报告题目：What Can Data Do in Engineering Research?（数据能为工程研究作点什么？）
报告专家：Jian-Qiao Sun（孙建桥） 美国加州大学Merced分校 教授
报告时间：2025年01月10日（周五）上午8:30-10:30
腾讯会议：198-536-213
报告摘要：We are in the middle of the so-called big data time when the AI has started penetrating many scientific and engineering fields. This talk discusses what we can do with data in engineering research. In particular, we consider a number of engineering research topics that heavily rely on the availability of data, and then focus on the work we have done including global analysis of nonlinear dynamic systems, modeling, identification and control of mechanical and robotic systems. We shall also highlight the importance of machine learning methods that can solve complex engineering problems with data, which were very difficult to attack otherwise. Interesting examples will be presented. It is hoped that this talk will motivate more people to learn various methods from the machine learning and artificial intelligence community and apply them to find new solutions of engineering research problems while making use of big data.
报告人简介：Dr. Jian-Qiao Sun earned a BS degree in Solid Mechanics from Huazhong University of Science and Technology in Wuhan, China in 1982, a MS and a PhD in Mechanical Engineering from University of California at Berkeley in 1984 and 1988. He worked for Lord Corporation at their Corporate R&D Center in Cary, North Carolina. In 1994, Dr. Sun joined the Department of Mechanical Engineering at the University of Delaware as an Assistant Professor, was promoted to Associate Professor in 1998 and to Professor in 2003. He joined University of California at Merced in 2007 and is currently a professor of the Department of Mechanical Engineering in School of Engineering. Besides many other editorial experiences, he is the Editor-in-Chief of International Journal of Dynamics and Control published by Springer.
His research interests include stochastic non-linear dynamics and control, cell mapping methods, multi-objective optimization, intelligent control systems, high-density piezoelectric energy harvesting from highway traffic and machine learning for engineering applications.
