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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. Our main methodological and theoretical research axes are: 
- estimator selection
- the relationship between statistical accuracy and computational complexity
- algorithmic fairness
- Statistical inference: (multiple) tests and confidence regions (including post-selection)

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.

In industry, CELESTE has several ongoing collaborations with the R&D department of EDF, as well as with a number of other companies (via CIFRE PhDs in particular).