Latest news

  • Sep 22

    Co-organization of a workshop at NIPS 2018

    Workshop on Privacy Preserving Machine Learning. Submissions are open until Oct 8! Check out the CFP.

  • Aug 10

    Paper accepted to EMNLP 2018

    A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images (EMNLP 2018). pdf

  • Jul 23

    New paper on similarity learning

    Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds (arXiv:1803.09984). pdf

  • 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

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