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 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:
Thursday June 30th, 2022, 11h00-12h00, (Paris time), room Amphi Jaures (29 Rue d'Ulm).
Chris Wiggins (New York Times, Columbia)
Abstract: The Data Science group at The New York Times develops and deploys machine learning solutions to newsroom and business problems. Re-framing real-world questions as machine learning tasks requires not only adapting and extending models and algorithms to new or special cases but also sufficient breadth to know the right method for the right challenge. I'll first outline how
- supervised, and
- reinforcement learning methods
are increasingly used in human applications for
- prediction, and
respectively. I'll then focus on the 'prescriptive' cases, showing how methods from the reinforcement learning and causal inference literatures can be of direct impact in
- business, and
- decision-making more generally.