Dominik Stöger
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Dominik Stöger KU Eichstätt-Ingolstadt |
Since 2021, I have been an Assistant Professor (tenure-track) at KU Eichstätt-Ingolstadt at the Department of Mathematics, affiliated with MIDS. Before this role, I was a postdoctoral researcher at USC from 2019 to 2021, where I was mentored by Mahdi Soltanolkotabi. I completed my Ph.D. in Mathematics at the Technical University of Munich in 2019 under the supervision of Felix Krahmer. I also obtained my MSc and BSc degrees in Mathematics from the Technical University of Munich in 2015 and 2013, respectively.
My research interests lie in the mathematical foundations of data science. These include non-convex optimization problems in machine learning and signal processing, understanding overparameterization in modern machine learning models, and low-rank matrix recovery problems. To study these topics I often combine tools from optimization and high-dimensional probability.
A longer CV can be found here (last updated May 2024).
News:
06/2025: new preprint Near-optimal estimates for the lp-Lipschitz constants of deep random ReLU neural networks joint with Sjoerd Dirksen, Patrick Finke, Paul Geuchen, and Felix Voigtlaender
06/2025: new preprint Linear regression with overparameterized linear neural networks: Tight upper and lower bounds for implicit l1-regularization joint with Hannes Matt
05/2025: the paper Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity joint with Yizhe Zhu was accepted in COLT 2025