Rationale

As modern astrophysical surveys deliver an unprecedented amount of data, from the imaging of billions of distant galaxies to the innermost region of supermassive blackholes, the integration of AI into scientific workflows, which have stringent accuracy requirements, becomes increasingly crucial to exploit the full information content of these datasets. In addition, the multimodal and publicly accessible nature of astronomical data creates a fertile development ground for large scientific models, with applications far beyond astronomy.

Following successful iterations of this workshop at ICML 2022 and ICML 2023, our continued goal for this workshop series is to bring together Machine Learning researchers and domain experts in the field of Astrophysics to discuss key opportunities, create new synergies and help promote the large-scale application of AI for science. We expect this workshop to appeal to ICML audiences as an opportunity to connect their research interests to concrete and outstanding scientific challenges.

We welcome in particular submissions that target or report on the following non-exhaustive list of problems:

Foundation models for astrophysics and their (potential) impact on discovery
Integration of LLMs and autonomous agents in scientific workflows
Efficient high-dimensional Likelihood-based and Simulation-Based Inference
Robustness to covariate shifts and model misspecification
Anomaly and outlier detection, search for rare signals with ML
Methods for model interpretability
(Astro)-physics informed models, symmetry and equivariance-preserving models
Deep Learning for accelerating numerical simulations
Benchmark datasets aligned with any of the above themes

We encourage both submissions on these topics with an astrophysics focus, as well as more methodologically oriented works with potential applications in the physical sciences. Submissions focusing on preliminary work and recent work submitted elsewhere are also welcome.

See Call For Abstracts

Program

Note: As a co-located event, registration to ICML 2025 is not required to attend this workshop.

Invited Speakers

Shirley Ho

CCA/Polymathic

Berthy Feng

MIT/IAIFI

Invited Panelists

Siddharth Mishra-Sharma

Anthropic/Boston University

Ann Zabludoff

University of Arizona/Steward Observatory

Joshua S. Speagle (沈佳士)

University of Toronto

Workshop Schedule

Coming soon - The detailed schedule will be announced after the abstract submission and review process.

Accepted Contributions

Coming soon - Accepted papers will be listed here after the review process.

Call for Abstracts

Important Dates

Abstract Submission Deadline: June 9th (23:59, AoE)
Notification Deadline: June 22nd
Camera-Ready Paper Deadline: July 11th
Camera-Ready Poster Deadline: July 18th
Workshop Date: July 20th (ICML, Vancouver)

Submission Guidelines

Submissions should be in the form of extended abstracts (2-4 pages) following the ICML 2025 format.
Submissions should clearly articulate the connection between machine learning and astrophysics
Work that has been recently published at other venues is eligible for submission, provided it is clearly indicated
Submissions will be reviewed by our program committee based on their technical quality, clarity, and relevance to the workshop

Submission Process

The submission portal is now closed. Please volunteer as a reviewer and register to attend the workshop!

Registration

To register for the workshop (either in-person or remotely), submit the form below. The registration deadline is July 6th, 23:59 AOE. Workshop registration is free.

Workshop Registration