ICML Workshop on Machine Learning for Astrophysics

ICML 2023 will host a second workshop on Machine Learning for Astrophysics, which will be held in Honolulu, Hawai’i on July 29th. A Call for Abstracts is open until May 19th, 23:59 AoE.
After a successful initial iteration of this workshop at ICML 2022, the goal for this one-day workshop is to bring together Machine Learning researchers and domain experts in the field of Astrophysics to discuss the key open issues which hamper the use of Deep Learning for scientific discovery. In particular, submissions that target or report on the following non-exhaustive list of problems are welcome:

  • Efficient high-dimensional inference
  • Robustness to covariate shifts and model misspecification
  • Anomaly and outlier detection, search for rare signals with ML
  • Methods for accurate uncertainty quantification
  • Methods for improving interpretability of models
  • (Astro)-physics informed models, symmetry and equivariance-preserving models
  • Methods of emulation / acceleration of simulation models
  • Benchmarking and deployment of ML models for large-scale data analysis

Submissions on these topics with an astrophysics focus are encouraged, as well as more methodologically oriented works with potential applications in the physical sciences.

Being hosted at ICML, this workshop is designed to foster a two-way interdisciplinary dialog in which concrete data-analysis challenges can spur the development of dedicated Machine Learning tools and vice-versa. It will feature a mix of interdisciplinary invited talks and panel discussions, as well as contributed talks and posters selected from submitted contributions.

Confirmed speakers and panelists:

  • Dmitry Duev, Weights & Biases
  • Chelsea Finn, Stanford
  • Yashar Hezaveh, University of Montreal, Mila
  • David W. Hogg, New York University
  • Peter Melchior, Princeton
  • Anna Scaiffe, University of Manchester
  • Ross Taylor, Meta AI
  • Yuan-Sen Ting, Australian National University


More information is available on the workshop website: https://ml4astro.github.io/icml2023 and inquiries can be directed to .

The organizing committee
Marc Huertas-Company, IAC
Francois Lanusse, CNRS
Brice Ménard, Johns Hopkins University
Laurence Perreault-Levasseur, University of Montreal, Mila
Xavier Prochaska, UC Santa Cruz
Uros Seljak, UC Berkeley
Ashley Villar, Penn State
Francisco Villaescusa-Navarro, Simons Foundation