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

Speaker List

• Andrei Alexandru (The George Washington University)
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    Machine learning for theories with fermions

• Dimitrios Bachtis (Swansea University (UK))
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    Machine learning with quantum field theories

• Barak Bringlotz (Sight Diagnostics, Israel)
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    Machine learning in optical metrology

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

• Marco Cristoforetti (FBK)
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    Machine Learning to learn physics? Trials and questions

• Robert de Mello Koch (Huzhou University and University of the Witwatersrand)
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    Why deep networks generalize

• William Detmold
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    Observifolds: path integral contour deformation

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

• Sam Foreman (Argonne National Laboratory)
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    Training Topological Samplers for Lattice Gauge Theories

• Koji Hashimoto (Kyoto University, Physics Department)
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    Deep learning and holographic QCD

• Yang-Hui He (London Institute, Royal Institution)
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    Universes as Big Data

• Gurtej Kanwar (University of Bern)
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    Flow-based generative models for ensemble generation

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

• Di Luo (University of illinois, Urbana-Champaign)
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    Gauge equivariant neural networks for quantum lattice gauge theories

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

• Marina Marinkovic (ETH Zurich)
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    Machine learning phase transitions in a scalable manner on classical and quantum processors

• Srijit Paul (Johannes Gutenberg University Mainz)
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    Critical temperature from unsupervised deep learning autoencoders

• Giovanni Pederiva (Michigan State University)
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    Machine Learning Algorithms for faster determination of Lattice QCD Hadron Correlators

• Nicholas Sale (Swansea University)
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    Quantitative analysis of phase transitions in two-dimensional XY models using persistent homology

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

• Sebastian Wetzel (Perimeter Institute for Theoretical Physics)
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    Interpreting artificial neural networks in the context of theoretical physics

• Boram Yoon (Los Alamos National Laboratory)
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    Machine Learning Prediction and Compression of Lattice QCD Observables