Institute of Advanced Study 2023 - Public Session 29th September

Contribution List

1. Learning physics from the machines

Daniel Whiteson

29/09/2023, 08:30

Daniel is a particle physicist and a professor of physics at UC Irvine. He uses machine learning and statistical tools to analyze high-energy particle collisions in an effort to unravel the nature of matter and energy.

7. AI-designed detectors: the interplay with object reconstruction

Jan Kieseler

29/09/2023, 09:05

Jan Kieseler is junior research group leader at the Karlsruhe Institute of Technology, and a member of the CMS experiment at CERN, convening one of the six physics analysis groups within the experiment, the top quark group, and the ML4RECO (machine learning for reconstruction) effort, where he developed dedicated AI architectures and techniques for one-shot (end-to-end) detection of physics...

3. How stable are our foundations? Challenges facing the evaluation of downstream performance for foundation models in astrophysics

Anna Scaife

29/09/2023, 10:05

Anna Scaife is Professor of Radio Astronomy at the University of Manchester. Her research focuses on the use of artificial intelligence for discovery in data-intensive astrophysics and in 2019 she was appointed as one of the five inaugural AI Fellows of the UK’s Alan Turing Institute. She has previously led a number of projects in technical radio astronomy development and scientific computing...

4. The Vision of End-to-End ML models in HEP

Lukas Heinrich

29/09/2023, 10:40

Lukas Heinrich is a particle physicist and Professor for „Data Science in Physics“ at the Technical University of Munich. His work focuses on developing new methods for machine learning, data analysis and statistical inference for the large scale experiments at the LHC. As a member of the ATLAS experiment he is focusing on searching for phenomena beyond the Standard Model of Particle Physics.

5. How good is the Standard Model? Hunting anomalies in the LHC data and beyond

Gaia Grosso

29/09/2023, 11:15

Gaia is currently a PhD student at the University of Padua (2019-2023) and a member of the mPP ERC-funded group at CERN, developing machine learning applications for high-energy physics. Her research activity deals with statistical tools for data analysis at collider experiments, with focus on model agnostic searches for new phenomena at the LHC. She is member of the CMS collaboration at CERN...

Back to the event