Current Ph.D. students

    • Etienne Bost (co-supervision with Thomas Schatz, QARMA, LIS)
    • Etude de la perception des sons naturels pour la description automatique de scènes sonores
    • Christian Ferreyra (co-supervision with Bruno Giordano, INT) on Studying cerebral representation of sounds with machine learning
    • Loris Berthelot (co-supervision with Annie Zavagno, LAM, and François-Xavier Dupé QARMA, LIS)
    • Big Data et Apprentissage automatique pour l’étude de la formation stellaire Galactique
    • Hamed Benazha (co-supervision with Stephane Ayache, QARMA, LIS)
    •  Explainability and interpretability in recurrent neural networks

Past Ph.D. students

    • Swetali Nimje (co-supervision with L. De Rochefort, CRMBM, These ANR IA)
    • An Evaluation of Scan-Specific Deep-Learning Strategies for Brain MRI Reconstruction, defended November 22nd, 2024.
    • Malek Senoussi (co-supervision with Paul Villoutreix, LIS, INSERM et Institut Centuri)
    • A mathematical framework for morpho-transcriptomics, defended July 2nd, 2024.
    • Charly Lamothe (co-supervision with Pascal Belin, INT, Sylvain Takerkart, INT, Etinne Thoret INT, et Stéphane Ayache QARMA@LIS), defended December 5, 2023. Now postdoc at Institut Pasteur with  Brice Bathellier.
      Exploring the vocal brain with deep networks.
    • Luc Giffon (co-supervision with Hachem Kadri and Stéphane Ayache at LIS), defended December 2020.
      Approximations parcimonieuses et méthodes à noyaux pour la compression de modèles d’apprentissage. Now postdoc et ENS Lyon with Rémi Gribonval.
    • Mickael Chen (co-supervision with Ludovic Denoyer, Facebook and Paris Sorbonne University), defended July 2020. Now at Valeo (https://sites.google.com/view/mickaelchen/)

      Learning with weak supervision using deep generative networks.

    • (Miss) Qi Wang (co-supervision with S. Takerkart at INT Marseille).

      Multisource learning and neuroscience (defended February 2020).

    • Ziyu Guo (co-supervision with Y. Coadou and C. Diaconu at CPPM).
      Deep Learning for High Energy Physics (defended July 2019).
    • (Mister) Qi Wang
      A generic statistical model for Avatar animation in multiple setting, defended July 2018.
      Now: Machine Intelligence Team of DAMO Academy, Alibaba Group (https://damo.alibaba.com/labs/?lang=en)
    • Jeremie Tafforeau (co-supervision with F. Béchet et B. Favre at LIF), defended september 2017.
      Distributed word representation for information extraction in texts
    • Gabriella Contardo (co-supervision with L. Denoyer at LIP6, UPMC), defended July 2017.
      Machine learning under budget constraints
      Now: Research Fellow in Data Science and Astro @ SISSA
    • Olivier Dufour (co-supervision with Mathieu Lecorre (Univ Réunion), defended February 2016.
      Reconnaissance de chants d’oiseaux
    • Yu Ding  (co-supervision with C. Pelachaud at LTCI, Telecom ParisTech), defended September 2014.
      Modèle statistique de l’animation expressive de la parole et du rire pour un agent conversationnel animé
      Now: Postdoc university of Houston at NetEase in Hangzhou (near Shanghai) leading a team on virtual agents
    • Moustapha Cissé (co-supervision with Patrick Gallinari at LIP6, UPMC), defended June 2014.
      Efficient Extreme Classification
      Now: Researcher at Facebook Artificial Intelligence Research (FAIR).
    • Mathieu Radenen
      Contextual Markovian Models, defended September 2014.
      Now: Machine Learning Research Engineer at Audionamix
    • Antoine Vinel
      Réseaux de neurones profonds et traitement de séquences, defended December 2013.
      Now: Researcher (Apple)
    • Yann Soullard (co-supervision with Bernard Main / BBSP-Creo), defended in 2013.
      Apprentissage statistique et analyse technique pour la prévision de cours financiers
      Now: Assistant Professor at Rennes 2 University
    • Roxana Horincar (co-supervision with Bernd Amann and Nicolas Labroche)
      Bases de données, Graphes et apprentissage statistique
      Now: Postdoc at Telecom ParisTech
    • Trinh Minh Tri DO
      Modèles discriminants pour les séquences : champs Markoviens conditionnels, maximisation de la marge, defended June 2010.
      Now : Research engineer at Trusting Social
    • Juliette Brézillon (co-supervised with Charles Tijus) »
      Outil d’auto-évaluation pour la conduite automobile
    • Rudy Sicard
      Contributions à l’apprentissage par moyenne Bayésienne de modèles, defended 2008.
      Now: R&D engineer at Criteo
    • Henri Binsztok (co-supervison with Patrick Gallinari at LIP6, UPMC)
      Apprentissage de modèles Markoviens pour l’Analyse de séquences, defended in 2005.
      Now: Chief Innovation Officer at WALLIX Group
    • Alexander Estacio-Moreno (co-supervison with Patrick Gallinari)
      Thesis: Modèles d’Apprentissage pour l’Analyse de la Mobilité : Applications à des Parcours de Vie en Colombie
      Now: IRD
    • Sanparith Marukatat (co-supervison with Patrick Gallinari at LIP6, UPMC)
      Thesis: Une approche générique pour la reconnaissance de signaux écrits en ligne, defended in 2004.
      Now: Researcher, Information Research and Development Division, National Electronics and Computer Technology Center (NECTEC), Thailand
    • Haifeng Li (co-supervison with Patrick Gallinari at LIP6, UPMC), defended in 2002.
      Thesis: Traitement de la Variabilité et Développement de Systèmes Robustes pour la Reconnaissance de l’Ecriture Manuscrite En-ligne
      Now: Professor, School of Computer Science & Technology, Harbin Institute of Technology, China