I'm a mathematician and PostDoc in the group of Gitta Kutyniok at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence of the Ludwig-Maximilians-Universität München since summer 2026. Formerly, I was a PhD student, research and lecture assistant at the Technical University Berlin in the department for Applied Mathematics since winter 2022, supervised by Gabriele Steidl. Before that, I reseaved my Bachelor and Master degree at the TU Braunschweig in group theory.

My current research is focused on denoising manifold-valued data, especially in data processing. In this context, I'm searching for new relaxation theories and theory for tight solutions of those relaxed problems. Furthermore, I'm interested in new regularization ideas for optimal transport problems and optimal transport based image classification and pattern recognition. Beyond, I try to find novel, improved algorithms in randomized linear algebra.

Since Winter 2026, I am organizing the internal group seminar of the Math4AI chair and co-organizing the mathematical colloquium at the LMU. Feel free to contact me, we are delighted to hoste you.

I'm a junoir member of the Munich Center for Machine Learning (MCML) and admitted to the COST Action 24136 - `Interactions between Control Theory and Machine Learning` (InterCoML). Furthermore, I am a member of the Deutschen Statistischen Gesellschaft e.V. (DStatG).

For a rough introduction to my different research interests, see the overview slides below:

• Denoising Manifold-Valued Data by Relaxed Regularizations,
  joint work with R. Beinert, G. Steidl.
• Generalization of Normalized Radon Cumulative Distribution Transforms in the Light as Feature Extractors for Image Classification,
  joint work with R. Beinert, M. Beckmann.
• Stochastic Zeroth-Order Computation of Matrix Quantities,
  joint work with O. Melnyk, M. Schoen, G. Steidl, D. Lorenz, F. Schneppe, M. Winkler

Any time, I'm open for discussions.