With my research, I aim to simplify the usage of machine learning by researching methods and developing tools that allow the usage of machine learning by domain scientists and also make machine learning more efficient for expert users. My focus is on Automated Machine Learning (AutoML) which encompasses methods for hyperparameter optimization, meta-learning, and model selection. However, machine learning is not solely about predictive performance but also about additional objectives such as model interpretability, deployability, or fairness. For this, I want to move beyond performance-driven machine learning by applying multi-objective AutoML.

Furthermore, I am developing and contributing to multiple open-source projects in the realm of AutoML (see below) and am a co-founder of the Open Machine Learning Foundation that supports the development of OpenML.org.