Mar 16 – 17, 2021
Remote only
Europe/Paris timezone

The workshop will take place remotely only, on mornings : 9AM to 1PM

Machine Learning is now potentially impacting many aspects of physics.

This workshop covers current development with Machine Learning at IN2P3 and CEA-IRFU, following up from the Jan 2020 workshop

Submission of contributions is now closed. 

The following non exclusive Tracks have been defined (a contribution can be relevant to 2 tracks, preferably not more):

  1. ML for data reduction : Application of Machine Learning to data reduction, reconstruction, building/tagging of intermediate object

  2. ML for analysis : Application of Machine Learning to analysis, event classification and fundamental parameters inference

  3. ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

  4. Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis

  5. ML algorithms : Machine Learning development across applications

  6. ML infrastructure : Hardware and software for Machine Learning

  7. ML training, courses and tutorials

  8. ML open datasets and challenges

  9. ML for astroparticle

  10. ML for experimental particle physics

  11. ML for nuclear physics

  12. ML for phenomenology and theory

  13. ML for particle accelerators

  14. Special contribution

The workshop will be on zoom. Connection details are sent by mail to registrants.

Please make sure you are subscribed to on IN2P3 listserv to keep up to date with ML. 

Organisation : Valérie Gautard (CEA/IRFU), David Rousseau (IJCLab)


Remote only