I recently completed my PhD at the Mathematical Institute of Utrecht University under the supervision of Sjoerd Dirksen. My research was on the theory of machine learning, specifically the memorization capacity of neural networks and robustness properties of random neural networks. Before this, I studied mathematics at RWTH Aachen University, where I completed my master’s thesis under the supervision of Holger Rauhut.

In my free time, I enjoy running and building computers, most recently i build my home server. Right now, I’m learning about home networking and containerization and I’m thinking about exploring 3D printing soon.

Publications

• Memorization With Neural Nets: Going Beyond the Worst Case [paper] [code]
  with S. Dirksen and M. Genzel, in Journal of Machine Learning Research, 25(347):1–38, 2024
• Near-optimal estimates for the $\ell^p$-Lipschitz constants of deep random ReLU neural networks [preprint]
  with S. Dirksen, P. Geuchen, D. Stöger and F. Voigtlaender, preprint arXiv:2506.19695 [stat.ML], 2025

Talks and Posters

• Memorization capacity and robustness of neural networks: PhD defense layman’s talk [slides]
  Talk at my PhD defense, January 2026 in Utrecht
• Memorization With Neural Nets: Going Beyond the Worst Case [poster]
  Poster at Symposium on Sparsity and Singular Structures, February 2024 in Aachen
• Memorizing Structured Data With Neural Nets: A Randomized Algorithm
  Talk at MI PhD Seminar, March 2023 in Utrecht
• Memorization With Neural Nets: Going Beyond the Worst Case [slides]
  Talk at AIM networking event, December 2022 in Utrecht

Theses

• Memorization capacity and robustness of neural networks [thesis]
  PhD Thesis supervised by Sjoerd Dirksen at RWTH Aachen University
• Compressive Sensing and Stable Embeddings with Structured Random Matrices under Generative Priors
  Master’s Thesis supervised by Holger Rauhut at RWTH Aachen University
• Integral Deferred Correction Methods for Singularly Perturbed Problems
  Bachelor’s Thesis supervised by Sebastian Noelle at RWTH Aachen University

Teaching

As a teaching assistant I supervised exercise groups of students. Apart from this, I designed practical exam and homework exercises for Introduction to Machine Learning and supervised students one-on-one in the Seminar on Machine Learning. I assisted in all of the following courses:

• Introduction to Probability Theory
  Spring 2024, Spring 2023 and Fall 2021 at Utrecht University
• Introduction to Machine Learning
  Fall 2023, Fall 2022 and Spring 2022 at Utrecht University
• Seminar on Machine Learning
  Spring 2023 at Utrecht University
• Programming for Mathematics
  Spring 2022 at Utrecht University
• Analysis
  Spring 2024 and Spring 2021 at Utrecht University
• Algorithms and Data Structures
  Spring 2016 at RWTH Aachen University