I am a statistician with a focus on probabilistic (Bayesian) methods currently working as a Full Professor of Computational Statistics at TU Dortmund University, Department of Statistics. Having originally studied psychology and mathematics, my core research is nowadays located somewhere between statistics and machine learning, with applications in almost all quantitative sciences.
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Thus, it should not come as a surprise that Bayesian methods are increasingly used in statistical and computational inference in both science and industry. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. In my lab, we are working on a wide range of research topics related to the development, estimation, evaluation, implementation, or application of Bayesian methods. This includes, among others, uncertainty quantification, prior specification, simulation-based inference, as well as model comparison.
If you are a scientist who wants to collaborate with me, a PhD student or PostDoc candidate interested in my research, or a student looking for a thesis topic, please reach out to me! Please also check out the open positions in my lab, which additionally includes a list of thesis topics for Bachelor and Master students.