Ludwig Bothmann

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

• GitHub
• Google Scholar
• ORCiD
• ResearchGate
• Linkedin

Teaching

• Summer 2025:
  • Supervised Learning
  • Introduction to Machine Learning (Major Statistics)
  • Seminar “Probabilistic ML” (BA)
  • Seminar “Advances in Variational Autoencoding” (MA)
• Winter 2024/25:
  • Supervised Learning
  • Seminar “Trustworthy ML” (BA)
  • Seminar “Causal Discovery” (MA)
  • Machine Learning und Deep Learning mit Python
• Summer 2024:
  • Introduction to Machine Learning (Major Statistics)
  • Machine Learning und Deep Learning mit Python
• Winter 2023/24:
  • Introduction to Machine Learning (Minor Statistics / AIM)
  • Seminar “FairML” (BA)
  • Seminar “ML meets Causality” (MA)
• Summer 2023:
  • Introduction to Machine Learning (Major Statistics)
  • Seminar “Causal Inference” (BA)
• Winter 2022/23:
  • Introduction to Machine Learning
  • Supervised Learning
• Summer 2022:
  • Introduction to Machine Learning
  • Machine Learning and Deep Learning with Python
• Winter 2021/22:
  • Introduction to Machine Learning
  • Supervised Learning
• Summer 2021:
  • Machine Learning and Deep Learning with Python
• Winter 2020/21:
  • Introduction to Machine Learning
  • Automated Machine Learning

Prior teaching:

• Data Science in der Praxis: Machine Learning und Deep Learning mit Python (BA and MA Statistics):
  • Lecture and Exercise: WiSe 2019/20
• R-Kurs für Nebenfachstudierende (Statistics as Minor):
  • Lecture and Exercise: SoSe 2015, WiSe 2015/16, SoSe 2016, SoSe 2017, SoSe 2019
• Fortgeschrittener R-Kurs für Nebenfachstudierende (Statistics as Minor):
  • Lecture and Exercise: WiSe 2017/18
• Statistik III für Nebenfachstudierende (Statistics as Minor):
  • Lecture: WiSe 2014/15, WiSe 2015/16
  • Exercise: WiSe 2013/14, WiSe 2014/15, WiSe 2015/16
• Schätzen und Testen I (Master Statistics)
  • Exercise: WiSe 2012/13, WiSe 2013/14
  • Tutorial: WiSe 2010/11, WiSe 2011/12
• Schätzen und Testen II (Master Statistics)
  • Exercise: SoSe 2012, SoSe 2013, SoSe 2014, SoSe 2015, SoSe 2018
  • Tutorial: SoSe 2011, SoSe 2012, SoSe 2013, SoSe 2014
• Stochastische Prozesse (Master Statistics)
  • Exercise: SoSe 2013
• Versuchsplanung (Bachelor Statistics)
  • Exercise: SoSe 2015

Talks

• 26.03.2025, “Privilege Scores”, accepted talk at DAGStat 2025, Berlin
• 29.07.2024, “Causal Fair Machine Learning”, accepted talk at Statistical Computing 2024, Schloß Reisensburg
• 24.07.2024, “Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?”, accepted poster at ICML 2024, Vienna, Austria
• 02.07.2024, “What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds”, accepted poster at EWAF - European Workshop on Algorithmic Fairness 2024, Mainz
• 01.07.2024, “Can generative AI-based data balancing mitigate unfairness issues in Machine Learning?”, accepted poster at EWAF - European Workshop on Algorithmic Fairness 2024, Mainz
• 17.04.2024, “Causal Fair Machine Learning via Rank-Preserving Interventional Distributions”, accepted poster at EuroCIM - European Causal Inference Meeting 2024, Copenhagen, Denmark
• 01.10.2023, “Causal Fair Machine Learning via Rank-Preserving Interventional Distributions”, accepted talk at AEQUITAS Workshop on Fairness and Bias in AI at ECAI 2023, Krakow, Poland
• 07.09.2023, “Automated wildlife image classification: An active learning tool for ecological applications”, accepted talk at Camera traps, AI, and Ecology, Jena
• 24.07.2023, “Causal Fair Machine Learning via Warping Real World Data to a Fictitious, Normatively Desired World”, accepted talk at Causality for Ethics and Society, Munich
• 18.07.2023, “An active deep learning method for high out-of-sample predictive performance in image classification”, accepted poster at International Workshop on Statistical Modelling, Dortmund
• 30.03.2023, “What is fairness? Implications for fairML”, invited talk at Grenzen Künstlicher Intelligenz, Munich
• 07.09.2022, “What is fairness? Implications for fairML”, invited talk at Quality Aspects of Machine Learning - Official Statistics between Specific Quality Requirements and Methodological Innovation, Munich
• 08.02.2022, “Künstliche Intelligenz in der Strafverfolgung”, invited talk at Lecture series Cybercrime, Faculty of Law, LMU Munich
• 22.11.2021, “Kollaborative Entwicklung von Open Source Educational Resources für Machine Learning und Data Science”, invited talk at Workshop “Biometrie online lehren”, Working group (IBS-DR), Munich
• 18.02.2020, “Bilddaten: Mit Deep Learning echten Geschäftsnutzen generieren”, invited talk at German Data Science Days, LMU Munich
• 15.03.2016, “Realtime Classification of Fish in Underwater Sonar Videos”, accepted talk at DAGStat, Göttingen
• 19.03.2013, “Real Time Classification of Fish in Sonar Images (DIDSON)”, accepted talk at ECAS 2013, Castro Urdiales, Spain

Media Coverage

• Our projekt “AI4Wildlife” has been featured in a documentary by Bayerischer Rundfunk (Bavarian Television) (broadcasted on 24/09/07, still available online).
• The computer magazine c’t interviewed me for an article on “AI and Causality” (volume 22/2024).

References

1. Wimmer, L., Bischl, B. & Bothmann, L. (2025). Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning. Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions at ICLR.
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2. Surner, M., Khelil, A., & Bothmann, L. (2025). Invariance Pair-Guided Learning: Enhancing Robustness in Neural Networks. arXiv:2502.18975.
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3. Bothmann, L., Boustani, P.A., Alvarez, J.M., Casalicchio, G., Bischl, B. & Dandl, S. (2025). Privilege Scores. arXiv:2502.01211.
   link
4. Leininger, C., Rittel, S., & Bothmann, L. (2025). Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective. arXiv:2501.14710.
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5. Bothmann, L., & Peters, K. (2025). Fairness von KI – ein Brückenschlag zwischen Philosophie und Maschinellem Lernen. In: Rathgeber, B. & Maier, M. (Eds.), Grenzen Künstlicher Intelligenz. Kohlhammer, Stuttgart.
6. Felderer, B., Repke, L., Weber, W., Schweisthal, J., & Bothmann, L. (2024). Predicting the validity and reliability of survey questions. osf preprints.
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7. Ronval, B., Nijssen, S., & Bothmann, L. (2024). Can generative AI-based data balancing mitigate unfairness issues in Machine Learning? EWAF’24: European Workshop on Algorithmic Fairness.
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8. 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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9. Ewald, F., Bothmann, L., Wright, M., Bischl, B., Casalicchio, G., & König, G. (2024). A Guide to Feature Importance Methods for Scientific Inference. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2154. Springer.
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10. 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. AStA Wirtschafts- und Sozialstatistisches Archiv.
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11. 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.
    link
12. Bothmann, L., Peters, K., & Bischl, B. (2024). What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds. arXiv:2205.09622.
    link
13. 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. Accepted at Statistical Science.
    link
14. 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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15. 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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16. 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
17. Bothmann, L. (2022). Künstliche Intelligenz in der Strafverfolgung. In K. Peters (Ed.), Cyberkriminalität. LMU Munich.
    link
18. 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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19. 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
20. 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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21. 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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22. Bothmann, L. (2016). Efficient statistical analysis of video and image data [PhD thesis, Ludwig-Maximilians-Universität München].
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23. 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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24. 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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25. Bothmann, L. (2012). Statistische Modellierung von EEG-abhängigen Stimuluseffekten in der fMRT-Analyse [Diploma Thesis].
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