The road to AI-based discovery
by
Abstract: Machine learning and AI have quickly turned into indispensable tools for modern particle physics. They both greatly amplify the power of existing techniques - such as supercharging supervised classification - and enable qualitatively new ways of extracting information - such as anomaly detection for unsupervised discovery. After briefly introducing the environment of collider based particle physics, this talk will review key developments and new directions in machine learning applied to data analysis.
Bio: Gregor Kasieczka joined Universität Hamburg in 2017 where he is a 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”.