Head of Team : Paul Honeine

The main objective comprises modeling and statistical learning studies designed to grasp data diversity (dimensionality, structures, non-stationarity), as well as the nature of the expected solutions (prior knowledge). 


  • Multi-source, multi-view, multi-modal learning
  • Semi-supervised or weakly supervised learning
  • Transfer learning, domain adaptation, optimal transport
  • Robust machine learning and trusted AI
  • Deep neural networks
  • Recurrent neural networks
  • Generative adversarial networks
  • Graph analysis, graph matching, metrics on graphs
  • Graph-based models, multi-scale representations
  • Sparse models, dictionary learning and tensor decomposition
  • Information fusion, ensemble learning, random forests
  • Spatial and temporal information modeling
  • Federated learning and serious games
  • Apps

    • Handwriting recognition
    • Analysis of handwritten documents
    • Analysis of old documents
    • Information retrieval
    • Medical imaging
    • Analysis of sports gestures
    • Analysis and synthesis of molecules
    • Analysis of road scenes
    • Road traffic analysis
    • Risk management
    • Historical spatial dynamics
    • Analysis and description of audio scenes
    • Time series analysis


    International Collaborations

      National Academic Collaborations
      Academic Collaborations in Normandy
      Industrial Partners


      Chaire IA RAIMO : A Road toward safe Artificial Intelligence in MObility (ANR, 2020-2024)

      EXO-POPP : Optical Extraction of Handwritten Named Entities for the Marriage Certificates of the Population of Paris (1921-1946) (ANR, 2021-2024)
      Main partner : LARHRA

      CATCH : Automatic Understanding of Human Sensors Testimonials (ANR, 2021-2023)
      Partners : SaagieAtmo Normandie

      LabCom L-Lisa : Common Lab LITIS-Saagie (ANR, 2021-2025)
      Partner : Saagie

      SCHISM : Supporting CHemoinformatics via Interactive unsupervised and Semi-supervised data Mining (FEDER, 2020-2024)

      Partners : GREYCCERMN

      DeepART : Medical Image collection, segmentation and anonymization for DEEP learning in Adaptive Radiation Therapy (2020-2022)
      Partners : Centre François BaclesseCentre Henri BecquerelGREYC

      WeSmile : Weakly supervised medical image segmentation (PHC Van Gogh, 2020-2022)

      Partner : Eindhoven TU

      AMCAS : Machine Learning for Understanding Secondary Atomization (2020-2022)
      Partner : CORIA

      THESIS : THermoplastic Erosion Shield for new generation Ice protection System (2019-2022)
      Partners : Zodiac Aerosafety Systems (nowadays SAFRAN), Analyse and Surface, Dedienne Multi-plasturgy Group, IRSEEMGPMCORIA

      ESCAPE SG : Serious game for evacuation management (2019-2023)


      APi : Taming the Beast of the Preimage in Machine Learning for Structured Data : Signal, Image and Graph (ANR, 2018-2023)

      Partners : LTCI, LIG

      PAUSE APi : in complement to ANR APi, this funding is from the national program for the urgent aid and reception of scientists in exile (PAUSE, from the Collège de France)

      NormanD'eep : Deep learning and application (2018-2022) 
      Partner : GREYC

      ASTURIAS : Newspaper image analysis (2018-2022) 
      Partners : GREYCMIND group of LITIS

      DeepInFrance : Machine learning with deep neural networks (ANR, 2018-2022)
      Partners : INRIA GrenobleI3SGREYCLIF-AMULIP6

      LEAUDS : Learning to understand audio scenes (ANR, 2018-2022)
      Partners : NetatmoINRIA Nancy - Grand Est

      ESCAPE : Exploring by Simulation Cities Awareness on Population Evacuation (ANR, 2017-2022)

      DynACEV : Dynamics of Learning: Behaviour and Lived Experiences. The role of exploratory strategies (ANR, 2017-2022)
      Partners : CETAPSISSUL (Lausanne), MIPSOPESES (Nouvelle-Zélande), SPARC (UK) 

      OATMIL : Optimal Transport and Machine Learning (ANR, 2017-2022) 
      Partners : IRISALagrangeTechnicolor 

      HBDEX :  Analysis of historical financial data (ANR, 2017-2022)
      Partners : PSECAMSIRISA

      POPP : Project for the oceration of the Paris population census (1921-1946) (2020-2021) - voir démonstrateur ici
      Partner : LARHRA

      EurHisFirm :  The European project aims at designing a world-class research infrastructure to collect, merge, extract, collate, align and share detailed historical high-quality firm level data for Europe (H2020 EU, 2018-2021)
      Partners : Consortium of 16 European partners 

      Read E-Doc : Development of a system for extracting specific alphanumeric information from dematerialized container shipment slips (2019-2021)
      Partner : Toshiba

      Our PhD students in industry (CIFRE programs) with companies DataHertzLokadTEKLIAWeinig LuxscanActemiumSOLYSTICItekube




      PIVAJ (Plateforme d'Indexation et de Visualisation d'Archives de Journaux) is a software suite for automatic analysis and display of digitized historical newspapers.The offline part extracts sections, articles and text from the digitized images, to build structured METS/ALTO files.

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      DocExplore is a free software suite to build and display augmented books from digitization. DocExplore is WySiWyG, no XML knowledge required, and works on Windows, MacOs and Linux.

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      Products and tools from the Machine Learning group

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