16-17 March 2021
Remote only
Europe/Paris timezone

Scientific Programme

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

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

  • 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

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

  • ML algorithms : Machine Learning development across applications

  • ML infrastructure : Hardware and software for Machine Learning

  • ML training, courses and tutorials

  • ML open datasets and challenges

  • ML for astroparticle

  • ML for experimental particle physics

  • ML for nuclear physics

  • ML for phenomenology and theory

  • ML for particle accelerators

  • Special contribution