Ludwig Bothmann

About

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.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Ludwig.Bothmann [at] stat.uni-muenchen.de

You Can Find me on

Teaching

Prior teaching:

Talks

References

  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).
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  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.
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  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.
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  6. Bothmann, L., Peters, K., & Bischl, B. (2024). What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds. arXiv:2205.09622.
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  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.
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  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, https://ceur-ws.org/Vol-3523/.
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  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.
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  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.
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  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).
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  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 .
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  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.
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  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.
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  17. Bothmann, L. (2016). Efficient statistical analysis of video and image data [PhD thesis, Ludwig-Maximilians-Universität München].
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  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.
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  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.
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  20. Bothmann, L. (2012). Statistische Modellierung von EEG-abhängigen Stimuluseffekten in der fMRT-Analyse [Diploma Thesis].
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