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 here or 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

1. Brüne, J. F., Potts, S., & Bergherr, E. (2026). ParliamentLab: An interactive application for exploring voting behavior in the European Parliament. European Political Science, 1-14.
   DOI: https://doi.org/10.1017/S1682098326100629





2. Gluesing, L., Potts, S., Knieper, L., Giganti, F., Tsaur, I., Gassenmaier, S., ... & Uhlig, J. (2026). MRI-derived prostate volume assessment: manual segmentation versus two-axis ellipsoid approximation and its implications for PSA density. European Radiology, 1-12.
   DOI: https://doi.org/10.1007/s00330-026-12900-1





3. 
   Potts, S., Rappl, A., Kurz, K., & Bergherr, E. (2026). Bridging the Gap: Introducing Joint Models for Longitudinal and Time-to-Event Data in the Social Sciences. Methodology, 22(1), Article e18465. DOI: 10.5964/meth.18465.





4. Potts, Sophie, Martin Refisch, and Elisabeth Bergherr (2026). Comparing Information Criteria for Prediction-based Variable Selection. In: Proceedings of the 40th International Workshop on Statistical Modelling (IWSM). Oslo, Norway.





5. Potts, Sophie and Elisabeth Bergherr (2025). Joint Models for Rare Events. In: Proceedings of the 39th International Workshop on Statistical Modelling (IWSM). Limerick, Ireland.





6. Potts, S., Bergherr, E., Reinke, C., & Griesbach, C. (2023). Prediction-based variable selection for component-wise gradient boosting. The international journal of biostatistics, 20(1), 293–314. DOI: 10.1515/ijb-2023-0052.

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)