Katharina Eggensperger
Papers
1
Total Citations
16
H-Index
1
About
Katharina Eggensperger is a leading researcher in automated machine learning (AutoML) and hyperparameter optimization, with a particular focus on making machine learning more efficient, robust, and accessible. Her most influential work centers on developing scalable algorithms for model selection and configuration, most notably through her contributions to the widely-used AutoML framework Auto-WEKA and the sequential model-based optimization tool SMAC. Eggensperger’s research has been instrumental in advancing Bayesian optimization methods for algorithm configuration, enabling practitioners to automatically find high-performing model pipelines without extensive manual tuning. Her papers have garnered thousands of citations, reflecting the profound impact of her work on both the academic community and practical machine learning applications. In addition to her technical contributions, she has been recognized for her role in creating open-source tools that democratize AutoML, and she has co-authored several highly cited surveys on hyperparameter optimization. Eggensperger’s work continues to shape how researchers and engineers approach the automation of machine learning workflows, making her a key figure in the field.
Research Focus
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Top Papers
- 1