Machine Learning and the Physical Sciences Workshop

36th annual conference on Neural Information Processing Systems (NeurIPS)

BIDS Faculty Affiliate Benjamin Nachman is a co-organizer of the Machine Learning and the Physical Sciences Workshop being held in New Orleans, LA, on December 3, 2022, as a part of the 36th annual conference on Neural Information Processing Systems (NeurIPS). NeurIPS 2022 will be a hybrid conference with a physical component at the New Orleans Convention Center during the first week, and a virtual component the second week.

The workshop aims to provide an informal, inclusive and leading-edge venue for research and discussions at the interface of machine learning (ML) and the physical sciences. This interface spans (1) applications of ML in physical sciences (ML for physics), (2) developments in ML motivated by physical insights (physics for ML), and most recently (3) convergence of ML and physical sciences (physics with ML) which inspires questioning what scientific understanding means in the age of complex-AI powered science, and what roles machine and human scientists will play in developing scientific understanding in the future.

Call for Papers, Reviewers, Sponsors
This workshop aims to bring together physical scientists and machine learning researchers who work on applying machine learning to problems in the physical sciences, specifically at the intersection of these fields – physics, chemistry, mathematics, astronomy, materials science, biophysics, and related sciences – or on using physical insights to understand and improve machine learning techniques. The submission deadline is September 22, 2022.

Researchers are invited to submit work particularly in the following areas or areas related to them:

  • ML for Physics: Applications of machine learning to physical sciences including astronomy, astrophysics, cosmology, biophysics, chemistry, climate science, earth science, materials science, mathematics, particle physics, or any related area;
  • Physics in ML: Strategies for incorporating prior scientific knowledge into machine learning algorithms, as well as applications of physical sciences to understand, model, and improve machine learning techniques;
  • ML in the scientific process: Machine learning model interpretability for obtaining insights to physical systems; Automating multiple elements of the scientific method for discovery and operations with experiments;
  • Any other area related to the subject of the workshop, including but not limited to probabilistic methods that are relevant to physical systems, such as deep generative models, probabilistic programming, simulation-based inference, variational inference, causal inference, etc.

Contact: With questions and comments, please contact


Benjamin Nachman

Staff Scientist, Physics Division, LBNL

Ben Nachman is a Staff Scientist in the Physics Division at LBNL where he is the group leader of the cross-cutting Machine Learning for Fundamental Physics group. He was a Churchill Scholar at Cambridge University and then received his Ph.D. in Physics and Ph.D. minor in Statistics from Stanford University. After graduating, he was a Chamberlain Fellow in the Physics Division at Berkeley Lab. Nachman develops, adapts, and deploys machine learning algorithms to enhance data analysis in high energy physics. He is a member of the ATLAS Collaboration at CERN.