Statistics and Machine Learning are two fields that solve similar problems, although each of them has its own paradigm. Thus, the tools used by these communities often have a different nature. The spirit of this research group is to take the best of both worlds, combining them so as to create improved solutions to relevant real-world problems.
Our work includes both theoretical and applied research. From a methological perspective, we are currently interested in nonparametric inference, recommender systems, high-dimensional statistics, Bayesian methods, predictive inference, and hypothesis tests. Our applications include genetics, cosmology and linguistics.