Latest news

  • May 11

    Paper accepted to ICML 2018

    A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization (ICML 2018). pdf

  • Apr 6

    Paper accepted to Machine Learning Journal

    A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm (Machine Learning). pdf

  • Mar 28

    New paper on privacy in decentralized setting

    Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries (arXiv:1803.09984). pdf

  • Dec 22

    Paper accepted to AISTATS 2018

    Personalized and Private Peer-to-Peer Machine Learning (AISTATS 2018). pdf

  • Dec 21

    New paper on distributed ML

    A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm (arXiv:1712.07495). pdf

  • Nov 20

    Paper accepted to NIPS 2017 workshop

    Personalized and Private Peer-to-Peer Machine Learning (NIPS 2017 workshop on Machine Learning on the Phone and other Consumer Devices).

Short bio

I am a tenured researcher at Inria, where I am part of the Magnet Team (MAchine learninG in information NETworks). I am also affiliated with CRIStAL (UMR CNRS 9189), a research center of the University of Lille.

Prior to joining Inria, I was a postdoctoral researcher at the University of Southern California (working with Fei Sha) and then at Télécom ParisTech (working with Stéphan Clémençon). I obtained my Ph.D. from the University of Saint-Etienne in 2012 under the supervision of Marc Sebban and Amaury Habrard.

You can find more information in my CV or on my LinkedIn profile.

Research interests

My main line of research is in statistical machine learning. I am particularly interested in designing large-scale learning algorithms which allow a good trade-off between computational complexity (or other "resources", such as privacy or communication) and statistical performance.

My current research focus includes:

  • large-scale machine learning
  • distributed / decentralized algorithms
  • privacy-preserving machine learning
  • graph-based methods
  • optimization for machine learning
  • similarity and distance metric learning
  • statistical learning theory
  • applications to NLP, speech recognition and computer vision