Katharina Eggensperger is an Early Career Research Group Leader AutoML for Science in the Cluster of Excellence Machine Learning for Science at the University of Tübingen.
Her research focuses on methods for automated machine learning (AutoML), hyperparameter optimization (HPO), and efficient benchmarking. Motivated by the goal of further democratizing the application of machine learning for scientific researchers and practitioners, she researches how to improve and extend AutoML systems to leverage the full potential of ML for new applications.
Previously, she was part of the ML Lab at the University of Freiburg, where she completed her Ph.D. under the supervision of Frank Hutter and Marius Lindauer (2022). Katharina also co-developed open-source tools for HPO methods and AutoML systems and has been a member of the team winning three AutoML competitions (2016, 2018, 2020). She is part of the Automl.org group, co-organized the AutoML workshop series at ICML in 2019, 2020 and 2021 and serves as a social chair for the AutoML Conference in 2022 and 2023.