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Tutorial
ML for Physics and Physics for ML
Shirley Ho · Miles Cranmer

Mon Dec 06 09:00 AM -- 01:00 PM (PST) @ Virtual

Physics research and deep learning have a symbiotic relationship, and this bond has become stronger over the past several years. In this tutorial, we will present both sides of this story. How has deep learning benefited from concepts in physics and other sciences? How have different subfields of physics research capitalized on deep learning? What are some yet-unexplored applications of deep learning to physics which could strongly benefit from machine learning? We will discuss the past and present of this intersection, and then theorize possible directions for the future of this connection. In the second part of this talk, we will outline some existing deep learning techniques which have exploited ideas from physics, and point out some intriguing new directions in this area.

Author Information

Shirley Ho (Flatiron institute/ New York University/ Carnegie Mellon)

Shirley Ho is a group leader and acting director at Flatiron Institute at Simons foundation, a research professor of physics and an affiliated faculty at Center for Data Science at NYU. Ho also holds associate (adjunct) professorship at Carnegie Mellon University and visiting appointment at Princeton University. She was a senior scientist at Berkeley National Lab from 2016-2018 and a Cooper-Siegel Development chair professor at Carnegie Mellon University before that. Ho was a Seaborg and Chamberlain Fellow from 2008-2011 at Berkeley Lab, after receiving her PhD in Astrophysics from Princeton University in 2008 under supervision of David Spergel. Ho graduated summa cum laude with a B.A. in Physics and a B.A. in Computer Science from UC Berkeley. A cited expert in cosmology, machine learning applications in astrophysics and data science,her interests are using deep learning accelerated simulations to understand the Universe, and other astrophysical phenomena. She tries her best to balance her love for the Universe, the machine and life especially during these crazy times.

Miles Cranmer (Princeton University)

Miles Cranmer is an Astro PhD candidate trying to accelerate astrophysics with AI. Miles is from Canada and did his undergraduate in Physics at McGill. He is deeply interested in the automation of science, particularly aspects that are not yet tractable with existing machine learning, such as experiment planning, simulation, and theory. He works on symbolic regression, graph neural networks, normalizing flows, and learned simulation. He is hugely interested in symbolic ML, since, as he argues, symbolic models seem to be a surprisingly efficient basis for describing our universe.

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