yokohama, Japan. He received the IEEE Control Systems Magazine Outstanding Paper Award in 2015. Dr. Ishii is a Fellow of IEEE and IFAC.
Speech Title: Resilient Consensus of Multi-agent Systems with Multi-hop Communication
Abstract: In this talk, we focus on a multi-agent control problem known as resilient consensus, where some agents may be adversarial and misbehave to prevent the normal agents from reaching consensus in a safe manner. To mitigate the effects of such misbehaving agents, we follow the approach to equip the normal agents with the so-called weighted mean subsequence reduced (MSR) algorithm. In particular, we show how to enhance the capabilities of the algorithm through the use of multi-hop relay channels to increase the data communicated among the agents. For our algorithm to achieve resilient consensus, the network structure plays an important role, we provide a tight characterization based on a novel condition expressed by graph robustness. We will see that this line of research can be viewed as an generalization of the classic problems of Byzantine agreement in the area of distributed algorithms. Further extensions on detection of adversarial agents, averaging, and leader-follower consensus will be introduced as well.
Keynote Speech Ⅳ
Prof. Zuyi Li
IEEE Fellow
Zhejiang University, China
Brief Introduction to Prof. Zuyi Li: Dr. Zuyi Li is currently the Qiushi Chair Professor of the College of Electrical Engineering at Zhejiang University and the Executive Director of the Energy Internet Research Center of Zhejiang University. He was previously the Grainger Chair Professor of the Department of Electrical and Computer Engineering at the Illinois Institute of Technology (IIT) and Associate Director of the Galvin Center for Electricity Innovation at IIT. He was elevated to a Fellow of the Institute of Electrical and Electronics Engineers (IEEE Fellow) for his contributions to functional microgrid design and microgrid cybersecurity analyses. His research interests include economic, secure and low-carbon operation of power systems, intelligent dispatch of new power systems, electricity markets, microgrids and interconnected microgrids, and integrated energy systems. He co-led the design, implementation and operation of the world's first 10MW campus microgrid, the IIT Microgrid, and the design and control of the world's first 20MW interconnected microgrid. His paper has been cited more than 20,000 times on Google Scholar.
Speech Title: A New Paradigm for Intelligent Solutions to the Operational Optimization Problems of New-type Power Systems
Abstract: The power systems are undergoing profound changes. The energy mix is shifting from traditional thermal power to renewable energy sources such as wind power and photovoltaics. New loads are experiencing explosive growth, and new energy storage systems are developing in a diversified and large-scale manner. The resulting new-type power systems (NPS) are extremely large scale, numerous agents, and strong uncertainty. These new characteristics significantly increase the difficulty of modeling and solving the operational optimization problems (OOP) of NPS. Typical OOP scenarios include day-ahead operational planning, intraday forward scheduling, and real-time operational control, which can be formulated as mixed integer programming or nonlinear programming problems. Traditional optimization methods are insufficiently fast for solving the OPP of NPS and cannot meet the time requirements of multi-scenario optimization and iterative boundary condition adjustments. Existing artificial intelligence methods primarily fine-tune large language models and invoke end-to-end smaller models for solution. These methods suffer from the inherent drawbacks of generalized models and lack accuracy, generalizability, and interpretability. Our recent research proposes a large-model and small-model fusion framework for the OOP of NPS, including a large model for representation, modeling, and analysis based on a multi-channel encoder and multi-task decoder architecture, a cluster of small models for solution based on optimization mechanisms, and a scalable mechanism for cross-model coordination based on bidirectional decision-making and parameter transfer. The objective is to improve the speed, accuracy, generalization, and interpretability of OOP modeling and solutions, and ultimately to create a new intelligent paradigm for the accurate representation of and the efficient solution to the OOP of NPS.