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Abstract
Artificial intelligence has revolutionized the analysis of large-scale data in particle physics, significantly enhancing the discovery potential for new fundamental laws of nature. In this talk, Dr. Qu will provide an overview of the rapidly evolving role of machine learning in high energy physics, highlighting recent developments and their impact on experiments at the Large Hadron Collider. The talk will also explore cutting-edge efforts to develop large-scale, general-purpose foundation models tailored for particle physics and discuss how these models could shape the future of discovery in the field.
Bio
Dr. Huilin Qu is a staff research physicist at CERN. He received his B.S. degree from Peking University in 2014, and Ph.D. from University of California, Santa Barbara in 2019. He was a postdoctoral researcher at UCSB (2019-2020) and, subsequently, a senior research fellow at CERN (2020-2022). His research has focused on searches for new physics and measurements of the Higgs boson properties with the CMS experiment at the CERN LHC, particularly using novel approaches and advanced machine learning techniques. He played a key role in searches for Higgs boson decay to a pair of charm quarks, for Higgs boson pair production in the high-momentum regime, and for supersymmetric partners of the top quark. In addition, he proposed a series of novel deep-learning approaches for jet tagging, which substantially improved the performance and have been widely adopted at the LHC and beyond.
Video: https://vshare.sjtu.edu.cn/open/adc1b6a6defd7681036bd052d268cf00ee20f742a0e257a4eea189842a18229d