About

I am a postdoc in the working group Biometry in Molecular Medicine (lab lead: Prof. Anne-Laure Boulesteix) and a member of the Munich Center for Machine Learning (MCML), where I am the Reproducibility and Open Science Transfer Coordinator. I also lead the Empirical Machine Learning research focus group jointly established with the Chair of Statistical Learning and Data Science (Department of Statistics) and I am a member of the LMU Open Science Center, the Open Science Initiative in Statistics (OSIS) and the Open Science Initiative in Medicine (OSIM).

I studied Mathematics, Sports Science, and Education (1st state examination) at the University of Augsburg from 2008 to 2014 and Statistics (M.Sc.) at the Ludwig-Maximilians-University Munich (LMU) from 2016 to 2018. In 2022, I received a Ph.D. in Statistics from the LMU with a thesis on reliable machine learning (supervisor: PD Dr. Fabian Scheipl).

From 2014 to 2016, I completed the in-service teacher training (Referendariat) for Bavarian academic high schools (2nd state examination in mathematics and sport).

Research Interests

• Empirical Machine Learning (see our Position Paper)
• Manifold & Representation Learning
• Metascience, Epistemological foundations of Machine Learning and Statistics
• Data Literacy

Publications

• Wünsch, M., Sauer, C., Herrmann, M., Hinske, L. C., & Boulesteix, A.-L. (2025). To Tweak or Not to Tweak. How Exploiting Flexibilities in Gene Set Analysis Leads to Overoptimism. Biometrical Journal, 67(1), e70016. https://doi.org/10.1002/bimj.70016
• Wünsch, M., Herrmann, M., Noltenius, E., Mohr, M., Morris, T. P., & Boulesteix, A.-L. (2024). On the handling of method failure in comparison studies. arXiv preprint arXiv:2408.11594. https://doi.org/10.48550/arXiv.2408.11594 (under review)
• Herrmann, M., Lange, F. J. D., Eggensperger, K., Casalicchio, G., Wever, M., Feurer, M., Rügamer, D., Hüllermeier, E., Boulesteix, A.-L., & Bischl, B. (2024). Position: Why We Must Rethink Empirical Research in Machine Learning. Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Proceedings of Machine Learning Research, 235, 18228–18247. https://proceedings.mlr.press/v235/herrmann24b.html
• Gauss, J., Scheipl, F., & Herrmann, M. (2024). DCSI – An improved measure of cluster separability based on separation and connectedness. arXiv preprint arXiv:2310.12806. https://doi.org/10.48550/arXiv.2310.12806 (under review)
• Herrmann, M., Kazempour, D., Scheipl, F., & Kröger, P. (2024). Enhancing cluster analysis via topological manifold learning. Data Mining and Knowledge Discovery, 38, 840–887. https://doi.org/10.1007/s10618-023-00980-2
• Herrmann, M., Pfisterer, F., & Scheipl, F. (2023). A geometric framework for outlier detection in high-dimensional data. WIREs Data Mining and Knowledge Discovery, 13(3), e1491. https://doi.org/10.1002/widm.1491
• Herrmann, M. (2022). Towards more reliable machine learning: conceptual insights and practical approaches for unsupervised manifold learning and supervised benchmark studies. PhD thesis, Ludwig-Maximilians-Universität München, Munich, Germany. https://doi.org/10.5282/edoc.30789
• Nießl, C., Herrmann, M., Wiedemann, C., Casalicchio, G., & Boulesteix, A.-L. (2022). Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(2), e1441. https://doi.org/10.1002/widm.1441
• Herrmann, M., & Scheipl, F. (2021). A geometric perspective on functional outlier detection. Stats, 4(4), 971–1011. https://doi.org/10.3390/stats4040057
• Herrmann, M., Probst, P., Hornung, R., Jurinovic, V., & Boulesteix, A.-L. (2021). Large-scale benchmark study of survival prediction methods using multi-omics data. Briefings in Bioinformatics, 22(3), bbaa167. https://doi.org/10.1093/bib/bbaa167
• Herrmann, M., & Scheipl, F. (2020). Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. arXiv preprint arXiv:2012.11987. https://doi.org/10.48550/arXiv.2012.11987