About me

I'm currently a PhD student at ENS Paris-Saclay, in the Centre Borelli's Machine Learning and Massive Data Analysis (MLMDA) team, in collaboration with EDF Lab's consumption forecasting team, under the supervision of Argyris Kalogeratos, Mathilde Mougeot and Yvenn Amara-Ouali. My work involves testing new methods for forecasting electricity consumption and production for power grid management, using graph-based neural networks. In particular, I'm interested in developing temporal and dynamic graph models that can be explained in the same way as generalized additive models. Prior to this, I graduated from Télécom SudParis with a specialization in mathematics, as well as from the Master MVA program at the ENS Paris-Saclay, in 2023. I was able to spend a semester in the Netherlands at the University of Twente as part of the Erasmus program in 2022, where I was able to learn more about Machine Learning.

Keywords: Machine Learning, Artificial Intelligence, Graph Neural Networks, Time Series Forecasting, Explainability, Renewable Energy.

Miscellaneous

:books: Books

Here is a non-exhaustive list of books I recommend based on the themes they cover.

  • Economy

    • Donella Meadows, Dennis Meadows, Jørgen Randers, and William Behrens III. The Limits to Growth: a Report for the Club of Rome’s Project on the Predicament of Mankind. New York: Universe Books, 1972.

    • The Shift Project. Climat, crises: Le plan de transformation de l’économie française. Odile Jacob, 2022.

    • Timothée Parrique. Ralentir ou périr: L’économie de la décroissance. Seuil, 2023.

  • Climate

    • Inès Léraud, Pierre van Hove. Algues vertes, l’histoire interdite. Delcourt, 2019.

    • Jean-Marc Jancovici, Christophe Blain. Le Monde sans fin. Dargaud, 2021.

    • Anne Bres, Claire Marc, Bonpote. Tout comprendre (ou presque) sur le climat. CNRS Éditions, 2022.

  • Science fiction

    • René Barjavel, Christian de Metter. La nuit des temps. Phileas, 2021.