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This process can be generalized to jointly select algorithms, preprocessing methods, and their hyperparameters as follows: the choices of classifier / regressor and preprocessing methods are top-level, categorical hyperparameters, and based on their settings the hyperparameters of the selected methods become active. In the auto track competing systems were run autonomously for 100 minutes to process 5 previously unseen datasets per phase. His research aims to make hyperparameter optimization for expensive machine learning models like deep neural networks feasible. His group on Machine Learning for Automated Algorithm Design works on machine learning and optimization, with a recent focus on Bayesian optimization, automated machine learning, and deep learning.