LHC ML WG: WG on Machine Learning for the LHC

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The WG activities are documented on the IML web page

The LHC Machine Learning Working Group (LHC ML WG) goals:

The Inter-experimental LHC Machine Learning (IML) Working Group is focused on the development of modern state-of-the art machine learning methods, techniques and practices for high-energy physics (HEP) problems. We provide solutions, software and training beneficial to high-energy physics experiments as well as a forum where on-going work and relevant issues are discussed by the community. 
We focus on best techniques and practices, solutions to common problems, and ways to tackle open challenges. We additionally provide an interface with the machine-learning community at large, both benefiting from outside progress in HEP, as well as exporting ML solutions developed in HEP to the outside.

More details on the WG mandate are given here

WG documents and meeting agendas: see links in the right menu


  • ALICE: Gian Michele Innocenti
  • ATLAS: Michael Kagan
  • CMS: Pietro Vischia
  • LHCb: Simon Akar
  • LPCC: Lorenzo Moneta (EP-SFT)
  • TH : Riccardo Torre
  • TH : Anja Butter