报告时间：2026年5月14日 星期四 14：00-15：00
报告地点：779cn太阳集团九龙湖校区 信息大楼 1334
报告人：Prof. Min Xia
报告摘要：Machine learning, especially deep learning, has been widely investigated in data-driven solutions for smart manufacturing and smart energy systems, including monitoring, optimization, and decision-making, due to its superior capabilities in classification, regression, or content generation (e.g., Large Language Model-based approaches). However, the "black box" nature of deep learning has limited the practical application or acceptance of these methods in real industrial settings. Building reliable and trustworthy approaches is both an urgent and demanding task in both academia and industry. This talk will illustrate trustworthy AI-based methods, focusing on interpretable model learning, uncertainty estimation, and physics-informed learning, which can significantly enhance the reliability of AI-based solutions in smart engineering systems like manufacturing and energy systems. Through real-world case studies, the developed solution with scientific machine learning and future application scenarios will be explored.
报告人简介：Dr. Min Xia is an Associate Professor and the Director of the Machine Intelligence Laboratory (MIN Lab,Machine Intelligence Laboratory) at the Department of Mechanical and Materials Engineering at The University of Western Ontario in Canada. His research interests include intelligent machine condition monitoring, advanced manufacturing process monitoring and optimization, industrial data mining, and smart clean energy systems. He has led 20 research projects as PI or Co-I funded by Horizon Europe, Innovate UK, NSERC, Mitacs, etc., with total funding of more than $21 million. Dr. Xia has published more than 100 papers in peer-reviewed journals and conferences. He is Editor-in-Chief of the Journal of Mechatronic Systems and Control, Associate Editor of IEEE Transactions on Industrial Informatics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Instrumentation and Measurement, Journal of Intelligent Manufacturing, and IET Collaborative Intelligent Manufacturing.
- Senior Member of IEEE
- Fellow of Higher Education Academy (UK)
- Fellow of Royal Society of Arts (UK)
Education:
Ph.D.: University of British Columbia, Canada
M.Sc.: University of Science and Technology of China
B.Eng.: Southeast University, China
