Section: Overall Objectives

Mathematical statistics and learning

Data science – a vast field that includes statistics, machine learning, signal processing, data visualization, and databases – has become front-page news due to its ever-increasing impact on society, over and above the important role it already played in science over the last few decades. Within data science, the statistical community has long-term experience in how to infer knowledge from data, based on solid mathematical foundations. The more recent field of machine learning has also made important progress by combining statistics and optimization, with a fresh point of view that originates in applications where prediction is more important than building models.

The Celeste project-team is positioned at the interface between statistics and machine learning. We are statisticians in a mathematics department, with strong mathematical backgrounds behind us, interested in interactions between theory, algorithms and applications. Indeed, applications are the source of many of our interesting theoretical problems, while the theory we develop plays 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-based foundations

In the theoretical and methodological domains, Celeste aims to analyze statistical learning algorithms – especially those which are most used in practice – with our mathematical statistics point of view, and develop new learning algorithms based upon our mathematical statistics skills.

A key ingredient in our research program is connecting our theoretical and methodological results with (a great number of) real-world applications. Indeed, Celeste members work in many domains, including – but not limited to – neglected tropical diseases, pharmacovigilance, high-dimensional transcriptomic analysis, and energy and the environment.