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 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, 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. Therefore, we tackle several major challenges of machine learning with our mathematical statistics point of view, always having in mind that modern datasets are often high-dimensional and/or large-scale, which must be taken into account at the building stage of statistical learning algorithms. For instance, there often are trade-offs between statistical accuracy and complexity which we want to clarify as much as possible.
Finally, a key ingredient in our research program is connecting our theoretical and methodological results with (a great number of) real-world applications. This is the reason why a large part of our work is devoted to industrial and medical data modeling on a set of real-world problems coming from our long-term collaborations with several partners, as well as various opportunistic one-shot collaborations.
