Teaching

Good teaching starts with a question, not a method. In my courses, I try to establish why something matters before getting into how it works, whether that is computational text analysis, political communication, or the logic behind quantitative inference. Coming from a first-generation university background, I know how quickly a course can lose students when the bigger picture is missing. I also think it matters that students develop a critical eye: knowing when not to apply a method, what assumptions it rests on, and where the data comes from.

My teaching covers computational methods and substantive political science. I have taught courses on computational social science and core political science topics, and have developed a workshop on scaling computational workflows with high-performance computing and workflow management systems (taught at, e.g., COMPTEXT 2026; materials on GitHub). I hold a 200-work-unit teaching certificate in Professional Teaching from TU Darmstadt and was recognised with the Athene Teaching Award in 2023. Having worked in industry before academia, I also try to make clear how these skills translate outside of research.

Courses

  • Fall 2024: Machine Learning for Social Scientists (PhD graduate school course, MZES Uni Mannheim (with Ruben Bach))
  • Spring 2024: Analysis and Comparison of Political Systems (Undergraduate proseminar, Technical University of Darmstadt)
  • Spring 2023: Quantitative Text Analysis in R (Postgraduate seminar, Technical University of Darmstadt)
  • Fall 2022 & Fall 2023: Introduction to Quantitative Methods in R (Postgraduate seminar, Technical University of Darmstadt)
  • Spring 2017: IT Security and Operating Systems (Undergraduate tutorial, University of Applied Sciences Karlsruhe)
  • Fall 2016: Programming II (Undergraduate tutorial, University of Applied Sciences Karlsruhe)
  • Spring 2016: Introduction to Business Informatics (Undergraduate tutorial, University of Applied Sciences Karlsruhe)