I am currently working as a post-doc in data science at Roche to develop statistical forecasting tools for drug formulation development. Previously, I worked at the Department of Statistics at the University of Innsbruck, from which I graduated in 2020. My PhD thesis, under the supervision of Georg J. Mayr and Achim Zeileis, was on advanced statistical methods for probabilistic forecasting within the domain of environmental science. The statistical models employed range from parametric to non-parametric machine learning approaches, whereas the applications include one-dimensional, multivariate and circular responses.

My research stands at the intersection between computational statistics and natural science with a focus on probabilistic forecasting. In this framework, I am a (co-)developer of several R-packages for estimating distributional random forests and graphically evaluating probabilistic models. I enjoy working on descriptive and predictive problems, being comfortable with the entire data science pipeline, from restructuring different types of input data, to analysis and modeling, to visualizing the results in a web application.