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:
May 15th, 2018, 12h00-13h00, room Salle Jean Jaurès, 29 rue d’Ulm (sous-sol).
Balázs Kégl (Université Paris Saclay)
Title: Machine learning in scientific workflows
Abstract: I will describe our contributions to scientific ML workflow building and optimization, which we have carried out within the Paris-Saclay Center for Data Science. I will start by mapping out the different use cases of machine learning in sciences (data collection, inference, simulation, hypothesis generation). Then I will detail some of the particular challenges of ML/science collaborations and the solutions we built to solve these challenges. I will briefly describe the open code submission RAMP tool that we built for collaborative prototyping, detail some of the workflows (e.g., the Higgs boson discovery pipeline, El Nino forecasting, detecting Mars craters on satellite images), and present results on rapidly optimizing machine learning solutions.
Bio: Balázs Kégl received the Ph.D. degree in computer science from Concordia University, Montreal, in 1999. From January to December 2000 he was a Postdoctoral Fellow at the Department of Mathematics and Statistics at Queen's University, Kingston, Canada, receiving NSERC Postdoctoral Fellowship. He was in the Department of Computer Science and Operations Research at the University of Montreal, as an Assistant Professor from 2001 to 2006. Since 2006 he has been a research scientist in the Linear Accelerator Laboratory of the CNRS. He has published more than hundred papers on unsupervised and supervised learning (principal curves, intrinsic dimensionality estimation, boosting), large-scale Bayesian inference and optimization, and on various applications ranging from music and image processing to systems biology and experimental physics. At his current position he has been the head of the AppStat team working on machine learning and statistical inference problems motivated by applications in high-energy particle and astroparticle physics. Since 2014, he has been the head of the Center for Data Science of the University of Paris Saclay. In 2016 he is co-created the RAMP (www.ramp.studio).