Résumé
Hello there! I am a Double Master student at Polytechnique Montréal working in collaboration with the European Space Agency to develop cutting-edge Machine Learning algorithms for Mars rover navigation, tackling the unique challenges of interplanetary travel.
I'm driven to unlock the mysteries of the universe and make it accessible to everyone, connecting the world. For that, I am dedicated to creating AI solutions that transform our technological interactions and expand the frontiers of space exploration.
Expériences professionnelles
Machine learning research scientist
EUROPEAN SPACE AGENCY , Noordwijk - Stage
De Avril 2024 à Août 2024
- Developed AI algorithms for rovers to navigate autonomously on Mars, where communication delays prevent real-time guidance from Earth.
- Implemented techniques related to image segmentation, uncertainty quantification and conformal prediction for space exploration, and evaluating on-board applications.
- Applied mathematical methods to enable the rover to autonomously determine its path, leveraging the information obtained from uncertainty quantification to minimize risks and ensure the mission's success.
Project researcher on immersive technologies (freelance)
MINISTERE DES ARMEES , Paris - CDD
De Septembre 2023 à Novembre 2023
Selected by the French Ministry of the Armed Forces to undertake an analysis of immersive technologies (VR, AR, and MR) in parallel of my studies. Conducted an in-depth study on their functional principle, their applications, but also future possibilities in the fields of defense, security, and civilian use.
Deep learning research scientist
CNRS - CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE , Saint-étienne - Stage
De Juillet 2023 à Septembre 2023
Optimized the effectiveness of Natural Language Processing tasks by implementing Transformers and attention mechanisms inspired by human cognition
Machine learning research scientist intern
INRIA , Palaiseau - Stage
De Janvier 2023 à Janvier 2023
• Uncertainty quantification in convolutional neural networks
• Reduction of epistemic and aleatoric uncertainties using Ensemble techniques
and Bayesian methods (Monte Carlo dropout, variational autoencoders, etc.)
- 40% reduction in MPIW for a real estate price prediction algorithm
- 65% reduction in epistemic uncertainty
- 30% reduction in aleatoric uncertainty
Formation complémentaire
Double Master Degree
Polytechnique Montréal - Machine Learning
2024 à 2026
Double MSc degree in Machine Learning at the prestigious Polytechnique Montréal, Canada. My coursework has a strong research focus and includes:
• Machine Learning
• Reinforcement Learning
• Natural Language Processing
• Procedural Programming in Python
• Linear Algebra
Parcours officiels
Langues
Français - Langue maternelle
Anglais - Courant
Italien - Courant
Arabe - Notions
Russe - Notions
Chinois - Notions
linkedin
website
github