Friday, December 3, 2021 - 11:00am to 12:00pm
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Since the 1960s, particle physicists have developed a variety of data analysis strategies for the goal of comparing experimental measurements to theoretical predictions. Despite their numerous successes, these techniques can seem esoteric and ad hoc, even to practitioners in the field. In this talk, I explain how many-particle physics analysis tools have a natural geometric interpretation in an emergent “space” of collider events induced by the Wasserstein metric. This in turn suggests new analysis strategies to interpret generic point cloud data sets.
Jesse Thaler is a theoretical particle physicist who fuses techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. His current research is focused on maximizing the discovery potential of the Large Hadron Collider through new theoretical frameworks and novel data analysis techniques. Prof. Thaler joined the MIT Physics Department in 2010, and is currently a Professor in the Center for Theoretical Physics. He was a Miller Fellow at U.C. Berkeley from 2006 to 2009, and he received his Ph.D. in Physics from Harvard. He was awarded a Presidential Early Career Award for Scientists and Engineers in 2012 and a Sloan Research Fellowship in 2013. In 2020, Prof. Thaler became the inaugural Director of the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI).