Data Science Colloquium

of the ENS

Welcome to the Data Science Colloquium of the ENS.

This colloquium is organized around data sciences in a broad sense with the goal of bringing together researchers with diverse backgrounds (including for instance mathematics, computer science, physics, chemistry and neuroscience) but a common interest in dealing with large scale or high dimensional data.

The seminar takes place, unless exceptionally noted, on the first Tuesday of each month at 12h00 at the Physics Department of ENS, 24 rue Lhomond, in room CONF IV (2nd floor).

The colloquium is followed by an open buffet around which participants can meet and discuss collaborations.

These seminars are made possible by the support of the CFM-ENS Chair “Modèles et Sciences des Données.

You can check the list of the next seminars below and the list of past seminars.

Videos of some of the past seminars are available online.


The colloquium is organized by:

Next seminars

Nov. 26th, 2019, 12h15-13h15, room Jean Jaures (29 Rue d’Ulm).
Yue M. Lu (John A. Paulson School of Engineering and Applied Sciences, Harvard University)
Title: Exploiting the Blessings of Dimensionality in Big Data
Abstract: The massive datasets being compiled by our society present new challenges and opportunities to the field of signal and information processing. The increasing dimensionality of modern datasets offers many benefits. In particular, the very high-dimensional settings allow one to develop and use powerful asymptotic methods in probability theory and statistical physics to obtain precise characterizations that would otherwise be intractable in moderate dimensions. In this talk, I will present recent work where such blessings of dimensionality are exploited. In particular, I will show (1) the exact characterization of a widely-used spectral method for nonconvex statistical estimation; (2) the fundamental limits of solving the phase retrieval problem via linear programming; and (3) how to use scaling and mean-field limits to analyze nonconvex optimization algorithms for high-dimensional inference and learning. In these problems, asymptotic methods not only clarify some of the fascinating phenomena that emerge with high-dimensional data, they also lead to optimal designs that significantly outperform heuristic choices commonly used in practice.