IML Machine Learning Working Group

Virtual (everywhere and nowhere)

Virtual (everywhere and nowhere)


Meeting will be by video only on Zoom.

Join Zoom Meeting

Meeting ID: 918 3662 6922
Password:  check email

Recording available at:

Videoconference Rooms
IML Machine Learning Working Group
Zoom Meeting ID
Simon Akar
Alternative host
Riccardo Torre
Useful links
Join via phone
Zoom URL
    • 15:00 15:05
      News 5m
      Speakers: Andrea Wulzer (CERN and EPFL) , David Rousseau (LAL-Orsay, FR) , Gian Michele Innocenti (CERN) , Lorenzo Moneta (CERN) , Loukas Gouskos (CERN) , Dr Pietro Vischia (Universite Catholique de Louvain (UCL) (BE)) , Riccardo Torre (CERN) , Simon Akar (University of Cincinnati (US))
    • 15:05 15:15
      Introduction to workshop series on “AI in business” by CERN Knowledge Transfer 10m
      Speakers: CERN Knowledge Transfer, Paul Hientz
    • 15:15 15:20
      Question time 5m
    • 15:20 15:45
      FastCaloGAN: a tool for fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks 25m
      Speaker: Michele Faucci Giannelli (INFN e Universita Roma Tor Vergata (IT))
    • 15:45 15:50
      Question time 5m
    • 15:50 16:15
      Pre-Learning a Geometry Using Machine Learning to Accelerate High Energy Physics Detector Simulations 25m
      Speaker: Evangelos Kourlitis (Argonne National Laboratory (US))
    • 16:15 16:20
      Question time 5m
    • 16:20 16:45
      High Fidelity Simulation of High Granularity Calorimeters with High Speed 25m
      Speakers: Engin Eren, Engin Eren (Deutsches Elektronen-Synchrotron DESY) , Sascha Daniel Diefenbacher (Hamburg University (DE))
    • 16:45 16:50
      Question time 5m
    • 16:50 17:15
      Estimating Support Size of Distribution Learnt by Generative Adversarial Networks for Particle Detector Simulation 25m
      Speakers: Kristina Jaruskova (Czech Technical University in Prague) , Kristina Jarůšková, Dr Sofia Vallecorsa (CERN)
    • 17:15 17:20
      Question time 5m
    • 17:20 17:32
      Sparse data from graph GANs 12m
      Speaker: Raghav Kansal (Univ. of California San Diego (US))
    • 17:32 17:35
      Question time 3m
    • 17:35 17:47
      Sparse data from Variational autoencoders 12m
      Speakers: Breno Orzari (UNESP - Universidade Estadual Paulista (BR)) , Mary Touranakou (National and Kapodistrian University of Athens (GR))
    • 17:47 17:50
      Question time 3m