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 with a main focus on model-based optimization for hyperparameter optimization in machine learning. I am also a member of the Munich Center for Machine Learning (MCML).

I have a Bachelor's Degree (B.Sc.) in Mathematics from the TU Munich and a Master's Degree (M.Sc.) in Data Science (ESG) from the LMU Munich.

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

• Bayesian Optimization
• Model-based Optimization
• Automatic Machine Learning

You Can Find me on

• GitHub
• LinkedIn

References

1. Moosbauer J, Casalicchio G, Lindauer M, Bischl B (2022) Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.
   link | pdf.
2. Moosbauer J, Herbinger J, Casalicchio G, Lindauer M, Bischl B (2021) Explaining Hyperparameter Optimization via Partial Dependence Plots. Advances in Neural Information Processing Systems (NeurIPS 2021) 34.
   link | pdf.
3. Moosbauer J, Binder M, Schneider L et al. (2021) Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers.
   link | pdf.
4. Pfisterer F, Schneider L, Moosbauer J, Binder M, Bischl B (2021) YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. arXiv:2109.03670 [cs, stat].
   link | pdf.
5. 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.
   link|pdf.