DistributedML 2023


Aurélien Bellet

Aurélien is a senior researcher (directeur de recherche) at Inria, France. He is currently part of the PreMeDICaL Team (Precision Medicine by Data Integration and Causal Learning) based in sunny Montpellier. He is also an associate member of the Magnet Team. Prior to joining Inria, he was a postdoctoral researcher at the University of Southern California (working with Fei Sha) and then at Télécom Paris (working with Stéphan Clémençon). He obtained his Ph.D. from the University of Saint-Etienne in 2012 under the supervision of Marc Sebban and Amaury Habrard.

His main line of research is in the theory and algorithms of machine learning. He is particularly interested in designing large-scale learning algorithms that provably achieve good trade-offs between statistical performance and other key criteria such as computational complexity, communication, privacy and fairness.

His current research focus includes:

  • distributed / federated / decentralized learning algorithms
  • privacy-preserving machine learning
  • representation learning and distance metric learning
  • optimization for machine learning
  • graph-based methods
  • statistical learning theory
  • fairness in machine learning
  • applications to NLP, speech recognition and health
More Information:
Talks at this conference: