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

Summer 2024:
- Introduction to Machine Learning (Major Statistics)
- Machine Learning und Deep Learning mit Python
Winter 2023/24:
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

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

References

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.
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.
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? arXiv:2402.01484.
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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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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.
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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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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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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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
Bothmann, L. (2022). Künstliche Intelligenz in der Strafverfolgung. In K. Peters (Ed.), Cyberkriminalität. LMU Munich.
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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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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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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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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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Bothmann, L. (2016). Efficient statistical analysis of video and image data [PhD thesis, Ludwig-Maximilians-Universität München].
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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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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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Bothmann, L. (2012). Statistische Modellierung von EEG-abhängigen Stimuluseffekten in der fMRT-Analyse [Diploma Thesis].
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