Dr. Julia Moosbauer

About

After previously being a student assistant at the working group Computational Statistics at the Ludwig-Maximilians-University Munich, I joined the group as a PhD student in November 2018. My research primarily revolves around Automated Machine Learning (AutoML) and model-based optimization strategies for hyperparameter tuning in machine learning. I am particularly interested in the interface of AutoML and explainability, aiming to enhance the interpretability of automated machine learning processes. Additionally, I am affiliated with the Munich Center for Machine Learning (MCML), where I collaborate with fellow researchers to advance the field of machine learning.

I hold a Bachelor of Science (B.Sc.) degree in Mathematics from the Technical University of Munich and a Master of Science (M.Sc.) degree in Data Science from Ludwig Maximilian University of Munich. In 2023, I completed my Ph.D., focusing on "Towards Explainable Automated Machine Learning."

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 148, 1st floor

Phone: +49 89 2180 3521

julia.moosbauer [at] stat.uni-muenchen.de

Research Interests

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References

  1. Münch P, Mreches R, To X-Y, Gündüz HA, Moosbauer J, Klawitter S, Deng Z-L, Robertson G, Rezaei M, Asgari E, Franzosa E, Huttenhower C, Bischl B, McHardy A, Binder M (2023) A platform for deep learning on (meta)genomic sequences (preprint).
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  2. Karl F, Pielok T, Moosbauer J, Pfisterer F, Coors S, Binder M, Schneider L, Thomas J, Richter J, Lang M, Garrido-Merchán EC, Branke J, Bischl B (2022) Multi-Objective Hyperparameter Optimization–An Overview.
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  3. Moosbauer J, Casalicchio G, Lindauer M, Bischl B (2022) Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.
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  4. Moosbauer J, Binder M, Schneider L, Pfisterer F, Becker M, Lang M, Kotthoff L, Bischl B (2022) Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Trans. Evol. Comput. 26, 1336–1350. https://doi.org/10.1109/TEVC.2022.3211336.
  5. Pfisterer F, Schneider L, Moosbauer J, Binder M, Bischl B (2022) YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization.
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  6. Moosbauer J, Herbinger J, Casalicchio G, Lindauer M, Bischl B (2021) Explaining Hyperparameter Optimization via Partial Dependence Plots (MA Ranzato, A Beygelzimer, YN Dauphin, P Liang, and JW Vaughan, Eds.). Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, 2280–2291. https://proceedings.neurips.cc/paper/2021/hash/12ced2db6f0193dda91ba86224ea1cd8-Abstract.html.
  7. Binder M, Moosbauer J, Thomas J, Bischl B (2020) Multi-Objective Hyperparameter Tuning and Feature Selection Using Filter Ensembles Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 471–479. Association for Computing Machinery, New York, NY, USA.
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