I am a third year Ph.D. student in the Sierra Team, which is part of the DI/ENS (Computer Science Department of École Normale Supérieure). I graduated from Ecole Normale Supérieure de Paris (Ulm) in 2014 and got a Masters Degree in Mathematics, Probability and Statistics (at Université Paris-Sud, Orsay).

I am supervised by Francis Bach. My main research interests are statistics, optimization, stochastic approximation, high-dimensional learning, non-parametric statistics, scalable kernel methods.

From March to August 2016, I was a visiting scholar researcher at University of California Berkeley, under the supervision of Martin Wainwright.

My CV.


Non-parametric Stochastic Approximation with Large Step sizes
Aymeric Dieuleveut and Francis Bach.
Published in the Annals of Statistics.
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Aymeric Dieuleveut, Nicolas Flammarion and Francis Bach
arXiv:1602.05419 [math.ST].


December 2016, Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression, with Nicolas Flammarion, Nips, OPT16 . [abstract]
December 2015, Adaptativity of stochastic gradient descent, Nips Workshop. [abstract][slides]


2016-2017 : Teaching assistant, Statistics , Master 1 (31NU02MS), University Paris Diderot.
2016-2017 : Teaching assistant, Fundamental Statistics , Master 1 (ULMT42), University Paris Diderot.
2015-2016 : Teaching assistant, Calculus , (MM1), University Paris Diderot.
2014-2015 : Teaching assistant, Linear Algebra , (MM1), University Paris Diderot.
2010-2014 : Oral interrogations in ``Classes préparatoires'' (PC, MP*).


Reviewer for JMLR, AOS, COLT, IEEE, ACM, ICML.


February 2017, Scalable methods for Statistics, a short presentation, Cambridge, UK . [slides]
March 2016, Non parametric stochastic approxiation, UC Berkeley.
October 2015, Tradeoffs of learning in Hilbert spaces, ENSAI Rennes. [slides]
June 2015, Non parametric Stochastic Approximation, Machine Learning Summer School, Tubingen.