Aurélien Bellet

Ongoing

• PRIDE (Privacy-Preserving Decentralized Machine Learning), 2021-.
  Funded by the French National Research Agency (ANR). Role: Principal Investigator.
• FLAMED (​Federated Learning and Analytics on Medical Data), 2020-.
  Funded by Inria. Role: Principal Investigator.
• SLANT (​Spin and Bias in Language Analyzed in News and Texts), 2019-.
  Funded by the French National Research Agency (ANR). Role: Project Member.
• 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: 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: Principal Investigator.

Past

• SkMetricLearn (Scikit-learn Integrated Metric Learning), 2017-2019.
  Software development project funded by INRIA. Role: Principal Investigator.
• 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

• Privacy Preserving Machine Learning
  Workshop at CCS 2021, Seoul (South Korea), November 2021
  Co-organized with James Bell, Adrià Gascón, Olya Ohrimenko, Mariana Raykova, Phillipp Schoppmann, Carmela Troncoso.
• Privacy Preserving Machine Learning
  Workshop at NeurIPS 2020, Virtual event, December 2020
  Co-organized with Borja Balle, James Bell, Kamalika Chaudhuri, Adrià Gascón, Antti Honkela, Antti Koskela, Casey Meehan, Olya Ohrimenko, Mijung Park, Mariana Raykova, Mary Anne Smart, Yu-Xiang Wang and Adrian Weller.
• Federated Learning One World Seminar (FLOW)
  Online seminar on federated learning with 1000+ registered attendees, started in May 2020
  Co-organized with Peter Richtárik, Virginia Smith, Dan Alistarh and Sebastian Stich.
• 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

Editorial activities in international journals

• Action editor of Transactions on Machine Learning Research (TMLR) since 2022

Area Chair in international conferences

• NeurIPS 2020 2021 2022
• ICML 2019 2020 2021 2022
• AISTATS 2022 2023

PC Member in conferences & workshops

• ICML 2015 2016 2017 2018
• NIPS (now NeurIPS) 2016 2017 2018 2019
• AISTATS 2017 2018 2019 2020 2021
• ECML/PKDD 2011 2013 2014
• IJCAI 2013 2016
• SaTML 2022
• MOD 2015 2016 2017
• CAp 2018 2019 2020 2021
• Privacy in Machine Learning and Artificial Intelligence workshop at ICML & IJCAI 2018
• Privacy-Preserving Machine Learning workshop at CSS 2019
• Privacy in Machine Learning workshop at NeurIPS 2019
• Federated Learning workshop at NeurIPS 2019
• Workshop on Privacy-Preserving Artificial Intelligence at AAAI 2020
• Towards Trustworthy ML: Rethinking Security and Privacy for ML at ICLR 2020
• International Workshop on Federated Learning for User Privacy and Data Confidentiality at IJCAI 2020
• Workshop on Privacy-Preserving Artificial Intelligence at AAAI 2021
• International Workshop on Federated Learning for User Privacy and Data Confidentiality at ICML 2021
• Workshop on Privacy-Preserving Artificial Intelligence at AAAI 2022
• International Workshop on Federated Learning: Recent Advances and New Challenges at NeurIPS 2022

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

Postdocs

• Batiste Le Bars (2021-, Inria-EPFL fellowship, co-supervised with M. Tommasi and A.-M. Kermarrec)
• Mohamed Maouche (2019-2021, co-supervised with M. Tommasi and E. Vincent)
• Melissa Ailem (2017-2018, Inria@SiliconValley fellowship, co-supervised with P. Denis, F. Sha and M. Tommasi)

PhD students

• Edwige Cyffers (2021-)
• Jean-Remy Conti (2021-, CIFRE thesis with IDEMIA, co-supervised with S. Clémençon)
• Gaurav Maheshwari (2020-, co-supervised with P. Denis)
• Paul Mangold (2020-, co-supervised with M. Tommasi and J. Salmon)
• Mahsa Asadi (2018-, co-supervised with M. Tommasi)
• Brij Mohan Lal Srivastava (2018-2021, co-supervised with M. Tommasi and E. Vincent)
• Mariana Vargas Vieyra (2018-2021, co-supervised with P. Denis)
• Robin Vogel (2017-2020, CIFRE thesis with IDEMIA, co-supervised with S. Clémençon)

Engineers

• Yannick Bouillard (2020-2021, co-supervised with M. Tommasi)
• William de Vazelhes (2017-2019, working on metric-learn)

Visiting PhD students

• Ali Shahin Shamsabadi (2020-2021, Queen Mary University of London)
• Tejas Kulkarni (2018, University of Warwick)
• Valentina Zantedeschi (2018, University of Saint-Etienne)

Master interns

• 2021: Maxence Noble (École Polytechnique)
• 2020: Edwige Cyffers (ENS Lyon)
• 2019: Paul Mangold (ENS Lyon)
• 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)