Hammers & Nails 2022

Machine Learning Meets Astro & Particle Physics
Dates: August 3rd - August 11th, 2022 | Location: Weizmann Institute of Science, Israel

Following the success of the 2017 and 2019 Hammers & Nails workshops, and in the spirit of the post-COVID era, we believe it is time to restart.

We are happy to announce a follow-up Hammers & Nails 2022 workshop.

The purpose of the workshop is to bring together HEP/Astrophysicists and machine learning researchers to discuss the unique challenges posed by high-energy physics and Astro data analysis problems.

While some of these problems are simply waiting to be matched with well-established techniques (the pairing of hammers and nails), many require or inspire the development of novel methods around cutting-edge research challenges...

We expect the workshop to be an essential moment to foster new collaborations and shape the next two years of applied ML research

in particle physics.


Topics include:

  1. Transformers, Attention, large language models (LLM), etc.
  2. New types of generative models
  3. Molecules (symmetries, graphs, generative models, etc.)
  4. Uncertainty quantification and Bayesian NNs
  5. Algorithmic reasoning
  6. Optimal transport
  7. Implicit layers
  8. Variational inference / probabilistic reconstruction
  9. Self-supervised learning

We plan an informal atmosphere, with typically 3-4 open-ended lectures each day turning into a free discussion, and plenty of time for both independent work and collaboration.

Participation is by invitation only.

We offer travel support; for organizational purposes, you must provide us with the Visiting Scientist and reimbursement details that can be found in the "forms" page on this website, and in the email that you should have received from our admin. Please do not hesitate to contact us with questions.

Previous Workshops

Local Organizer

Eilam Gross
Weizmann Institute of Science

Scientific Organising Committee

  • Peter Battaglia
    Google Deep Mind
  • Kyle Cranmer
    NYU & University of Wisconsin-Madison
  • Tobias Golling
    Université de Genève
  • Danilo Jimenez Rezende
    Google Deep Mind
  • Sven Krippendorf
    LMU, Munich
  • Maurizio Pierini
  • Tilman Plehn
    University of Heidelberg
  • David Rousseau
  • Gary Shiu
    University of Wisconsin-Madison
  • Slava Voloshynovkiy
  • Université de Genève
  • Tamir Hazan
  • Technion