My research interest lies in constructing statistical models and exploring their interactions with various areas in the natural and technical sciences. I currently work with generalised linear mixed models (GLMMs), graphical models, and their multivariate and semiparametric extensions. Leading the Applied Statistics Laboratory (aStatLab), I engage with fields such as biology, agriculture, and environmental sciences. Over many years, I developed methods for multivariate non-Gaussian responses by combining GLMMs and graphical models to meet the demands of hundreds of statistical analyses.

I lecture the following courses:
 Study Design and Analysis: A basic 5 ECTS course explaining principles of statistical modelling for Master’s students in Agrobiology, biological, and environmental sciences, focusing on the analysis of variance techniques.
 PhD courses for natural and technological sciences (also for health sciences and humanities):
    Introduction to R: A short course on the statistical program R.
    Basic Statistical Analysis: A course on statistical modelling based on generalised linear models.
    Mixed Models: A course on Gaussian and generalised linear mixed models for structured dependencies.
Advanced courses for master students in statistics and mathematics:
    Mixed Models with R: On generalised linear mixed models.
    Graphical Models and Related Multivariate Techniques.
I am the lecturer and course responsible for these courses (not an exhaustive list).

I lead the Applied Statistical Laboratory (aStatLab), providing various types of statistical support for members of the Faculty of Natural Sciences:

1. Statistical consultation system for simple, daily questions.
2. Short ad hoc internal projects for well-defined questions, typically lasting a week or less.
3. Statistical assistance for PhD and Master students, including supervision of thesis-related analyses.

 These activities do not involve costs for the clients, the aStaLab reserves the right to limit the total workload of the activities.