Objective: Machine learning (ML) is the key technology of our time. However, so far, its success crucially relies on human machine learning experts to perform manual tasks, thus limiting its potential impact.
To overcome this issue, the Machine Learning Lab focuses on the progressive automation of machine learning (AutoML) in order to democratize access to ML by making state-of-the-art ML solutions accessible for everyone in world-leading open source systems. Framed differently, and from a technical point of view, the lab develops AI that builds AI.

Research Areas: Meta-Learning, Neural Architecture Search, Efficient Hyperparameter Optimization, Deep Learning for Tabular Data, Multi-Objective Optimization, Proper Benchmarking, AutoRL, AutoML for Medical Sector