Scholar
Arthur Ouaknine
Google Scholar ID: OCT3E9wAAAAJ
McGill University, Mila
Citations & Impact
Citations
558
H-index
7
i10-index
6
Publications
15
Co-authors
6
list available
Publications
Resume (English only)
Academic Achievements
- - September 2025: Two accepted papers at NeurIPS 2025 on individual tree crown segmentation and vegetation trait prediction with hyperspectral data.
- - July 2025: Invited talks at LASTIG lab (IGN) in Champs-sur-Marne, France, and CMCC workshop on AI for Carbon in Como, Italy.
- - May 2025: Invited talk at Yale Center for Natural Carbon Capture Spring Symposium, in New Haven, USA.
- - December 2024: Paper 'FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring' accepted and will be presented at AAAI.
- - October 2024: Paper 'OpenForest: A data catalogue for machine learning in forest monitoring' accepted in Environmental Data Science (journal).
- - August 2024: Granted the IVADO Postdoc Entrepreneur Fellowship to support and develop further the project, Rubisco AI.
- - June 2024: Invited to speak at the Mila Entrepreneurs’ Night on AI for Climate.
- - May 2024: Officially started Rubisco AI, a Mila startup that aims to monitor forest restoration projects.
- - May 2024: Organized the workshop 'Tackling Climate Change with Machine Learning' at ICLR 2024.
- - December 2023: New work 'FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models' now on ArXiv.
- - November 2023: New work 'OpenForest: A data catalogue for machine learning in forest monitoring' now on ArXiv.
- - February 2023: Started a core team member position at Climate Change AI as the webinar team leader.
Research Experience
- - Researcher Fellow at McGill University and Mila, collaborating with Prof. David Rolnick and Prof. Etienne Laliberté.
- - Co-founder and CTO of Rubisco AI, aiming to monitor forest restoration projects.
- - Core team member of Climate Change AI, leading the webinar team and co-organizing the CCAI workshops at ICLR 2024 and NeurIPS 2024.
- - During his Ph.D., co-organized the Deep Learning Working Group of the IMAGES Team (Télécom Paris).
Education
- - Ph.D.: March 2022 from Institut Polytechnique de Paris (Télécom Paris; IDS department, IMAGES Team) in collaboration with valeo.ai, supervised by Patrick Pérez, Florence Tupin, and Alasdair Newson.
- - M.Sc.: 2018 in Machine Learning and Big Data from Télécom Paris, 2016 in Statistical Modelling from Paris Panthéon Sorbonne University.
- - B.S.: 2014 in Applied Mathematics from Paris Diderot University.
Background
- Research Interests: Multi-modal and multi-task deep learning for remote sensing, particularly in forest monitoring. Expertise: Deep learning, computer vision, and remote sensing, with a strong emphasis on sustainability and environmental impact.
Miscellany
- No detailed information provided about personal interests or hobbies.
