Gregor Kasieczka is a Professor (W2) for Machine Learning in Particle Physics at Universität Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, affiliated with the Institute of Experimental Physics. His academic journey includes a doctorate from Universität Heidelberg (2013), postdoctoral research at ETH Zürich (2013–2017), and progression from Junior Professor (2017–2022) to full Professor at Universität Hamburg (since 2023). He leads the Emmy Noether Group Kasieczka, focusing on machine learning applications in particle physics. Key responsibilities include: Speaker of KISS consortium (BMBF-funded AI-based simulation initiative) Platform coordinator at Cluster of Excellence 'Quantum Universe' Former Software/Algorithms coordinator for DIG-UM community First convener of CMS Collaboration's Machine Learning group Research interests span machine learning-driven particle physics, including jet substructure analysis, generative models for calorimeter simulations, anomaly detection, and quantum theory applications. His publications emphasize AI/ML integration with experimental particle physics, particularly for LHC/CMS data analysis. Notable research trends from recent works include advances in generative models for fast calorimeter simulation, foundation models for LHC data, self-supervised learning in astrophysics, and model-agnostic anomaly detection techniques. He directs significant projects on AI-based simulation and coordinates cross-institutional ML initiatives. No scientific awards are documented in the provided materials. Kasieczka leads multiple research consortia and advises projects at the intersection of AI and fundamental physics, including generative model development and anomaly detection frameworks. His group actively contributes to the CMS collaboration and quantum universe research platforms.
