Time: 10:30-12:00 (Beijing time), Tuesday, 2025/12/16 (December 16)
Location: Room 401, Building 7, UCAS Zhongguancun Campus
Speaker: Ma Yanqing, Peking University
Abstract: The limitations of human-driven scientific discovery research make the adoption of an AI-based research paradigm necessary. Although the rapid development of AI presents promising prospects for this new paradigm, making AI mimic human-like scientific discovery remains a significant challenge yet to be addressed. The theoretical part of scientific discovery involves a cyclical process between data and models, and between models and data. This lecture will introduce the challenges and research progress in both directions; specifically, it will cover AI-Newton, which is related to the former, and LOCA-R, which is related to the latter.
Bio: Yanqing Ma is a Boya Distinguished Professor at the School of Physics, Peking University. He obtained his Bachelor's degree from Wuhan University in 2006 and his PhD from Peking University in 2011. From 2011 to 2015, he conducted postdoctoral research at Brookhaven National Laboratory and the University of Maryland in the United States. Since 2015, he has been working at Peking University. His research interests include quantum field theory methods, precision tests of the Standard Model, color confinement and hadronization mechanisms, and the intersection of physics and artificial intelligence. He has made a series of internationally leading contributions to specific problems such as heavy quarkonium production mechanisms, proton structure, Feynman integral solving methods, collider physics, and the use of artificial intelligence to uncover physical laws. He has published over 70 papers in academic journals. In 2023, he was supported by the National Science Fund for Distinguished Young Scholars, and in 2024, he was awarded the Chen Jiageng Young Scientist Award for his research on Feynman integrals and their contributions to the development of particle physics.
Link for Zoom meeting:
https://us06web.zoom.us/j/85715129440?pwd=QlJJRFBWWFRLMkxCVmcxZ1RKdlRxQT09
Meeting ID: 857 1512 9440
Passcode: 501105
