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POSTPONED to 2021 | Machine Learning for High Energy Physics, on and off the Lattice


Staff ECT*
Monday, 23 March, 2020 - 09:00

Machine learning (ML) has been recently used as a very effective tool for the study and prediction of data in various fields of physics, from statistical physics to theoretical high energy physics. The aim of this workshop is to bring together active researchers on ML and Physics to interact and initiate a collaborative effort to investigate timely problems on Lattices and Theoretical High Energy Physics. Hence we invite scientists with research areas covering a broad spectrum to present their work. Some of the topics which will be highlighted are supervised and unsupervised identification of phase transitions on lattice models, applications of generative algorithms in the production of lattice configurations, applications of machine learning estimators in observables in Lattice QCD and the connection of ML with Renormalization Group as well as the gauge/gravity correspondence.

Registration period: 
21 Jul 2020 to 29 Aug 2020


Andreas Athenodorou Università di Pisa
Dimitrios Giataganas National and Kapodistrian University of Athens
Biagio Lucini Swansea University
Enrico Rinaldi Arithmer Inc. Tokyo
Kyle Cranmer New York University
Costantia Alexandrou University of Cyprus & The Cyprus Institute