报告人：宋永端 教授 重庆大学人工智能研究院
报告时间：2025年4月2日（周三）下午15:30
报告地点：东南大学九龙湖校区计算机楼513报告厅
报告摘要：In contemporary engineering and scientific research, the interplay between automatic control and machine learning has become increasingly significant. This report explores two key aspects of this relationship: the application of machine learning techniques to enhance automatic control systems and the use of automatic control principles to improve machine learning algorithms. Firstly, we discuss how machine learning can be leveraged to optimize control strategies in complex systems, enabling adaptive and intelligent responses to dynamic environments. Techniques such as reinforcement learning and neural networks are examined for their ability to learn from data, resulting in more efficient control mechanisms that can handle uncertainty and nonlinearity.
Secondly, we investigate how principles of automatic control can be applied to refine machine learning processes. Concepts such as feedback control can be utilized to stabilize learning algorithms, reduce overfitting, and ensure convergence in various machine learning applications. This dual perspective highlights the mutual benefits and synergies that arise from integrating these two fields.
Through case studies and examples, we demonstrate the transformative potential of combining machine learning and automatic control, paving the way for advances in robotics, autonomous systems, and smart technologies. Ultimately, this report aims to provide insights into the future directions of research and the practical implications of merging these two domains.
报告人简介：宋永端Yongduan Song (Fellow, IEEE; AAIA, CAA, CAI) earned his Ph.D. in Electrical and Computer Engineering in 1992 and is a tenured Full Professor in the United States. From 2005 to 2008, he served as one of six Langley Distinguished Professors at the National Institute of Aerospace (NIA), where he was also the Founding Director of the Center for Cooperative Systems.
Professor Song previously held the position of Dean of the School of Automation at Chongqing University and is currently the Director of the Chongqing University Artificial Intelligence Research Institute. Additionally, he has a dual appointment as the Dean of the School of Artificial Intelligence at Anhui University.
His research interests are broad and include intelligent systems, guidance, navigation, and control, as well as bio-inspired adaptive and cooperative systems, with a particular focus on data-driven control and machine learning methodologies. He has published 12 books and over 400 scientific papers, and holds 100 patents issued by China, the USA, and Japan. He has made significant contributions to the academic community through his role as an Associate Editor for several prestigious international journals, including the IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, and IEEE Transactions on Systems, Man, and Cybernetics. Currently, he serves as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems.
Since 2021, Professor Song has been recognized as one of the World’s Top 2% Scientists by Stanford University and has been listed as one of the most highly cited researchers by Clarivate since 2019.
