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• Simon Akar (University of Cincinnati (US))
• David Rousseau (LAL-Orsay, FR)
• Lorenzo Moneta (CERN)
• Pietro Vischia (Universite Catholique de Louvain (UCL) (BE))
• Gian Michele Innocenti (CERN)
• Andrea Wulzer (CERN and EPFL)
• Loukas Gouskos (CERN)
• Riccardo Torre (CERN)

Introduction to workshop series on “AI in business” by CERN Knowledge Transfer

• Paul Hientz
• CERN Knowledge Transfer

FastCaloGAN: a tool for fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks

• Michele Faucci Giannelli (INFN e Universita Roma Tor Vergata (IT))

Pre-Learning a Geometry Using Machine Learning to Accelerate High Energy Physics Detector Simulations

• Evangelos Kourlitis (Argonne National Laboratory (US))

High Fidelity Simulation of High Granularity Calorimeters with High Speed

• Engin Eren
• Engin Eren (Deutsches Elektronen-Synchrotron DESY)
• Sascha Daniel Diefenbacher (Hamburg University (DE))

Estimating Support Size of Distribution Learnt by Generative Adversarial Networks for Particle Detector Simulation

• Kristina Jarůšková
• Kristina Jaruskova (Czech Technical University in Prague)
• Sofia Vallecorsa (CERN)

Sparse data from graph GANs

• Raghav Kansal (Univ. of California San Diego (US))

Sparse data from Variational autoencoders

• Mary Touranakou (National and Kapodistrian University of Athens (GR))
• Breno Orzari (UNESP - Universidade Estadual Paulista (BR))