Institute of Advanced Study 2023 - Public Session 4th October

Contribution List

1. Machine Learning for Fundamental Physics: from the Smallest to the Largest Scales

David Shih

04/10/2023, 14:15

David Shih is a Professor in the New High Energy Theory Center at Rutgers University. His current research focuses on machine learning and its enormous potential to answer the major open questions of fundamental physics -- such as the nature of dark matter and new particles and forces beyond the Standard Model -- using big datasets from particle colliders and astronomy. His work has touched on...

8. On unreasonable efficiency of large language models for science

Andrey Ustyuzhanin, Artem Maevskiy

04/10/2023, 14:50

5. Toward Building Large HEP Models with Self-Supervised Learning

Michael Kagan

04/10/2023, 16:00

Michael Kagan is a Lead Staff Scientist at SLAC National Accelerator Laboratory. Michael received his B.S. in Physics and Mathematics at the University of Michigan, where he first worked in High Energy Physics on the CDF experiment. Michael received his Ph.D. from Harvard University, where he worked on the ATLAS experiment at CERN, focusing on some of the earliest measurements on W and Z...

6. Fast and furious AI-machines for physics at the LHC

Andrea Coccaro

04/10/2023, 16:35

Andrea Coccaro is a research scientist at INFN and a member of the ATLAS and FASER collaborations at the LHC. He obtained his PhD in Physics at the University of Genoa, and then worked as a post-doctoral researcher first at the University of Washington and then at the University of Geneva. His main interests are triggering strategies, jet classification and analyses with heavy-flavoured jets...

4. Optimal Transport for Transfer Learning and Algorithmic Fairness Problems Arising in High-Energy Physics

Mikael Kuusela

04/10/2023, 17:10

Dr. Mikael Kuusela is an Assistant Professor of Statistics and Data Science at Carnegie Mellon University. His research focuses on developing statistical methods for analyzing large and complex datasets in the physical sciences. He specializes in questions related to ill-posed inverse problems, spatio-temporal data, uncertainty quantification and statistical learning in climate science,...

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