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. Ronval, B., Nijssen, S., & Bothmann, L. (2024). Can generative AI-based data balancing mitigate unfairness issues in Machine Learning? Accepted at EWAF’24: European Workshop on Algorithmic Fairness.
  2. Dandl, S., Becker, M., Bischl, B., Casalicchio, G., & Bothmann, L. (2024). mlr3summary: Concise and interpretable summaries for machine learning models. Accepted at 2nd World Conference on eXplainable Artificial Intelligence 2024 (Demo Track).
  3. Ewald, F., Bothmann, L., Wright, M., Bischl, B., Casalicchio, G., & König, G. (2024). A Guide to Feature Importance Methods for Scientific Inference. Accepted at 2nd World Conference on eXplainable Artificial Intelligence 2024.
  4. Bothmann, L., & Peters, K. (2024). Fairness als Qualitätskriterium im Maschinellen Lernen – Rekonstruktion des philosophischen Konzepts und Implikationen für die Nutzung außergesetzlicher Merkmale bei qualifizierten Mietspiegeln. To appear in: AStA Wirtschafts- und Sozialstatistisches Archiv.
  5. Sommer, E., Wimmer, L., Papamarkou, T., Bothmann, L., Bischl, B., & Rügamer, D. (2024). Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? Accepted at International Conference on Machine Learning (ICML) 2024.
  6. Bothmann, L., Peters, K., & Bischl, B. (2024). What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds. arXiv:2205.09622.
  7. 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.
  8. 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.
  9. 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,
  10. 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.
  11. 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
  12. Bothmann, L. (2022). Künstliche Intelligenz in der Strafverfolgung. In K. Peters (Ed.), Cyberkriminalität. LMU Munich.
  13. 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).
  14. Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., & Bischl, B. (2022). Developing Open Source Educational Resources for Machine Learning and Data Science. Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 207:1-6 .
    link | pdf
  15. 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.
  16. 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.
  17. Bothmann, L. (2016). Efficient statistical analysis of video and image data [PhD thesis, Ludwig-Maximilians-Universität München].
  18. 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.
  19. 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.
  20. Bothmann, L. (2012). Statistische Modellierung von EEG-abhängigen Stimuluseffekten in der fMRT-Analyse [Diploma Thesis].
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