Learning physics from the machines

• Daniel Whiteson

Room: M R030 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.

AI-designed detectors: the interplay with object reconstruction

• Jan Kieseler

Room: M R030 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 objects directly from raw detector signals. He also used to coordinate the studies of the physics potential for the upgrades of the CMS detector targeting the end of this decade. Bringing together his AI background and the detector upgrades, he is one of the founding members of the MODE collaboration (machine learning optimised design of experiments), and in this context, he is investigating how modern AI tools can help us design the next generation of experiments.

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

• Anna Scaife

Room: M R030 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 as part of the Square Kilometre Array project, including the design of the computing and storage for a European SKA Regional Data Centre. In addition to her scientific work, Anna runs two training programs that provide bursaries for students from Southern Africa and Latin America to pursue graduate degrees in the UK focusing on big data and data intensive science. In 2014, Anna was honoured by the World Economic Forum as one of thirty scientists under the age of 40 selected for their contributions to advancing the frontiers of science, engineering or technology in areas of high societal impact. In 2017 she was awarded the Blaauw Chair in Astrophysics (prize chair) at the University of Groningen in The Netherlands for excellence in research, broad knowledge of astronomy and an outstanding international status in astronomy. In 2019, Anna received the Jackson-Gwilt Medal of the Royal Astronomical Society, awarded for outstanding invention, improvement, or development of astronomical instrumentation or techniques.

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

• Lukas Heinrich

Room: M R030 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.

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

• Gaia Grosso

Room: M R030 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 since 2019. From September 2023 she will be joining the IAIFI (https://iaifi.org/) group in Boston as a fellow, to further investigate ML based methods for offline model-independent new physics searches and to explore approaches of online anomaly detection and physics analysis.