About

I am a PhD candidate and research associate in the School of Mathematics and Natural Sciences at the University of Wuppertal, supervised by Prof. Dr. Peter Zaspel. My research focuses on the scalability of kernel-based methods and Gaussian processes, with particular emphasis on randomized low-rank approximations and objective-aware approaches.

Before joining Wuppertal, I completed my MSc in Mathematics at the University of Bayreuth. My academic background combines mathematics and computer science, with interests in machine learning, uncertainty quantification, and scientific computing.

News

June 2026 New preprint "Variational Free Energy Pivot Selection for Pivoted Cholesky" (joint work with Peter Zaspel) is out: arXiv.
April 2026 Selected as one of 200 young researchers worldwide to attend the 13th Heidelberg Laureate Forum in Heidelberg, Germany in September 2026.
March 2026 Upcoming poster presentation at the Numerical Linear Algebra Workshop, Foundations of Computational Mathematics in Vienna, Austria, in July 2026.
February 2026 Talk at the minisymposium Kernel Approximation Methods (MS054), WCCM–ECCOMAS in Munich, Germany in July 2026.

Research Interests

Gaussian Processes

Scalable probabilistic regression and uncertainty quantification with kernel-based models.

Kernel-Based Methods

Efficient approximation methods for kernel matrices arising in machine learning and scientific computing.

Randomized Numerical Linear Algebra

Randomized low-rank approximation and sketching methods for large-scale kernel matrices.

Objective-Aware Approximation

Low-rank structures designed for inference objectives rather than matrix reconstruction alone.