Ludwig Bothmann


I am a postdoctoral researcher at the Chair of Statistical Learning and Data Science in the Department of Statistics at the LMU Munich. I have a Diploma Degree (Dipl. - Stat.) in Statistics and a PhD (Dr. rer. nat.) in Statistics with focus on efficient statistical analysis of video and image data. During my PhD I worked at the Chair of Applied Statistics in Social Sciences, Economics and Business at the LMU, under the supervision of Prof. Dr. Göran Kauermann. Afterwards I worked for nearly three years as Data Scientist and Data Science Lead for an insurance company, implementing various data science use cases from first idea to production.
Since August 2020, I am part of this chair. My main research interests include fairML, uncertainty quantification, causal inference, interpretable ML and application of ML and DL in other sciences. I am leading the research group on Causal and Fair Machine Learning.


Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Ludwig.Bothmann [at]

You Can Find me on


Prior teaching:



  1. Bothmann, L., & Peters, K. (2024). Fairness von KI – ein Brückenschlag zwischen Philosophie und Maschinellem Lernen. To appear in: Rathgeber, B. & Maier, M. (Eds.), Grenzen Künstlicher Intelligenz. wbg Academic, Darmstadt.
  2. Hornung, R., Nalenz, M., Schneider, L., Bender, A., Bothmann, L., Bischl, B., Augustin, T., & Boulesteix, A-L. (2023) Evaluating machine learning models in non-standard settings: An overview and new findings. arXiv:2310.15108.
  3. Bothmann, L., Dandl, S., & Schomaker, M. (2023) Causal Fair Machine Learning via Rank-Preserving Interventional Distributions. Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023), CEUR Workshop Proceedings,
  4. Dandl, S., Casalicchio, G., Bischl, B., & Bothmann, L. (2023) Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. In: Koutra D, Plant C, Gomez Rodriguez M, Baralis E, Bonchi F (eds) ECML PKDD 2023: Machine Learning and Knowledge Discovery in Databases: Research Track, pp. 479–495. Springer Nature Switzerland, Cham.
  5. Bothmann, L., Wimmer, L., Charrakh, O., Weber, T., Edelhoff, H., Peters, W., Nguyen, H., Benjamin, C., & Menzel, A. (2023). Automated wildlife image classification: An active learning tool for ecological applications. Ecological Informatics, 77(102231).
    link | arXiv
  6. Bothmann, L., Peters, K., & Bischl, B. (2023). What Is Fairness? Philosophical Considerations and Implications For FairML. arXiv:2205.09622.
  7. Bothmann, L. (2022). Künstliche Intelligenz in der Strafverfolgung. In K. Peters (Ed.), Cyberkriminalität. LMU Munich.
  8. Ghada, W., Casellas, E., Herbinger, J., Garcia-Benadí, A., Bothmann, L., Estrella, N., Bech, J., & Menzel, A. (2022). Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar. Remote Sensing, 14(18).
  9. Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., & Bischl, B. (2021). Developing Open Source Educational Resources for Machine Learning and Data Science. arXiv:2107.14330 (accepted at Teaching Machine Learning Workshop at ECML 2022).
  10. Matiu, M., Bothmann, L., Steinbrecher, R., & Menzel, A. (2017). Monitoring succession after a non-cleared windthrow in a Norway spruce mountain forest using webcam, satellite vegetation indices and turbulent CO 2 exchange. Agricultural and Forest Meteorology, 244–245, 72–81.
  11. Bothmann, L., Menzel, A., Menze, B. H., Schunk, C., & Kauermann, G. (2017). Automated processing of webcam images for phenological classification. PLoS ONE, 12(2): e0171918.
  12. Bothmann, L. (2016). Efficient statistical analysis of video and image data [PhD thesis, Ludwig-Maximilians-Universität München].
  13. Bothmann, L., Windmann, M., & Kauermann, G. (2016). Realtime classification of fish in underwater sonar videos. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(4), 565–584.
  14. Kalus, S., Bothmann, L., Yassouridis, C., Czisch, M., Sämann, P., & Fahrmeir, L. (2014). Statistical modeling of time-dependent fMRI activation effects. Human Brain Mapping, 36(2), 731–743.
  15. Bothmann, L. (2012). Statistische Modellierung von EEG-abhängigen Stimuluseffekten in der fMRT-Analyse [Diploma Thesis].