I am a researcher at the Chair for AI in Healthcare and Medicine (Daniel Rückert), TUM.

My PhD (Dr. rer. nat) is from LMU Munich, (Department of Statistics).

During that time, I worked as a researcher and data scientist at the Institute for Medical Information Processing, Biometry, and Epidemiology.

My past research focused primarily on Bayesian approaches to model uncertainty. I also worked on federated learning, specifically validation. For more information, see the Research section. Alternatively, you may look in my thesis that summarizes many anspects of my past work.

Currently, I am interested in uncertainty quantification and Bayesian approaches in the medical context.

Specifically, this includes inference algorithms for Bayesian neural networks, handling uncertain data, differential privacy, and (soft-) inductive bias through a Bayesian lens.

Given my background in economics, I also have some interest in (financial) econometrics and time series analysis. For instance, I am interested in the efficient usage of sparse data in stock markets, leveraging Bayesian methods (ofc).