Machine Learning for Physics

This workshop brings together leading researchers working at the intersection of modern data science and fundamental physics. The speakers span a wide range of topics — from topological data analysis in gauge theories and QCD, to machine learning techniques for particle physics including jet tagging, generative inference, and diffusion models, to applications in lattice field theories, quantum computing and condensed matter. The workshop aims to foster cross-disciplinary dialogue and chart new directions for data-driven approaches in theoretical and experimental physics.

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Invited Speakers:

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• David Shih (Rutgers)
• Vinicius Mikuni (KMI, Nagoya)
• Gert Aarts (Swansea)
• Jeff Giansiracusa (Durham/Erlangen)
• Huilin Qu (CERN)
• Andrea Tirelli
• Lingxiao Wang (RIKEN)
• Min-Hsiu Hsieh (Foxconn)
• Muhammad Usman (Melbourne)
• Mathis Gerdes (MIT)
• Fernando Romero-Lopez (Bern)
• Gurtej Kanwar (Edinburgh)
• Jae-Hun Jung (POSTEC)
• Luigi Del Debbio (University of Edinburgh)
• Alberto Ramos (Universitat de Valencia)
• Guilherme Catumba (Milan, Biccoca University)

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Organizers:

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• Miranda Cheng
• Gary Shiu
• Brandan Robinson
• Marco Fazzi
• Kai-Feng Chen
• Cheng-Wei Chiang

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More information can be found here.