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

June 11th, 2019, 12h00-13h00, room Salle Jean Jaurès, 29 rue d’Ulm (sous-sol).
Jean-Remi King (ENS)
Title: From brains to algorithms: parsing neuroimaging data to infer the computational architecture of human cognition.
Abstract: While machine learning is an autonomous research field, a number of historical (e.g. artificial neural networks) as well as more recent computational strategies (e.g. attentional gating) have been influenced by cognitive and neuroscientific findings. To what extent can cognitive neuroscience continue to guide and intersect with the development of machine learning? To highlight potential directions to this major issue, I will present three studies that investigate the computational organization of brain processing. For each of them, I will show that we can use non-invasive neuroimaging techniques with high temporal precision to parse the computational stages of visual processing in the healthy human brain. Our results show that the raw visual input that bombards our retina is progressively transformed into meaningful representations by a hierarchical algorithm, distributed both over time and space. Finally, I will briefly show how these methods can now be applied to understand language processing in humans, and thus help us tackle the modern challenges of machine learning.