Machine Learning for Astrophysics

Workshop at the Thirty-ninth International Conference on Machine Learning (ICML 2022), July 22nd, Baltimore, MD


As modern astrophysical surveys deliver an unprecedented amount of data, from the imaging of hundreds of millions of distant galaxies to the mapping of cosmic radiation fields at ultra-high resolution, conventional data analysis methods are reaching their limits in both computational complexity and optimality. Deep Learning has rapidly been adopted by the astronomical community as a promising way of exploiting these forthcoming big-data datasets and of extracting the physical principles that underlie these complex observations. This has led to an unprecedented exponential growth of publications with in the last year alone about 500 astrophysics papers mentioning deep learning or neural networks in their abstract. Yet, many of these works remain at an exploratory level and have not been translated into real scientific breakthroughs.

The goal of this ICML 2022 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.

An important aspect to the success of Machine Learning in Astrophysics is to create a two-way interdisciplinary dialog in which concrete data-analysis challenges can spur the development of dedicated Machine Learning tools. This workshop is designed to facilitate this dialog and will include a mix of interdisciplinary invited talks and panel discussions, providing an opportunity for ICML audiences to connect their research interests to concrete and outstanding scientific challenges.

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

  • 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 accurate uncertainty quantification
  • Methods for improved interpretability of models
  • (Astro)-physics informed models, models which preserve symmetries and equivariances
  • Deep Learning for accelerating numerical simulations
  • Benchmarking and deployment of ML models for large-scale data analysis

Contributions on these topics do not necessarily need to be Astrophysics-focused, works on relevant ML methodology, or similar considerations in other scientific fields, are welcome.

Important Dates

Note that dates are not final and still subject to change

  • Submission deadline: May 23rd
  • Author Notification: June 10th
  • Slideslive upload deadline for online talks: July 1st (SlidesLive will only guarantee your recording will be available in time for the conference if you respect the official July 1st deadline, so we highly encourage you to submit it by then.)
  • Camera-ready paper deadline: July 10th
  • Camera-ready poster deadline: July 15th (see instructions below)
  • Workshop date: July 22nd

Instructions for Posters

To prepare your poster, please use the following dimensions:

  • Recommended size: 24”W x 36”H, 61 x 90cm (A1 portrait)
  • Maximum size: 48”W x 36”H, 122 x 90 cm (A0 landscape)
  • Please use lightweight paper for printing, poster boards may not be available for workshops, but you will be able to tape your poster to the wall using provided tape.

In addition to the physical poster, please visit this page for instructions on how to upload the camera-ready version of your poster to the ICML website: You will be able to submit your poster at this link:


Confirmed Invited Speakers and Panelists

Josh Bloom
UC Berkeley

Katie Bouman

Daniela Huppenkothen

Jakob Macke
Tübingen University

Laurence Perreault-Levasseur
University of Montreal

Dustin Tran

George Stein
UC Berkeley

Soledad Villar
Johns Hopkins University

Tentative schedule

The following schedule is only tentative and subject to modifications, a final schedule will be announced ahead of the workshop.

9:15-10:00 Keynote Katie Bouman: Computational Imaging in astronomy
10:00-10:30 Spotlight 2 talks
10:30-11:30 Pannel Discussion Building an interdisciplinary future
11:30-12:30 Poster Session Morning Session
12:30-13:30 Lunch break
13:30-14:15 Keynote Jakob Macke: Simulation-Based Inference
14:15-15:00 Spotlight 3 talks
15:00-16:00 Poster Session Afternoon Session
16:00-16:45 Keynote Dustin Tran: Uncertainty Quantification and calibration in ML
16:45-17:30 Spotlight 3 talks
17:30-18:15 Keynote Soledad Villar: Symmetries and Equivariance in ML for Physics
18:15-19:15 Panel Discussion Enabling Scientific Discoveries with ML

Call for Abstracts

We invite all contributions in connection with the theme of the workshop as described here, from the fields of Astrophysics and Machine Learning, but also from other scientific fields facing similar challenges.

Original contributions and early research works are encouraged. Contributions presenting recently published or currently under review results are also welcome. The workshop will not have formal proceedings, but accepted submissions will be linked on the workshop webpage.

Submissions, in the form of extended abstracts, need to adhere to the ICML 2022 format (LaTeX style files), be anonymized, and be no longer than 4 pages (excluding references). After double-blind review, a limited set of submissions will be selected for contributed talks, and a wider set of submissions will be selected for poster presentations.

Please submit your anonymized extended abstract through CMT at before May 23rd, 23:59 AOE.

Logistics and FAQs

ICML 2022 is currently planned as an in-person event. As such, this workshop is currently assuming a hybrid format, with physical poster sessions and in-person speakers, but with support for virtual elements to facilitate participation of people unable to travel. The exact format might evolve in the coming months, depending on the COVID conditions. We encourage all interested participants (regardless of their ability to physically travel to ICML) to submit an extended abstracts.

Registration for ICML workshops is handled through the main ICML conference registration here. The workshop will be able to guarantee the ICML registration for participants with accepted contributions (i.e. these participants will not be subject to the ICML lottery).

More information about logistical details will be shared here in the coming weeks.

Inquiries regarding the workshop can be directed to


Scientific Organizing Committee for the ICML 2022 Machine Learning for Astrophysics workshop:

Francois Lanusse
CNRS (Co-Chair)

Marc Huertas-Company
IAC (Co-Chair)

Vanessa Boehm
UC Berkeley

Brice Ménard
Johns Hopkins University

Xavier J. Prochaska
UC Santa-Cruz

Uros Seljak
UC Berkeley

Francisco Villaescusa-Navarro
Simons Foundation

Ashley Villar
Pennsylvania State University