This day, co-organized by EDF R&D and the French network of ENBIS (frENBIS), is part of our efforts to promote statistics and machine learning methods within businesses and the industry.
We offer you a morning of training on additive linear models led by Matteo Fasiolo, Senior Lecturer at the School of Mathematics - Statistical Science at the University of Bristol and author of the packages qGAM and mgcViz. The theoretical foundations of these methods will be discussed, along with examples of applications in R (notebooks).
The afternoon will be dedicated to presentations from the industry and academics about applications of machine learning for climate modeling.
Location: EDF Lab Saclay
Date: October 19 2023
The event is free, but registration is mandatory. Please fill out the form at the bottom of the page.
| Moring | |
|---|---|
| 09h00 - 09h30 | Welcome |
| 09h30 - 11h00 | GAM, lecture 1 |
| 11h00 - 11h15 | Pause |
| 11h15 - 12h30 | GAM, lecture 2 |
Pause déjeuner
| Après midi | ||
|---|---|---|
| 13h45 - 14h25 | Near real time global power production data and implications of climate extrêmes | Philippe Ciais, Directeur de recherche at Laboratoire des Sciences du Climat et de l’Environnement (LSCE) |
| 14h25 - 15h05 | Leveraging AI for weather prediction at Météo-France : a review of ongoing activities | Laure Reynaud, Researcher at CNRM (UMR 3589, Météo-France/CNRS), team leader of ‘Prévisibilité’, Groupe de Modélisation pour l’Assimilation et la Prévision |
| 15h05 - 15h45 | How to aggregate climate data to predict crop yield: an application to soybean, | Mathilde Chen, Postdoctoral fellow, Mathématiques et Informatiques Appliquées (MIA) Paris-Saclay, University Paris-Saclay, campus AgroParisTech INRAE) |
| 15h45 | Coffee & Networking |