Physics in Machine Learning Workshop

May 29, 2019 - University of California, Berkeley

This workshop will focus on substantive connections between machine learning (including but not limited to deep learning) and physics (including astrophysics). Namely, we are interested in topics like imbuing physical laws into training (e.g., physics regularization of layers), learning new physical phenomena from learned models, physics-constrained reinforcement learning, prediction outside training parameters, causal inference, and the (physical) interpretability of models. Registration has closed for this event.

Head over to the Preliminary schedule/abstracts of talks

Code of Conduct

Scientific Organizing Committee

  • Joshua Bloom (UC Berkeley)
  • Laura Waller (UC Berkeley)
  • Fernando Perez (UC Berkeley)
  • David Hogg (NYU)
  • Kyle Cramner (NYU)
  • Benjamin Nachman (LBNL)

Local Organizing Committee

  • Stacey Dorton
  • Stefan van der Walt
  • Francois Lanusse
  • Peter Nugent

If you have any questions please contact us at

Organization Schedule

March 1 - Workshop website live, solicitations sent

April 1 - Preliminary participants list posted (Updated: May 14)

May 1 - Preliminary schedule/abstracts of talks posted (Updated: May 1) - if you'd like to give a poster, please fill out the Google Form

May 15 - Final schedule posted

May 28 - (Evening) Arrival

May 29 - This Workshop

May 29 (Evening) - Joint reception for both workshops

May 30 - (optional) MMA Inference Workshop

May 31 - Departure


Those asking for travel support should hold off on making their own arrangements for now. We will be in touch separately on the travel logistics.

These are the workshop hotels if you are planning on making your own arrangements:

Immediately following this Workshop, at the same venue, we will be putting on a related workshop for Inferencing in Multi-messenger Astrophysics (MMA). Participants are welcome in both. More...

Sponsored by the Gordon and Betty Moore Foundation