Hammers and Nails - Machine Learning and HEP

Welcome to Hammers and Nails!

The purpose of this workshop is to bring together particle physicists and machine learning researchers to discuss the unique challenges posed by high-energy physics 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.

Topics include:

  1. Generative models, high-dimensional density estimation, and likelihood-free inference
  2. Sublinear-time pattern recognition and online learning
  3. Domain adaptation and systematic uncertainty
  4. Anomaly detection
  5. Optimal experiment design and black box optimization
  6. Generative Adversarial Network (GAN)
  7. Geometric Deep Learning
  8. U-Net


And new ideas we don’t yet know we need!

We plan an informal atmosphere, with typically 2-3 open-ended lectures each day turning into free discussion, and plenty of time for both independent work and collaboration.

Participation is by invitation only.

We offer travel support; for organizational purposes, it is crucial that you 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 for questions.


Eilam Gross
Weizmann Institute of Science

Scientific Committee

  • Kyle Cranmer
  • Cecile Germain-Renaud
    Université Paris-Sud
  • Tobias Golling
    Université de Genève
  • David Rousseau
  • Uri Shalit

Advisory Committee

  • Tamir Hazan
  • Ohad Shamir
    Weizmann Institute of Science
  • Nathan Srebro
    University of Chicago and Toyota Technological Institute at Chicago
  • Naftali Tishby
    Hebrew University of Jerusalem
  • Yair Weiss
    Hebrew University of Jerusalem