Modern machine learning has had an outsized impact on many scientific fields, and particle physics is no exception. What is special about particle physics, though, is the vast amount of theoretical knowledge that we already have about many problems in the field, as well as the daunting deluge of data coming from flagship experiments like the Large Hadron Collider (LHC). In this talk, I will explain how one can teach a machine to "think like a physicist" by embedding theoretical principles into advanced machine learning architectures. At the same time, I will advocate that physicists must learn how to "think like a machine" to maximize the physics reach of the LHC. These joint developments are leading to a new kind of "centaur science" that, analogously to the mythical centaur, draws half from particle physics and half from machine learning.
Biography:
Professor 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 (LHC) through new theoretical frameworks and novel data analysis techniques. Prof. Thaler is an expert in jets, which are collimated sprays of particles that are copiously produced at the LHC, and he studies the substructure of jets to enhance the search for new phenomena and illuminate the dynamics of gauge theories. He is also interested in new strategies to probe the nature of dark matter at the LHC and beyond, as well as in the theoretical structures and experimental signatures of supersymmetry. (Source)
Location: Cory Hall, 373