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Learning brain structure, function and variability from neuroimaging data.
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Home Research Statistical and machine learning methods for large-scale data

Statistical and machine learning methods for large-scale data

Scientific projects

  • Stochastic subsampling for factorizing huge matrices
  • Fast independent component analysis solver for real data
  • Convolutional Dictionary Learning Model at scales
  • Learning from heterogeneous data
  • Fast clustering with the Recursive Nearest agglomeration algorithm
  • Magneto-encelography source localization
  • Untangling high dimensional statistical inference problems using clustering and ensembling
  • Causal inference with mediation using machine learning in high-dimensional settings
  • Feature grouping as a Stochastic Regularizer for High-Dimensional Structured Data 

Partners and related efforts

  • Paris-Saclay Center for Data Science – Phase 2: Parietal is the host of the data-scientists for the CDS
  • Dataia: Parietal is funded by DataIA: MissingBigData project. Bertrand Thirion is the head of Dataia since January 2019.

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  • Parietal is an Inria-CEA joint Team part of the Neurospin research center where our main partners are the UNICOG and UNATI.

  • News & Events

    • We’re hiring !
    • PhD defence of Valentin Iovene on November 23rd, 3PM at Inria Saclay
    • Congratulations to Hamza
    • PhD defense of Hicham Janati on March 23rd
    • PhD defense of Antonia
  • Research Projects

    • BrAIN
    • DirtyData
    • fast-big
    • HiDimStat
    • Individual Brain Charting
    • INSERM-INRIA 2018
    • Karaib
    • Large Small Brain Nets
    • meegBIDS
    • MetaMRI
    • MissingBigData
    • NeuroLang
    • Paris-Saclay CDS
    • SLAB
    • TVB-Cloud
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