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Speaker: David Miller (University of Chicago) – Title: Black Boxes or Interpretable Models? Applications of Machine Learning, Symmetries, and Domain Knowledge to High-Dimensional Problems in Particle Physics.
June 18 @ 4:00 pm - 5:00 pm
Research Progress Meeting
Date: June 18, 2024
Time: 4:00- 5:00 pm
Location: Sessler Conference Room- 50A-5132 [In-Person and HYBRID]
Speaker: David Miller (University of Chicago)
Title: Black Boxes or Interpretable Models? Applications of Machine Learning, Symmetries, and Domain Knowledge to High-Dimensional Problems in Particle Physics.
Abstract: The world of artificial intelligence (AI) and machine learning (ML) has undergone what some scientists refer to as the “3rd Age of AI” due to the confluence of developments in Algorithms, Computing Resources, and Big Data. Particle Physics has benefitted from, and in many ways strengthened and advanced, progress in AI/ML for decades due to its proliferation of enormous data sets, complex instrumentation, and enormous computing infrastructure. However, there exist both known and unknown deficiencies in our ability to explain “why” some AI/ML models yield a certain result. In this talk, I will discuss some of the context and applications of AI/ML in experimental particle physics. I will then focus on a few projects ongoing in my group that we believe target important problems relevant to the use of machine learning, symmetries, and domain knowledge in particle physics.
Join Zoom Meeting
https://lbnl.zoom.us/j/95679892182?pwd=RU5xU2dDRFNabnR1U3pQMklkYWFIdz09
Meeting ID: 956 7989 2182
Passcode: 169037