BIDS Machine Learning and Science Forum — ML Methods for Particle Physics & Choreography

ML&Sci Forum

September 27, 2021
11:00am to 12:00pm
Virtual Participation

BIDS Machine Learning and Science Forum
Date: Monday, September 27, 2021
Time: 11:00 AM - 12:00 PM Pacific Time
Location: Participate remotely using this Zoom link 

ML Methods for Particle Physics & Choreography

Speaker: Mariel Pettee, LBNL
Abstract: While completing my physics PhD, I pursued independent research in developing custom ML tools to generate and analyze my own dance movements. What started as a curiosity-driven side project grew into leading several teams of researchers across academia, industry, and the arts building the state-of-the-art in ML-generated choreography using techniques including Variational Autoencoders (VAEs) and Graph Neural Networks (GNNs). In this talk, I'll discuss not only these models and their generative capabilities for artists, but also how this work could generalize to other domains interested in dynamic many-body systems such as particle physics and astrophysics.

The BIDS Machine Learning and Science Forum meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by BIDS Affiliates Uroš Seljak (professor of Physics at UC Berkeley) and Ben Nachman (physicist at Lawrence Berkeley National Laboratory), these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research.  To receive email notifications about upcoming meetings, or to request more information, please contact the organizers at berkeleymlforum@gmail.comAll interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend. 

Speaker(s)

Mariel Pettee

Chamberlain Postdoctoral Fellow, LBNL

Mariel Pettee recently defended her PhD in Physics at Yale University and is now a Chamberlain Postdoctoral Fellow at LBNL. Her research encompasses the development of custom machine learning techniques for high-energy particle physics, with a particular emphasis on generic techniques that have broad applicability across other areas of fundamental science and the arts. 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.