Hi there! My name is Redouane (Red+1 if you like)!

I am interested in Machine learning for oceanic, atmospheric and climate sciences. My PhD thesis consisted in the use of analog methods (K-Nearest Neighbor Regression) to develop a new data-driven approach to tackle data assimilation: the Analog Data Assimilation (AnDA). In parallel to my thesis I investigated the use of deep learning methods for the segmentation of oceanic eddies from satellite-derived sea related maps.

Currently, I am a postdoc at IGE (Institut des Géosciences de l’Environnment) at Grenoble, France. My postdoc is funded by CNES (French Space Agency). I try to use machine learning methods for the inversion of SWOT data (CNES/NASA mission).

Google Scholar Profile

Researchgate

News:

• October 2019
  • Our paper “Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models” is accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2019
  • Pleased to serve as a Hackathon chair at Climate Informatics 2019 workshop, details
• June 2019
  • Invited talk: “Physical Oceanography meets Deep Learning”; Data Science for the future, a workshop of Global Science Week 2019, Grenoble, France.
• April 2019
  • The first IndabaXMorocco was a success! Glad to have taken part of the general chair committee.
  • Poster presentation at the European Geophysical Union 2019; Vienna, Austria. Abstract
• February 2019
  • Talk at AI & Climate-Ocean-Atmoshpere workshop; Rennes, France