Collision Course: Particle Physics Meets Machine Learning

The Simons Foundation’s mission is to advance the frontiers of research in mathematics and the basic sciences.

Simons Foundation

Registration for this lecture will close at 2 p.m. the day of the event. Review the complete list of entrance requirements below.

<h2>This lecture is part of the Machine Learning in the Natural Sciences Presidential Lecture Series in Mathematics and Computer Science.</h2>

Modern machine learning has had an outsized impact on many scientific fields, and particle physics is no exception.&nbsp; What is special about particle physics, though, is the vast amount of theoretical knowledge we already have about many problems in the field and the daunting deluge of data coming from flagship experiments like the Large Hadron Collider.In this Presidential Lecture, Jesse Thaler will explain how one can teach a machine to "think like a physicist" by embedding theoretical principles into advanced machine learning architectures.&nbsp; At the same time, Thaler will advocate that physicists must learn how to "think like a machine" by capitalizing on advances in computational optimization to perform precision calculations and identify potential signals of new physics.

<a href="https://physics.mit.edu/faculty/jesse-thaler/" title="https://physics.mit.edu/faculty/jesse-thaler/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Thaler</a> 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. Thaler joined the MIT Physics Department in 2010 and is currently a professor at the university’s Center for Theoretical Physics. In 2020, he became the inaugural director of the NSF Institute for Artificial Intelligence and Fundamental Interactions.

REGISTRATION REQUIREMENTS<ul><li>You must be 18+ to attend this event.</li><li>Registration will close at 2:00 p.m. the day of the event</li><li>Please only register yourself and use your complete first and last name as it appears on your photo ID when filling in the form.</li><li>If you have a guest you would like to bring with you to this event, please send them the link to register so they can sign up on their own.</li></ul>BUILDING ENTRY PROTOCOL<ul><li>Provide valid photo ID.</li><li>Present your digital or printed Eventbrite ticket confirmation; make sure it is for the correct event and that the name on it matches your ID.</li><li>Limited seating is available for this in-person event and is on a first-come, first-served basis.</li><li>All attendees must be pre-registered. Walk-in entry will be denied.</li></ul>Please note that by entering the Simons Foundation, you are attesting that you are not experiencing COVID symptoms and are not knowingly positive for COVID.SCHEDULEDoors open: 5:30 p.m. (No entrance before 5:30 p.m.)Lecture: 6:00 p.m.–7:00 p.m. (Admittance closes at 6:20 p.m.)The Gerald D. Fischbach Auditorium complies with the Americans with Disabilities Act and offers accessible seating to visitors with special access needs.

<a href="https://www.simonsfoundation.org/sf-events/?type=simons-foundation-lectures" title="https://www.simonsfoundation.org/sf-events/?type=simons-foundation-lectures" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Presidential Lectures</a> are free public colloquia at the Simons Foundation centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite anyone interested in the topic to join us for this weekly lecture series.

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Collision Course: Particle Physics Meets Machine Learning

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Collision Course: Particle Physics Meets Machine Learning

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