Modern machine learning and artificial intelligence are starting to fundamentally change how we analyse 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 of particle physics. This talk will provide an overview of recent, exciting developments with a focus on model agnostic discovery strategies — including the use of in-situ generative models, ultra-fast outlierdetection, and new experimental results.
Biography:
Gregor Kasieczka joined Universität Hamburg in 2017 where he is a professor for machine learning in particle physics. His work focuses on searches for exotic new particles with the CMS experiment and on developing new techniques for simulation and data analysis in fundamental physics. He is an author of the first textbook on machine learning for physicists “Deep Learning For Physics Research”.