In my teaching, I try to strike a careful balance between a substantive and a methodological focus combining rigorous methods training with cutting edge theoretical and empirical questions. I believe that the quickly rising demand for quantitative methods training for undergraduate and graduate students, in particular, is best met by combining it with hands-on research. This ensures that methods are taught in the context where they are applied and students not only master the most suitable techniques to address specific empirical questions but also are fully aware of their relative strengths and weaknesses. In the past years, ever larger, more disaggregate, often geo-coded empirical records have become available that – at least in theory – enable us to study societal processes at increasingly fine-grained resolution. Students have to be equipped to work with these data making full use of their potential while avoiding possible fallacies.
I currently primarily teach courses related to the SEDS data science degree at the University of Konstanz. They cover both relevant substantive and methodological aspects and are specifically designed to lead students close to the cutting-edge of research in computational social science. In addition, I also offer specialized methodology courses as part of the graduate curriculum at the Graduate Institute in Geneva. These sessions on topics such as big data analysis or agent-based computational modeling are usually offered as block courses. I have also been invited to teach workshops at a number of other universities including the European University Institute in Florence, Charles University in Prague and Zeppelin University in Friedrichshafen.
Please refer to the specific semesters listed below for an overview of each course; you may also download a detailed syllabus for each class. For courses taught at ETH Zurich and the Graduate Institute Geneva prior to Fall 2016, please refer to previous semesters.