Chair of Spatial Data Science and Statistical Learning

Sophie Potts

As part of the Data Science Hub, I contributed to the creation of our webapp collection.

Research Interests

    Methods:
  • Statistical Learning Methods, e.g. Gradient Boosting for Regression Models
  • Machine Learning
  • 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 (summer term 2024)
  • Generalized Regression (summer term 23, winter term 24)
  • Current Topics in Applied Statistics (winter term 23/24)
  • Grundlagen Bayesianische Statistik und statistisches Lernen (winter term 22/23, winter term 23/24, winter term 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.

Educational Background

  • 2019-2022: Applied Statistics, M.Sc., Georg-August University Göttingen
  • 2017-2019: Sociology, M.A., University of Leipzig
  • 2014-2017: Sociology, B.A., University of Leipzig


Publications


Talks and Posters

  • Talk Freda User Conference: 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
  • Poster IWSM 2024: Joint models for longitudinal and time-to-event data in social science research
  • Talk IWSM 2023: Prediction-based variable selection for component-wise gradient boosting
  • Poster DAGStat 2022: Prediction-based variable selection for component-wise gradient boosting