My research and teaching focuses on the interface between Data Science and Marketing. I like applying modern econometric methods to novel data sources in the digital economy—and teach students how to do that with R and Python.
In my research project Measuring Self-Preferencing on Digital Platforms, we develop an approach to test for self-preferencing through platform recommendations.
“Do Political Conflicts Influence Daily Consumption Choices?”—published in the Journal of Economic Behavior and Organization—combines vast smartphone location data and textual analysis of newspaper articles to assess the impact of political conflict on consumption.
For the newest project, we created a Generative AI app that provide personalized feedback to students. Published in the International Journal of Research in Marketing, Generative AI for Scalable Feedback to Multimodal Exercises describes how well that works (spoiler: quite well!)
My teaching experience includes:
- Designing and teaching Introduction to Data Science courses in
RandPythonfor students from all fields, - Teaching a Marketing Analytics course that enables students to solve marketing problems with econometrics, programming, and substantive marketing skills,
- Teaching an Empirical Customer Analytics seminar about machine learning methods in marketing analytics, and
- Designing and teaching TechAcademy’s blended learning Data Science with R courses.
In my free time, I help preparing the next generation for the digital future as a member of the supervisory board at TechAcademy e.V., a non-profit organization teaching coding and promoting tech literacy.
On this website, you will find information about my Academic Research and Teaching, my Curriculum Vitae, and my passion project—TechAcademy e.V..
I currently work at the European Central Bank. The views expressed in my research and on this website are my personal ones and do not necessarily reflect those of my employer.
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