RESEARCH INTERESTS

Methods:

• Statistical Learning Methods, e.g. Gradient Boosting for Regression Models


• Maschinelles Lernen


• Regression Models, e.g. Mixed Models, Generalised Additive Models for Location, Scale and Shape (GAMLSS)


• Joint Models for longitudinal and time-to event data


• (Quantitative) Methods of Empirical Social Research

Applications:

• Research on Social Inequality


• Labour Market Sociology

TEACHING

• Praktikum Statistische Modellierung (SoSe 2024, SoSe 2025)


• Generalized Regression (SoSe 23, SoSe 24, SoSe 25)


• Current Topics in Applied Statistics (WiSe 23/24)


• Grundlagen Bayesianische Statistik und statistisches Lernen (WiSe 22/23, WiSe 23/24, WiSe 24/25)

Information on the content of the courses can be found hereor in the module descriptions.


Thesis offers can be found here or by personal arrangement. I am happy to support own suggestions for thesis topics related to my reserach interests, both regarding model choice and data set search.

PUBLICATIONS

TALKS AND POSTERS

• IWSM 2025 Joint models for rare events (Poster)


• DagStat 2025: Joint models for rare events (Talk)


• Joint models for longitudinal and time-to-event data in the social sciences (Talk)


• CompStat 2024: Joint models for longitudinal and time-to-event data in the social sciences (Talk)


• IWSM 2024: Joint models for longitudinal and time-to-event data in social science research (Poster)


• IWSM 2023: Prediction-based variable selection for component-wise gradient boosting (Talk)


• DAGStat 2022: Prediction-based variable selection for component-wise gradient boosting (Poster)