Speaking Sessions
The road to AI-based discoveries
Modern machine learning and artificial intelligence fundamentally changing how we analyze huge volumes of data in particle physics and adjacent scientific disciplines. These breakthroughs promise new insights into major scientific questions such as the nature of dark matter or the existence of physical phenomena beyond the standard model.
This keynote will provide an overview of recent, exciting developments with a focus on model agnostic discovery strategies (including first experimental results!), boosting model performance by incorporating physics knowledge, ultra-fast surrogate simulations, as well as foundation models that simultaneously solve multiple tasks across multiple datasets.
Biography
After a PhD in Heidelberg and a postdoctoral researcher phase at ETH Zürich, Gregor Kasieczka joined Universität Hamburg in 2017 where he is a full professor for machine learning in particle physics. His work focuses on discovering exotic new particles with the CMS experiment and on developing new techniques for data analysis — including anomaly detection, generative machine learning, and foundational models — in fundamental physics. He is an author of the first textbook on machine learning for physicists “Deep Learning For Physics Research”.