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News:

  • April 2024: paper accepted in QJRMS, “Online machine-learning forecast uncertainty estimation for sequential data assimilation“, by Sacco et al., more details here
  • January 2024: paper accepted in TGRS, “Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks“, by Colin et al., more details here
  • January 2024: new preprint in EGUsphere, “Could old tide gauges help estimate past atmospheric variability?“, by Platzer et al., more details here
  • December 2023: new preprint in EGUsphere, “Selecting and weighting dynamical models using data-driven approaches“, by Le Bras et al., more details here

Timeline:

  • Since 2023: HDR received from UBO (Brest, France)
  • Since 2022: member of the Odyssey research team (with INRIA and IFREMER)
  • Since 2022: editor of EGU journal Nonlinear Processes in Geophysics
  • Since 2021: head of the Ocean Data Science Master program, with IUEM, ENSTA Bretagne, and IMT Atlantique
  • Since 2021: head of the AI & Ocean program at Lab-STICC, UMR CNRS 6285
  • Since 2018: associate researcher at the Data Assimilation Research Team, RIKEN Center for Computational Science (Kobe, Japan)
  • Since 2015: associate professor at IMT Atlantique (Brest, France) and researcher at lab-STICC (French CNRS laboratory)
  • 2012-2015: postdoctoral researcher on spatial oceanography and statistics at Télécom Bretagne (Brest, France)
  • 2010-2012: postdoctoral researcher in data assimilation at the Atmospheric Science Research Group, Univ. Corrientes (Argentina)
  • 2007-2010: PhD at the Oceanography from Space Laboratory of IFREMER (Brest, France)

Research topics:

— Data assimilation:

  • dynamical systems
  • analog forecasting
  • estimation of model and observation error covariances
  • subgrid-scale parameterizations

— Remote Sensing:

  • spatio-temporal variability
  • satellite interpolations
  • statistical post-processing
  • remote sensing synergy
  • classification and clustering
  • free-access datasets

— Climate change, renewable energies and risk assessment:

  • detection and attribution
  • renewable energy forecasting
  • prediction of extreme events
  • uncertainty quantification in climate predictions

Teaching activities:

  • Probability and statistics (40h/year)
  • Machine learning and deep learning (20h/year)
  • Big data and cloud computing (30h/year)
  • Data assimilation (30h/year)

Organizing:

  • “SALMO-Skol“ (summer school)
    2022-2023, Sizun, France, more details here
  • “Ocean Remote Sensing Synergy“ (summer school)
    2014-2015-2016-2018-2019, Brest, France, more details here
  • “Data Science & Environment“ (workshop + summer school)
    2017, Brest, France, more details here
  • “Statistical Modeling and Machine Learning in Meteorology and Oceanography“ (workshop)
    2019-2020-2021, Brest, France, more details here
  • “Data Science for Geosciences“ (doctoral course)
    2018-2019-2020, France, more details here

Contacts: