I am a doctoral student in computer science at the University of Tübingen and an IMPRS-IS fellow advised by Philipp Hennig. My research focuses on **numerical methods for machine learning**, in particular **probabilistic numerics** which interprets numerical methods as learning procedures. I am also closely collaborating with John P. Cunningham at Columbia University’s Zuckerman Institute.

In my free time I love to do sports, in particular any kind of cycling.

- Probabilistic Numerics
- Numerical Analysis
- Gaussian Processes

Doctoral Degree in Computer Science

University of Tübingen, IMPRS-IS fellow

MSc in Engineering Physics, 2019

KTH Royal Institute of Technology

MSc in Mathematics, 2019

TU München

Recent Preprints and Papers

Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical …

Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which …

Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, large kernel matrices. Iterative …

Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear …

Linear systems are the bedrock of virtually all numerical computation. Machine learning poses specific challenges for the solution of …

Software, Tech Reports, etc.

Probabilistic Numerics (PN) interprets classic numerical routines as inference procedures by taking a probabilistic viewpoint. This …

Accurate representation of uncertainty in classification problems can be as critical as high prediction accuracy in computer vision, …

In this project we analyzed time-optimal control problems with linear dynamics and numerical methods for solving them. When …