Ben Nachman (LBNL, UC Berkeley)

Title: Discovering Unanticipated New Physics With Machine Learning
Abstract: Modern machine learning tools are allowing us to explore high energy physics data in new ways. These approaches may enable discoveries that were unthinkable with existing strategies. I introduce these new “anomaly detection” methods, which differ from typical approaches developed for non-scientific applications. While these methods are just starting to be applied to experimental data, there is an exciting program ahead of us as these tools become more developed and widely deployed.

Jesse Thaler (MIT/IAIFI)
Title: Weak Supervision for the Strong Force
Abstract: The strong nuclear force is governed by the interactions of quarks and gluons. Because of confinement, though, quarks and gluons can never been seen in isolation, so “quark” and “gluon” labels are fundamentally ambiguous. In this talk, I show how to leverage weak supervision to disentangle quarks and gluons without labeled training data. This technique is then applied to public data from the Large Hadron Collider. This analysis incorporates a wide range of machine learning tools — including topic modeling, permutation-invariant networks, simulation-based inference, and optimal transport — together with key insights from quantum field theory.

Max Tegmark (MIT/ IAIFI/CBMM)
Title: AI for physics & physics for AI
Bio: Max Tegmark is a professor doing AI and physics research at MIT as part of the Institute for Artificial Intelligence & Fundamental Interactions and the Center for Brains, Minds and Machines. He advocates for positive use of technology as president of the Future of Life Institute. He is the author of over 250 publications as well as the New York Times bestsellers “Life 3.0: Being Human in the Age of Artificial Intelligence” and "Our Mathematical Universe: My Quest for the Ultimate Nature of Reality". His most recent AI research focuses on intelligible intelligence.