Research

I work on methodological, educational and applied research in statistics and data science.
Learn more about my research interests and projects below.

My methodological research lies at the intersection of Bayesian statistics and machine learning. I have worked on Bayesian nonparametric models and efficient posterior inference for network analysis and probabilistic clustering. Here you can find some of my papers, posters and presentations.

Statistical modeling of sparse networks with overlapping communities


Probabilistic frameworks for temporal biclustering


My research in statistics and data science education aims to support instructors with providing effective qualitative feedback to students, study the benefits of learning statistics with self-collected data and promote teaching of Bayesian methodologies.

Automated grading workflows for formative feedback


See you at the Posterior Line! Learning Bayesian statistics with an online racing game


Advancing Bayesian Thinking in STEM


I enjoy supporting applied scientists with the formulation of their research goals into formal statistical questions, the identification of appropriate statistical methods and the implementation of statistical analyses. I have collaborated with scientists in behavioral nutrition to study how individuals can be nudged towards healthier and sustainable food choices.

Behavioral interventions to promote healthy and sustainable diets


Impact of organic and local labels on vegetable consumption

Factors contributing to the use of pre-ordering systems