Statistical Methods for the Physical Sciences Webinar

— 2:30pm

Speaker:
MICHAEL KAGAN, Lead Scientist, SLAC National Accelerator Laboratory

High Energy Physics experiments, like those at the Large Hadron Collider at CERN, have developed intricate data analysis pipelines to search for rare hints of new particles and forces. With the goal of maximizing our sensitivity to signs of new physics, how can we optimize our data analysis pipelines, which rely on a mixture of physics-driven computations and data-driven ML models, and optimize future experiments to get the most of out the data? This talk will discuss progress towards building differentiable data analysis and simulation components that are amenable to gradient-based optimization and challenges that arise in gradient estimation in these settings.

—

Michael Kagan is a Lead Staff Scientist at SLAC National Accelerator Laboratory. He received his Ph.D. in physics from Harvard University, and his B.S. in Physics and Mathematics at the University of Michigan. After his postdoctoral work at SLAC National Laboratory, Michael was a Panofsky Fellow at SLAC from 2016 to 2021. Michael’s work focuses on the study of the Higgs Boson and the search for new physics at the ATLAS experiment at the LHC, and on the development of Machine Learning for fundamental physics.

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Event Website:
https://www.stat.cmu.edu/stamps/webinar/michael-kagan-nov10/