Machine Learning for Fundamental Physics w/Ben Nachman, Staff Scientist at LBNL

Registration is required: https://usfca.zoom.us/webinar/register/WN_5p6hWnt4TP-mWJCZVoViQw

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 some of the key questions of fundamental physics and the experiments constructed to answer them. Furthermore, 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.

Ben Nachman is a Staff Scientist at Lawrence Berkeley National Laboratory where he leads a group that is developing quantum computing and machine learning solutions for fundamental physics questions. Dr. Nachman completed his Ph.D. in Physics with a Ph.D. minor in Statistics from Stanford University in 2016. He was a Chamberlain Fellow at Berkeley Laboratory from 2016-2020. Dr. Nachman is a member of the ATLAS Collaboration at the European Center for Nuclear Research (CERN).