FI Computational Methods and Data Science Journal Club
Flatiron Institute, 162 5th Avenue
Speaker: Mariel Patee (Berkeley)
Title: Interdisciplinary Machine Learning for Fundamental Physics (and Art)
Abstract: Machine learning (ML) has the potential to reveal fascinating insights into so many interesting datasets, though it's usually not obvious how to represent our data to these models most effectively. But this challenge need not be solved in isolation on a case-by-case basis -- even seemingly highly-specialized data like particle deposits in the ATLAS detector at CERN share common characteristics with data from other diverse disciplines. In this talk, I’ll discuss some of my work on ATLAS dealing with various representations of particles as sequences, images, and 3D point clouds for a variety of ML applications. I’ll also present recent results from leading several independent teams of researchers at the forefront of the ML-generated choreography space as an example of how my scientific research has been bolstered by working across disciplinary borders.
Bio: Mariel Pettee (Physics PhD, Yale University, 2021) is a Chamberlain Postdoctoral Research Fellow at Lawrence Berkeley National Laboratory and guest researcher at the CCA. Her research encompasses the development of custom machine learning (ML) techniques for high-energy particle physics and cosmology that have broad applicability across other areas of science and art. As a choreographer, director, dancer, and performer, she also uses ML as an artistic medium. Since 2017, she has led multiple independent teams of researchers to develop custom ML models to generate choreography based on 3D motion capture of her own movements. Prior to her PhD, she earned her Bachelors in Physics & Mathematics from Harvard University and her Masters in Physics at the University of Cambridge (Trinity College) as a Harvard-Cambridge Scholar
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