Information

Bio

I am Paul Borne Pons, a soon to be graduated Master Student in Computer Science (Machine learning) with a solid foundation in Applied Mathematics. I will soon be holding an engineering degree from CentraleSupélec with a major in Data Science and master from ENS Paris Saclay (MVA) in applied mathematics. Prior to my current internship I had two work experiences, both in research in Machine Learning. First at Therapancea where I used Deep Learning to help the treatment of patients ill with Multiple Sclerosis. Secondly at KTH where I assessed damage to buildings following natural disasters using satellite images using semi-supervised learning.

Current Role

Currently, I serve as a Research Intern at Adobe research Paris where I am working on terrain reconstruction and generation using satellite images and generative machine learning.

Areas of Expertise

My expertise lies in several key areas of Computer Science, including Geoinformatics, Computer Vision and Machine Learning. These specializations enabled me to create a global DEM expansion to Major TOM and train a diffusion model capable of generating 3D terrains on the created dataset. In line with the Major TOM initiative, the creation of the dataset and its use on innovative new algorithms significantly benefits ESA Φ-lab. My project not only advances the use of ESA data (Copernicus DEM) by reprocessing it to a format suitable for Machine Learning approaches, but also encourages the Machine Learning community to engage with it by demonstrating its potential through the diffusion model training.

Vision for the Future

After having spent 6 month exploring the diversity of earth landscapes through my project, I know want to use my skills in Machine Learning and Remote Sensing to help preserve our natural heritage. After a short-term contract to continue gain experience in these fields I plan on starting a PhD at the beginning of the next academic year (2025).

Project as CIN Researcher