I am a postdoctoral researcher at the Tübingen AI center at the University of Tübingen, Germany working with Jakob Macke.
My research is fairly diverse. Mainly, I try to understand deep learning through theory and empirical observations to then apply it to solve scientific problems. Specifically, I am interested in
- modelling of temporal data,
- automated model discovery,
- the role of overparameterization.
On the applied side, I develop deep models for (1) automated evaluations of electrocardiogram (ECG) recordings, and (2) for systems pharmacology models, with focus on chronic kidney disease.
Background
- Currently: Postdoctoral fellow at Tübingen University, Germany.
- 2024: Ph.D. in deep learning at Uppsala University, Sweden.
- 2019: M.Sc. in systems and control from TU Delft, the Netherlands.
- 2015: B.Sc. in aerospace engineering from DHBW, Germany.
- 1994: born.
Latest research results and news
September 15, 2024, Postdoc: I started my postdoc at the Tübingen AI center at the University of Tübingen, Germany.
June 14, 2024, Ph.D.: I successfully defended my Ph.D. thesis “On Deep Learning for Low-Dimensional Representations”.
pdf DiVA slides
May 2, 2024, Accepted Paper: Our high-dimensional analysis paper got accepted to ICML. We study the combination of Principal Component Analysis (PCA) with linear regression on data from a spiked covariance model to provide precise asymptotic guarantees for the risk.
OpenReview ICML code poster
April 26, 2024, Accepted Paper: Our work on integrating Recursive Feature Machines (RFMs) into Gaussian Processes was accepted at UAI. We show that the resulting GP-RFM is a strong alternative to existing SOTA methods for tabular regression problems.
OpenReview code poster
All news can be found here