Biweekly Group Meetings
The group organizes regular biweekly informal meetings that can be found at this indico. Meetings are held on Thursdays at 1 PM. Anyone in the Berkeley community (campus and lab, including affiliates) interested in hearing about or working on developing, adapting, or deploying machine learning in fundamental physics is welcome to participate. The goal of these meetings is to discuss technical and methodological aspects of work in progress and to foster cross-cutting research and collaboration. Please subscribe to [email protected] to receive reminders and updates about meetings. Due to Covid-19, all meetings are held virtually.
The code of conduct for our meetings can be found here.
- February 18, 2021. Graph Neural Networks for top quark physics (Ryan Roberts).
- February 11, 2021. Graph Neural Network (Xiangyang Ju).
- February 4, 2021. Particle Tracking (Daniel Murname).
- January 28, 2021. MADLens – Fast, accurate and differentiable simulations of weak cosmic lensing (Vanessa Bohm).
- January 21, 2021. ML Reconstruction for Neutrino Detectors (Jack Newsom and Ethan Lu).
- January 14, 2021. Self-Supervised Representation Learning for Astronomical Images (George Stein).
- December 10, 2020. Parameter Estimation using Neural Networks in the Presence of Detector Effects (Adi Suresh).
- December 3, 2020. Generative Models for the CMB (Giuseppe Puglisi).
- November 19, 2020. ML at LZ and Anomaly Detection for DESI (Scott Kravitz, Alex Kim, Vanessa Bohem).
- November 5, 2020. Welcome and Expectations, Roundtable Introductions, Plans and Goals of the Group.