Team

You want to join? I am always looking for outstanding and highly motivated people to work (as intern, Ph.D. student, post-doctorate or research engineer) on machine learning, optimization and statistics. More precisely, my team is are working on:

• citizen science and crowdsourcing
• high dimensional / robust statistics, variable selection, sparsity
• optimization for machine learning (including federated learning, privacy, etc.)

The application process is light:

• Email a CV, a transcript of recent grades, and explain in a short paragraph why you are interested to join.
• Upon interest, I will ask two reference letters (one only for interns or Ph.D. students) to be sent directly to me.
• At this stage an interview (possibly online) will be arranged to double-check your skills and profile compatibility.

Post-docs

• Jean-Baptiste Fermanian 2023-2024

Ph.D. Students

• Theo Larcher: co-supervised by Alexis Jolly 2024-2027?
• Antoine Simoes: co-supervised by Yohann de Castro 2022-2025?

Alumni

• Tanguy Lefort [Ph.D. 2021-2024] co-supervised with Benjamin Charlier and Alexis Joly (now post-doctorate at Inria Lille)
• Axel Dubar 2023-2024, now Ph.D. student at Univ. Montpellier.
• Camille Garcin [Ph.D. 2020-2023] co-supervised by Alexis Joly and Maximilien Servajean (now post-doctorate at Inria Montpellier)
• Emmanuel Pilliat [Ph.D. 2020-2023] co-supervised by Nicolas Verzelen and Alexandra Carpentier (now assistant professor at ENSAI)
• Hashem Ghanem [Ph.D. 2020-2023] co-supervised by Samuel Vaiter and Nicolas Keriven (now datascientist at Expleo)
• Damien Blanc [Ph.D. 2019-2022], co-supervised by Benjamin Charlier and funded by Quantacell
• Cassio Fraga Dantas Post-doctorate associate: 2022, (now at Researcher at INRAE)
  
• Florent Bascou [Ph.D. 2019-2022], co-supervised by Sophie Lèbre,
  Manuscript: “Sparse linear model with quadratic interactions”
• Quentin Bertrand [Ph.D. 2018-2021], co-supervised by Alexandre Gramfort (now at Mila),
  Manuscript: “Hyperparameter selection for high dimensional sparse learning : application to neuroimaging”
• Nidham Gazagnadou [Ph.D. 2018-2021] co-supervised by Robert Gower (now at Sony AI),
  Manuscript: “Expected smoothness for stochastic variance-reduced methods and sketch-and-project methods for structured linear systems”
• Pierre-Antoine Bannier [Intern 2021], co-supervised by Alexandre Gramfort
• Jérôme-Alexis Chevalier [Ph.D. 2017-2020], co-supervised by Bertrand Thirion (Senior Data Scientist at Emerton Data),
  Manuscript: “Statistical control of sparse models in high dimension”
• Mathurin Massias [Ph.D. 2016-2019], co-supervised by Alexandre Gramfort (now CR INRIA, Lyon),
  Manuscript: “Sparse high dimensional regression in the presence of colored heteroscedastic noise : application to M/EEG source imaging”
• Evgenii Chzhen [Ph.D. 2016-2019], co-supervised by Mohamed Hebiri (now CR CNRS, Saclay),
  Manuscript: “Plug-in methods in classification”
• Eugene Ndiaye [Ph.D., 2015-2018], co-supervised by Olivier Fercoq (now post-doctorate at GeorgiaTech),
  Manuscript: “Safe optimization algorithms for variable selection and hyperparameter tuning”
• Jean Lafond [Ph.D., 2013-2016] co-supervised by Éric Moulines (now at Cubist Systematic, UK),
  Manuscript: “Complétion de matrice : aspects statistiques et computationnels”
• Igor Colin [Ph.D., 2013-2016] co-supervised by Stéphan Clémençon and funded by Streamwide (now at Huawei),
  Manuscript: “Adapting machine learning methods to U-statistics”
• Jair Montoya [Post Doc, 2016-2017], co-supervised by Olivier Fercoq
• Thierry Guillemot (now at ARIADNEXT) Engineer, co-supervised by Alexandre Gramfort, 2016