Yaochu Jin
Chair Professor of AI
Director, Trustworthy and General AI Laboratory
Head, Artificial Intelligence Department, School of Engineering
Westlake University, Hangzhou, China
Biography
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, Head of the Artificial Intelligence Department, 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 the University of Jyväskylä, Finland, and “Changjiang Distinguished Visiting Professor” with Northeastern University, China from 2015 to 2017. He was the President of the IEEE Computational Intelligence Society and the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems. His main research interests include trustworthy AI for industry, embodied AI, and brain-like intelligence.
Prof Jin 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.
Data-driven optimization of complex systems assisted by small and large models
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, including privacy-preserving optimization, diffusion model-based optimization, and LLM-and agentic AI-assisted multi-modal data-driven optimization. Finally, remaining challenges and open questions are discussed.
Research Interests
- Trustworthy AI for Industry
- Embodied AI
- Brain-like Intelligence
- Data-driven Optimization
- Privacy-preserving Optimization
- Diffusion Model-based Optimization
- LLM and Agentic AI-assisted Optimization
Selected Distinctions
- 2025 IEEE Frank Rosenblatt Award
- Highly Cited Researcher by Clarivate since 2019
- Member of Academia Europaea
- Fellow of IEEE
- Former President, IEEE Computational Intelligence Society
- Former Editor-in-Chief, IEEE Transactions on Cognitive and Developmental Systems