In my research I try to understand deep learning through theory and empirical observations. I am interested in
- low-dimensional data representation learning,
- neural networks dynamics and implicit biases,
- the role of overparameterization,
- unsupervised representation learning.
On the applied side, my research uses deep models for the evaluation of electrocardiogram (ECG) recordings to (1) support physicians with diagnosis and (2) explore the information within those recordings.
- Fall 2024: Post-doc, in your group? Contact me! :)
- Summer 2024: will finish my Ph.D. 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
November 6, 2023, Accepted Paper: The first work from my research visit at Misha Belkins Lab at UCSD was accepted at the UniReps workshop at NeurIPS. We studied the emergence of similar representations in Recursive Feature Machines and in GPs with Automatic Relevance Determination.
OpenReview NeurIPS23 workshop
July 4, 2023, Accepted Paper: Another work ours using ECGs + deep learning got accepted at PLOS Neglected Tropical Diseases. We show that deep learning models can be used to detect Chagas disease from ECGs, aiding in early detection.
doi medRxiv code models
June 28, 2023, Invited Talk: A talk on our preprint survey paper of deep networks for system identification got accepted at the 2023 European Research Network System Identification (ERNSI) workshop in Stockholm. We got a special 1h presentation slot.
All news can be found here