About Me

I am a postdoctoral researcher fellow at McGill University and Mila (Quebec Artificial Intelligence Institute), in collaboration with David Rolnick. My research projects are focused on multi-modal and multi-task deep learning for remote sensing applied to forest monitoring. I am co-founder and chief technical officer (CTO) of Rubisco AI, a Mila startup that aims to monitor forest restoration projects. I am also a core team member of Climate Change AI where I lead the webinar team and I co-led the organization of the CCAI workshop at ICLR 2024.

I completed my Ph.D. in March 2022 in collaboration between Institut Polytechnique de Paris (Télécom Paris; IDS department, IMAGES Team) and valeo.ai, under the supervision of Patrick Pérez, Florence Tupin and Alasdair Newson. The aim of my Ph.D. work was to use and adapt deep neural network architectures for scene understanding using automotive radar data and multi-sensor fusion. During my Ph.D., I co-organized the Deep Learning Working Group of the IMAGES Team (Télécom Paris). I graduated from M.Sc. Machine Learning and Big Data at Télécom Paris in 2018, M.Sc. (1 and 2) in Statistical Modelling at Paris Panthéon Sorbonne University in 2016 and B.S. in Applied Mathematics at Paris Diderot University in 2014.


  • 06/2024: I’m invited to speak at the Mila Entrepreneurs’ Night on AI for Climate (register here).

  • 05/2024: We (Prof. Etienne Laliberté and I) officially started Rubisco AI, a Mila startup that aims to monitor forest restoration projects. Have a look to our demo here. More details to come soon.

  • 05/2024: Our workshop Tackling Climate Change with Machine Learning was held in Vienna at ICLR 2024. All accepter papers and the fully recording of the workshop are available online at this url.

  • 12/2023: Our new work FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models is now on ArXiv! Code and datasets will be release soon.

  • 11/2023: Our new work OpenForest: A data catalogue for machine learning in forest monitoring is now on ArXiv! The OpenForest catalogue is also available on github.

  • 02/2023: Starting a core team member position at Climate Change AI as the webinar team leader.

  • 09/2022: Starting my postdoc on forest monitoring using deep learning at McGill and Mila in Montreal, Canada.

  • 04/2022: My 2021 ICPR and ICCV articles have been quoted at the Nvidia GTC by Geoffrey Bouquot, group CTO and VP at Valeo. Link of the presentation is here.

  • 03/2022: Finally defended my PhD, the manuscript is available here.

  • 03/2022: Our recent work ``Raw High-Definition Radar for Multi-Task Learning” has been accepted at CVPR 2022!

  • 12/2021: New preprint available on ArXiv: Raw High-Definition Radar for Multi-Task Learning. The new RADIal dataset and the code of our proposed deep learning model are available on the github repo!

  • 08/2021: Our work on multi-view radar semantic segmentation has been accepted at ICCV 2021!

  • 07/2021: Last but not least, the RAD tensors of the CARRADA dataset are now available.

  • 05/2021: Code for Multi-View Radar Semantic Segmentation (MVRSS) and pretrained models have been publicly released on github here.

  • 04/2021: A new version of CARRADA is available! Have a look to the repo here.

  • 04/2021: New preprint available on ArXiv: Multi-View Radar Semantic Segmentation.

  • 01/2021: The paper of CARRADA has been presented in a poster session of ICPR 2020.

  • 10/2020: First paper accepted to ICPR 2020!