The goal of fundamental physics is to determine the building blocks of nature and how these components interact with each other. In order to achieve this goal, scientists have built enormous experiments to measure properties of particles interactions. These experiments are generating datasets that are comparable to some of the largest industrial datasets and require complex data science algorithms for processing and analysis.
In this talk, I will introduce our collaboration models and data workflows. The bulk of my talk will describe how machine learning is revolutionizing fundamental physics and how we are developing solutions to our unique challenges, some of which will likely have broader applicability.
Staff Scientist at Lawrence Berkeley National Laboratory(LBNL), leading the cross-cutting Machine Learning for Fundamental Physics group in the Physics Division. Ph.D. in Physics; Ph.D. minor in Statistics from Stanford University in 2016, Chamberlain Fellowship at LBNL 2016-2020.