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Artificial Intelligence meets Fundamental Physics

About this event

Please join us for an MPS-CISE Co-sponsored Distinguished Lecture:

Prof. Jesse Thaler, from the MIT Center for Theoretical Physics, 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.

Jesse Thaler joined the MIT Physics Department in 2010 and is currently a Professor in the Center for Theoretical Physics. From 2006 to 2009, he was a fellow at the Miller Institute for Basic Research in Science at the University of California, Berkeley. He received his Ph.D. in Physics from Harvard University in 2006, and his Sc.B. in Math/Physics from Brown University in 2002. In 2020, Prof. Thaler became the inaugural Director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions.


Modern machine learning has had an outsized impact on many scientific fields, and fundamental physics is no exception. What is special about fundamental physics, though, is the vast amount of theoretical, experimental, and observational knowledge that we already have about many problems in the field. Is it possible to teach a machine to “think like a physicist” and thereby advance physics knowledge from the smallest building blocks of nature to the largest structures in the universe? In this talk, I argue that the answer is “yes”, using the example of particle physics at the Large Hadron Collider to highlight the fascinating synergy between theoretical principles and machine learning architectures. I also argue that by fusing the “deep learning” revolution with the time-tested strategies of “deep thinking” in physics, we can galvanize research innovation in artificial intelligence more broadly.

Register to watch the webinar:  https://nsf.zoomgov.com/webinar/register/WN_ONeGyjGXQjWVzNKevIN3DQ