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.

Program

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

Invited Speakers

Invited Panelists

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

Please submit your anonymized extended abstract through the link below by June 9th, 23:59 AOE:

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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.

Venue

ML4Astro 2025 will take place on the campus of the University of British Columbia (about 20 min from downtown Vancouver by car, 45 min by bus).

Volunteering as a Reviewer

Our goal is to ensure that all extended abstracts will receive at least two independent reviews, in a double-blind process. As we aim for high quality and constructive reviews, we do not want to ask volunteers to review many papers, which translates into needing a large pool of volunteers.

As a result we are always looking for volunteers to help us review workshop submissions. If you are interested in serving as a reviewer, please let us know via the link below before June 2nd:

We may reach out to provide further details and confirm your availability.

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SOC

Contact Us

For any questions, please send an email to ml4astro2025[at]gmail.com.

Sponsors

We are grateful for the support from our sponsors.