Method(olog)ische Grundlagen der Statistik und ihre Anwendungen



Julian Rodemann

PhD Candidate (Doktorand)


Institut für Statistik
Ludwigstr. 33
80539 München

Raum: L 142
Telefon: +49 89 2180 3925

Website: Google Scholar
Website: Github
Website: ResearchGate

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 Personal Website:


  • Causality (Exercise, summer 23)
  • Statistics for Geosciences (Exercise, winter 21/22, Lecture and exercise winter 22/23)
  • Wahrscheinlichkeitstheorie und Inferenz I (Probability Theory and Inference 1, exercise, winter 21/22)
  • Wirtschafts- und Sozialstatistik (economic and social statistics, lecture/exercise, winter 21/22 and winter 22/23)
  • Statistik IV im Nebenfach (statistics as minor IV, lecture/exercise, summer 22)
  • Wahrscheinlichkeitstheoretische Grundlagen (basic probability theory, exercise, summer 22)

Research Interests

  • Imprecise Probabilities
    • Generalized Bayes
    • Imprecise Gaussian Processes
    • Neighborhood Models
  • Robust Surrogates in Bayesian Optimization
  • Superset Learning/Inference
  • Weak Supervision
  • (Causal Inference)



  • Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin (2023): Approximately Bayes-Optimal Pseudo Label Selection. 39th Conference on Uncertainty in Artificial Intelligence (UAI), Pittsburgh, USA. Proceedings of Machine Learning Research (to appear).
  • Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin (2023): Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement. 39th Conference on Uncertaint in Artificial Intelligence (UAI), Pittsburgh, USA. Proceedings of Machine Learning Research (to appear).
  • Julian Rodemann, Christoph Jansen, Georg Schollmeyer, Thomas Augustin (2023): In All Likelihood(s): Robust Selection of Pseudo-Labeled Data. 13th International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA), Oviedo, Spain. Proceedings of Machine Learning Research (to appear).
  • Julian Rodemann, Dominik Kreiss, Eyke Hüllermeier, Thomas Augustin (2022): Levelwise Data Disambiguation by Cautious Superset Classification. 15th International Conference on Scalable Uncertainty Management (SUM) in Paris, France. Lecture Notes in Artificial Intelligence, Springer.



  • Julian Rodemann (2023): Learning Under Weak Supervision: Insights from Decision Theory. Young Statistician’s
    Lecture Series (YSLS). International Biometric Society (IBS) Early Career Working Group, Germany


Fachstudienberatung Bachelor (Hauptfach) Statistik und Data Science (PO 2021/2010)

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