معرفی
Gregor Kasieczka is a Professor at the University of Hamburg, affiliated with the Faculty of Mathematics and Natural Sciences and the Department of Physics. His research focuses on applying machine learning techniques to high-energy physics, particularly for LHC event generation, detector simulations, and jet reconstruction. He collaborates extensively with physicists and computer scientists globally.
Research Trends: His recent work explores equivariant neural networks for particle jets, invertible models for detector simulations, and uncertainty quantification in ML-driven calibration. He also investigates generative models (GANs) to amplify event samples and improve resolution beyond traditional methods.
Collaborations: Gregor works with prominent researchers like Tilman Plehn, Benjamin Nachman, and Jesse Thaler, contributing to interdisciplinary advancements in ML applications for particle physics.
Labs & Teams: He leads the Gruppe Kasieczka at the University of Hamburg, specializing in machine learning for experimental particle physics and detector-level simulations.
