Archive for Université Paris Dauphine

data scientist position

Posted in R, Statistics, University life with tags , , , , , , , , , , on April 8, 2014 by xi'an

Université Paris-DauphineOur newly created Chaire “Economie et gestion des nouvelles données” in Paris-Dauphine, ENS Ulm, École Polytechnique and ENSAE is recruiting a data scientist starting as early as May 1, the call remaining open till the position is filled. The location is in one of the above labs in Paris, the duration for at least one year, salary is varying, based on the applicant’s profile, and the contacts are Stephane Gaiffas (stephane.gaiffas AT cmap DOT, Robin Ryder (ryder AT ceremade DOT and Gabriel Peyré (peyre AT ceremade DOT Here are more details:

Job description

The chaire “Economie et gestion des nouvelles données” is recruiting a talented young engineer specialized in large scale computing and data processing. The targeted applications include machine learning, imaging sciences and finance. This is a unique opportunity to join a newly created research group between the best Parisian labs in applied mathematics and computer science (ParisDauphine, ENS Ulm, Ecole Polytechnique and ENSAE) working hand in hand with major industrial companies (Havas, BNP Paribas, Warner Bros.). The proposed position consists in helping researchers of the group to develop and implement large scale data processing methods, and applying these methods on real life problems in collaboration with the industrial partners.

A non exhaustive list of methods that are currently investigated by researchers of the group, and that will play a key role in the computational framework developed by the recruited engineer, includes :
● Large scale non smooth optimization methods (proximal schemes, interior points, optimization on manifolds).
● Machine learning problems (kernelized methods, Lasso, collaborative filtering, deep learning, learning for graphs, learning for timedependent systems), with a particular focus on large scale problems and stochastic methods.
● Imaging problems (compressed sensing, superresolution).
● Approximate Bayesian Computation (ABC) methods.
● Particle and Sequential Monte Carlo methods

Candidate profile

The candidate should have a very good background in computer science with various programming environments (e.g. Matlab, Python, C++) and knowledge of high performance computing methods (e.g. GPU, parallelization, cloud computing). He/she should adhere to the open source philosophy and possibly be able to interact with the relevant communities (e.g. scikitlearn initiative). Typical curriculum includes engineering school or Master studies in computer science / applied maths / physics, and possibly a PhD (not required).

Working environment

The recruited engineer will work within one of the labs of the chaire. He will benefit from a very stimulating working environment and all required computing resources. He will work in close interaction with the 4 research labs of the chaire, and will also have regular meetings with the industrial partners. More information about the chaire can be found online at

reading classics (#9,10)

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , , , on January 28, 2014 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012Today was the very last session of our Reading Classics Seminar for the academic year 2013-2014. We listened two presentations, one on the Casella and Strawderman (1984) paper on the estimation of the normal bounded mean. And one on the Hartigan and Wong’s 1979 K-Means Clustering Algorithm paper in JRSS C. The first presentation did not go well as my student had difficulties with the maths behind the paper. (As he did not come to ask me or others for help, it may well be that he put this talk together at the last minute, at a time busy with finals and project deliveries. He also failed to exploit those earlier presentations of the paper.) The innovative part in the talk was the presentation of several R simulations comparing the risk of the minimax Bayes estimator with the one for the MLE. Although the choice of simulating different samples of standard normals for different values of the parameters and even for both estimators made the curves (unnecessarily) all wiggly.

By contrast, the second presentation was very well-designed, with great Beamer slides, interactive features and a software oriented focus. My student Mouna Berrada started from the existing R function kmeans to explain the principles of the algorithm, recycling the interactive presentation of last year as well (with my permission), and creating a dynamic flowchart that was most helpful. So she made the best of this very short paper! Just (predictably) missing the question of the statistical model behind the procedure. During the discussion, I mused why k-medians clustering was not more popular as it offered higher robustness guarantees, albeit further away from a genuine statistical model. And why k-means clustering was not more systematically compared with mixture (EM) estimation.

Here are the slides for the second talk

reading classics (#8)

Posted in Books, Kids, Statistics, University life with tags , , , on January 23, 2014 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012The Reading Classics Seminar today was about (the classic) Donoho and Johnstone’s denoising through wavelets, a 1995 JASA paper entitled Adapting to unknown smoothness via wavelet shrinkage. Two themes (shrinkage and wavelets) I discovered during my PhD years. (Although I did not work on wavelets then, I simply attended seminars where wavelets were the new thing.) My student Anouar Seghir gave a well-prepared presentation, introducing wavelets and Stein’s estimator of the risk. He clearly worked hard on the paper. However, I still feel the talk focussed too much on the maths and not enough on the motivations. For instance I failed to understand why the variance in the white noise was known and where the sparsity indicator came from. (This is anyway a common flaw in those Reading Classics presentations.) The presentation was helped by an on-line demonstration in Matlab, using the resident velvet code. Here are the slides:

from my office (last day too)

Posted in pictures, University life with tags , , , , , on December 17, 2013 by xi'an


[not] reading classics (#7)

Posted in Books, Kids, Statistics, University life with tags , , , , on December 15, 2013 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012This week, I decided not to report on the paper read at the Reading Classics student seminar, as it did not work out well-enough. The paper was the “Regression models and life-table” published in 1972 by David Cox… A classic if any! Indeed, I do not think posting a severe criticism of the presentation or the presentation itself would be of much use to anyone. It is rather sad as (a) the student clearly put some effort in the presentation, including a reproduction of an R execution, and (b) this was an entry on semi-parametrics, Kaplan-Meyer, truncated longitudinal data, and more, that could have benefited the class immensely. Alas, the talk did not take any distance from the paper, did not exploit the following discussion, and exceeded by far the allocated time, without delivering a comprehensible message. It is a complex paper with concise explanations, granted, but there were ways to find easier introductions to its contents in the more recent literature… It is possible that a second student takes over and presents her analysis of the paper next January. Unless she got so scared with this presentation that she will switch to another paper… [Season wishes to Classics Readers!]


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