Aurélien Bellet

Ongoing

• Comprise (​Cost-effective, Multilingual, Privacy-driven voice-enabled services), 2018-.
  Funded by the H2020-ICT-2018-2020. Role: Project Member.
• DEEP-PRIVACY (​Distributed, Personalized, Privacy-Preserving Learning for Speech Processing), 2018-.
  Funded by the French National Research Agency (ANR). Role: Project Member.
• PAD-ML (Privacy-Aware Distributed Machine Learning), 2018-.
  INRIA Associate Team with the Privacy-preserving data analysis group at Alan Turing Institute. Role: Co-Principal Investigator.
• PAMELA (Personalized and decentrAlized MachinE Learning under constrAints), 2016-.
  Funded by the French National Research Agency (ANR). Role: Project Member.
• GRASP (GRAph-based machine learning for linguistic Structure Prediction), 2016-.
  Funded by the French National Research Agency (ANR). Role: Project Member.
• LEGO (LEarning GOod representations for natural language processing), 2016-.
  INRIA Associate Team with Fei Sha's group at USC. Role: Co-Principal Investigator.

Past

• ERC Proof of Concept SOM (Statistical modeling for Optimization Mobility), 2016-2018.
  Funded by ERC. Role: Project Member.
• Machine Learning for Big Data, 2014-2015.
  Funded by Télécom Paris and several companies. Role: Project Member.
• BABEL, 2013-2014.
  Funded by IARPA. Role: Project Member.
• LAMPADA (Learning Algorithms, Models and sPArse representations for structured DAta), 2009-2013.
  Funded by the French National Research Agency (ANR). Role: Project Member.
• PASCAL2 Network of Excellence, 2009-2013.
  Funded by the European Commission. Role: Project Member.

Organizer

• French Workshop on Privacy Protection (APVP 2019)
  10th edition, Baie de Somme (France), July 2019
  Co-organized with Pierre Bourhis, Walter Rudametkin and Marc Tommasi.
• Privacy Preserving Machine Learning
  Workshop at NeurIPS 2018, Montreal (Canada), December 2018
  Co-organized with Adrià Gascón, Niki Kilbertus, Olya Ohrimenko, Mariana Raykova and Adrian Weller
• Workshop on Privacy-Aware Distributed Machine Learning
  Workshop at INRIA, Lille (France), October 2018
  Co-organized with Adrià Gascón
• Workshop on Decentralized Machine Learning, Optimization and Privacy
  Workshop at INRIA, Lille (France), September 2017
  Co-organized with Morten Dahl, Sébastien Gambs, George Giakkoupis and Joseph Salmon
• Private Multi-Party Machine Learning
  Workshop at NIPS 2016, Barcelona (Spain), December 2016
  Co-organized with Borja Balle, David Evans and Adrià Gascón
• Similarity and Distance Metric Learning with Applications to Computer Vision
  Tutorial at ECML/PKDD 2015, Porto (Portugal), September 2015
  Co-organized with Matthieu Cord

Area Chair

• ICML 2019

PC Member

• ICML 2015 2016 2017 2018
• NIPS (now NeurIPS) 2016 2017 2018 2019
• AISTATS 2017 2018 2019
• ECML/PKDD 2011 2013 2014
• IJCAI 2013 2016
• MOD 2015 2016 2017
• CAp 2018 2019
• PPML@CSS 2019

We are always looking for good students, postdocs and engineers! Check out this page for information on some open positions in the team.

Postdocs

• Melissa Ailem (2017-, Inria@SiliconValley fellowship, co-supervised with P. Denis, F. Sha and M. Tommasi)

PhD students

• Mahsa Asadi (2018-, co-supervised with M. Tommasi)
• Brij Mohan Lal Srivastava (2018-, co-supervised with M. Tommasi and E. Vincent)
• Mariana Vargas Vieyra (2018-, co-supervised with P. Denis)
• Robin Vogel (2017-, CIFRE thesis with IDEMIA, co-supervised with S. Clémençon and A. Sabourin)

Engineers

• William de Vazelhes (2017-, working on metric-learn)

Visiting PhD students

• Tejas Kulkarni (2018, University of Warwick)
• Valentina Zantedeschi (2018, University of Saint-Etienne)

Master interns

• 2018: Antoine Capriski (University of Lille), Arthur d'Azémar (University of Lille)
• 2016: Thibault Liétard (ENS Rennes), Paul Vanhaesebrouck (École Polytechnique - best internship award), Pierre Dellenbach (École Polytechnique), Robin Vogel (ENSAE)
• 2015: Maxime Flauder (ENS Paris)