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

• 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

• 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. Felderer, B., Repke, L., Weber, W., Schweisthal, J., & Bothmann, L. (2024). Predicting the validity and reliability of survey questions. osf preprints.
   link
2. 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.
3. 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).
   link
4. 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.
   link
5. 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.
   link
6. 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
7. Bothmann, L., Peters, K., & Bischl, B. (2024). What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds. arXiv:2205.09622.
   link
8. 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.
9. 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.
   link
10. 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/.
    link
11. 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.
    link
12. 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
13. Bothmann, L. (2022). Künstliche Intelligenz in der Strafverfolgung. In K. Peters (Ed.), Cyberkriminalität. LMU Munich.
    link
14. 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).
    link
15. 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
16. 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.
    link
17. 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.
    link
18. Bothmann, L. (2016). Efficient statistical analysis of video and image data [PhD thesis, Ludwig-Maximilians-Universität München].
    link
19. 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.
    link
20. 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.
    link
21. Bothmann, L. (2012). Statistische Modellierung von EEG-abhängigen Stimuluseffekten in der fMRT-Analyse [Diploma Thesis].
    link
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