Machine Learning for High Energy Physics, on and off the Lattice
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.
The workshop will take place as a hybrid meeting, with limited on-site participation, provided that the pandemic situation permits this.
- Barak Bringoltz (Sight Diagnostics),
- Juan Carrasquilla (Vector Institute),
- Marco Cristoforetti (FBK),
- William Detmold (Massachusetts Institute of Technology),
- Robert De Mello Koch (University of the Witwatersrand/South China Normal University),
- Tommaso Dorigo (INFN),
- Shotaro Shiba Funai (Okinawa Institute and Science and Technology),
- Koji Hashimoto (Osaka University),
- Yang-Hui He (City, University of London & Oxford University),
- Gurtej Kanwar (Massachusetts Institute of Technology),
- Ava Khamseh (The University of Edinburgh),
- Thomas Luu (Forschungszentrum Jülich/Universität Bonn),
- Srijit Paul (University of Mainz),
- Sam Foreman (Argonne National Laboratory),
- Boram Yoon (Los Alamos National Laboratory),
- Di Luo (University of Illinois at Urbana-Champaign),
- Sebastian Johann Wetzel (Perimeter Institute),
- Andrei Alexandru (George Washington University),
- Marina Marinkovic (ETH),
- Dimitrios Bachtis (Swansea University).