mathematical statistics and learning
mathematical statistics and learning
Data science – a vast field including statistics, machine learning, signal processing, data visualization and databases – has become front-page news, with a potentially major impact on society beyond the important role it has had in science for many decades. Within data science, the statistical community has long experience in how to infer knowledge from data, with strong mathematical foundations. The more recent field of machine learning has also involved major achievements, by combining statistics with optimization, and using a fresh point of view that came from applications where prediction was more important than building models.

The positioning of the CELESTE project-team is at the interplay between statistics and learning. We are statisticians, members of a mathematics laboratory, with a strong mathematical background, and are interested by interactions between theory, algorithms and applications. Indeed, applications lead to most interesting theoretical problems, while theory can play a key role in (i) understanding how and why successful statistical/learning algorithms work — hence improving them — and (ii) building new algorithms upon mathematical statistics foundations.
Centre(s) inria
In partnership with
CNRS,Université Paris-Saclay


Team leader

Laurence Fontana

Team assistant