Machine Learning for Theory - POSTPONED

Riccardo Torre (CERN)

Due to the current situation with Covid-19 the event is postponed.

The new dates will be communicated in due time.


There is growing interest in Machine Learning applications for high energy theory and phenomenology. This is motivated on the one hand by the recent paramount progresses in algorithms, software and hardware resources, and on the other hand by the large amount of data available in our field.

The aim of this workshop is to survey the current activity, contribute to shaping the key questions, and identifying promising directions. By bringing together experts and practitioners with different physics interests and backgrounds, ranging from Monte Carlo simulations to String Theory, we expect to stimulate cross-field interactions and to help consolidate Machine Learning related activities in the TH community.

Machine Learning applications for Monte Carlo simulations will be discussed  during the week of overlap with the workshop "Taming the accuracy of event generators".


  • Andrea Coccaro (INFN, Genova)
  • Guido D'Amico (Parma U.)
  • Jennifer Ngadiuba (CERN)
  • Fabian Ruehle (CERN)
  • Veronica Sanz (Sussex U. & Valencia U., IFIC)
  • Luca Silvestrini (INFN, Rome & CERN)
  • Riccardo Torre (CERN & INFN, Genova)
  • Andrea Wulzer (CERN & EPFL & Padova U.)

Applications will close April 15, 2020

Application for participation
  • Andrea Coccaro
  • andrea wulzer
  • Fabian Ruehle
  • Jennifer Ngadiuba
  • Jesse Thaler
  • Juan Rojo
  • Koji Hashimoto
  • Michael Spannowsky
  • Riccardo Torre
  • Veronica Sanz Gonzalez
TH workshop secretariat
The agenda of this meeting is empty