I grew up in a small village directly adjacent to the city of Hamburg, Germany. In the fall of 2011, I began studying Social Sciences at the Justus-Liebig University Gie├čen. During my studies, I worked as a student assistant at the Chair of Comparative Politics and as a statistics tutor at the Chair of International Comparative Social Research Methods. After completing my Bachelor's degree in 2014, I enrolled in the master's program in Political Science at the University of Mannheim where my coursework focused on quantitative methods and comparative politics. I also worked as a student assistant at the Chair of Political Economy and participated in the EITM Europe Summer School. After my graduation in 2016, I joined the Center for Doctoral Studies in Social and Behavioral Sciences (CDSS) and became a doctoral researcher at the Collaborative Research Center (SFB) 884.

My research has a strong empirical focus and primarily centers around the study of party competition, party politics, and legislative decision-making. Besides other things, I investigate diffusion and learning processes among parties within and across the different European multiparty systems and how these processes lead to endogenously evolving dynamics of party competition. I am also doing research on advanced quantitative methods, particularly Bayesian statistics, latent variable models, and parametric and semiparametric spatial econometric techniques. Using observational data and stochastic simulations, I study the consequences of different methodological problems associated with the application of spatial regression models for substantive inferences regarding social science theories.

Throughout my employment at the SFB 884, I contribute survey questions to the German Internet Panel (GIP) and design survey experiments in order to analyze how characteristics of the legislative decision-making process in democracies affect public evaluations of the final policy outcome. In this context, I also study how different questioning techniques can improve prevalence estimates of sensitive traits at the aggregate level.