WELCOME
About the event
A revolution is beginning, melding computationally enhanced science with machine learning in ways that respect and amplify both domains. The University of California's academic campuses and National Laboratories are at the forefront, but in different ways that would benefit from a dialog. This Workshop will promote that dialog in application to the physical sciences.
Where
Virtual Symposium
(Link provided to registrants)
When
Monday, October 26, 2020
8:30AM - 3:00PM PDT

Hosted by the University of California, Irvine
Sorry. Registration is now closed.
Topics

Machine learning for changes of scale in physical science models

Scientific applications including chemistry, materials science, earth sciences and fluid dynamics

Physics of, and by means of, machine learning and optimization methods

Applicable mathematics and formal methods

Graph-centered machine learning methods
KEYNOTE ADDRESS BY:
Stanley Osher, PhD
University of California, Los Angeles
Professor of Mathematics & Computer Science, Electrical Engineering & Chemical and Biomolecular Engineering
Director of Special Projects, Institute for Pure and Applied Mathematics
Event Speakers

Brian Spears, PhD
Director, Cognitive Simulation Initiative
Weapons and Complex Integration
Lawrence Livermore National Laboratory

Kipton Barros, PhD
Staff scientist, Physics and Chemistry of Materials | Executive committee member, Center for Nonlinear Studies, Theoretical Division
Los Alamos National Laboratory

Kieron Burke, PhD
Chancellor's Professor, Departments of Chemistry and Physics
University of California, Irvine

Gowri Srinivasan, PhD
Group Leader, Los Alamos National
Laboratory | Project Leader, ASC Machine Learning Project
Los Alamos National Laboratory

Eric Mjolsness, PhD
Professor, Departments of Computer Science and Mathematics
University of California Irvine.

Stephan Mandt, PhD
Assistant Professor of
Computer Science
University of California, Irvine