Name: Machine Learning for High Energy Physics, on and off the Lattice
Start: 2021-09-27T09:00:00+02:00
End: 2021-10-01T18:00:00+02:00
Location: ECT* - Trento

Machine Learning for High Energy Physics, on and off the Lattice

from Monday 27 September 2021 (09:00) to Friday 1 October 2021 (18:00)

Monday 27 September 2021

09:50 Welcome ECT* Director
Welcome ECT* Director
09:50 - 10:00
Room: Aula Renzo Leonardi
10:00
10:00 - 11:40
Room: Aula Renzo Leonardi
Contributions

10:00 Machine Learning to learn physics? Trials and questions - Marco Cristoforetti (FBK)

10:50 Interpreting artificial neural networks in the context of theoretical physics - Sebastian Wetzel (Perimeter Institute for Theoretical Physics)
11:40 Coffee Break in person
Coffee Break in person
11:40 - 12:20
Room: Aula Renzo Leonardi

12:20 Next session at 15:00 (Rome time)
Next session at 15:00 (Rome time)
12:20 - 15:00
Room: Aula Renzo Leonardi
15:00
15:00 - 17:10
Room: Aula Renzo Leonardi
Contributions

15:00 Machine learning phase transitions in a scalable manner on classical and quantum processors - Marina Marinkovic (ETH Zurich)

15:50 Neural autoregressive toolbox for many-body physics - Juan Carrasquilla (Vector Institute for Artificial Intelligence, Toronto (CA))

16:40 Machine learning with quantum field theories - Dimitrios Bachtis (Swansea University (UK))
Tuesday 28 September 2021
10:00
10:00 - 11:40
Room: Aula Renzo Leonardi
Contributions

10:00 Differentiable programming for fundamental physics research: status and perspectives - Tommaso Dorigo (INFN, Padova)

10:50 Critical temperature from unsupervised deep learning autoencoders - Srijit Paul (Johannes Gutenberg University Mainz)
11:40 Coffee Break in person
Coffee Break in person
11:40 - 12:20
Room: Aula Renzo Leonardi

12:20 Next session at 15:00 (Rome time)
Next session at 15:00 (Rome time)
12:20 - 15:00
Room: Aula Renzo Leonardi
15:00
15:00 - 18:30
Room: Aula Renzo Leonardi
Contributions

15:00 Higher-order interactions in statistical physics and machine learning - Ava Khamseh (School of Informatics & Higgs Centre for Theoretical Physics, The University of Edinburgh)

15:50 Machine Learning Prediction and Compression of Lattice QCD Observables - Boram Yoon (Los Alamos National Laboratory)

16:40 Machine Learning Algorithms for faster determination of Lattice QCD Hadron Correlators - Giovanni Pederiva (Michigan State University)
Wednesday 29 September 2021
10:00
10:00 - 11:40
Room: Aula Renzo Leonardi
Contributions

10:00 Flow-based generative models for ensemble generation - Gurtej Kanwar (University of Bern)

10:50 Machine learning in optical metrology - Barak Bringlotz (Sight Diagnostics, Israel)
11:40 Coffee Break in person
Coffee Break in person
11:40 - 12:10
Room: Aula Renzo Leonardi

12:10 Next session at 15:00 (Rome time)
Next session at 15:00 (Rome time)
12:10 - 15:00
Room: Aula Renzo Leonardi
15:00
15:00 - 18:20
Room: Aula Renzo Leonardi
Contributions

15:00 Observifolds: path integral contour deformation - William Detmold

15:50 Machine learning for theories with fermions - Andrei Alexandru (The George Washington University)

16:40 Quantitative analysis of phase transitions in two-dimensional XY models using persistent homology - Nicholas Sale (Swansea University)
Thursday 30 September 2021
10:00
10:00 - 11:40
Room: Aula Renzo Leonardi
Contributions

10:00 Feature extraction of machine learning and phase transition point of Ising model - Shotaro Shiba Funai (Okinawa Institute of Science and Technology)

10:50 Deep learning and holographic QCD - Koji Hashimoto (Kyoto University, Physics Department)
11:40 Coffee Break in person
Coffee Break in person
11:40 - 12:20
Room: Aula Renzo Leonardi

12:20 Next session at 15:00 (Rome time)
Next session at 15:00 (Rome time)
12:20 - 15:00
Room: Aula Renzo Leonardi
15:00
15:00 - 17:40
Room: Aula Renzo Leonardi
Contributions

15:00 Using Machine Learning to Alleviate the Sign Problem in the Hubbard Model - Thomas Luu (Forschungszentrum Jülich/University of Bonn)

15:50 Training Topological Samplers for Lattice Gauge Theories - Sam Foreman (Argonne National Laboratory)

16:40 Gauge equivariant neural networks for quantum lattice gauge theories - Di Luo (University of illinois, Urbana-Champaign)
Friday 1 October 2021
10:00
10:00 - 11:40
Room: Aula Renzo Leonardi
Contributions

10:00 Universes as Big Data - Yang-Hui He (London Institute, Royal Institution)

10:50 Why deep networks generalize - Robert de Mello Koch (Huzhou University and University of the Witwatersrand)
11:40 Coffee Break in person
Coffee Break in person
11:40 - 12:40
Room: Aula Renzo Leonardi

12:40 Waiting for Final discussion at 15:00 (Rome time)
Waiting for Final discussion at 15:00 (Rome time)
12:40 - 15:00
Room: Aula Renzo Leonardi

15:00 End
End
15:00 - 15:50
Room: Aula Renzo Leonardi

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