As a French PhD student, I am passionate to whatever comes close to Artificial Intelligence & Earth Observation. Whether it is theoretical content with exploring state-of-the-art models or more concrete applicative programming with Jupyter Notebooks, I always find myself curious about what the world is up to !
Additionally, I am currently exploring the depth of SAR imagery, understanding its underlying concepts such as Polarimetric SAR or Interfometry.
You may also explore the personal projects I find myself doing on my free time hosted on this website. I have focused on exploring different facets of Deep Learning with applications in Multimodal Learning, Computer Vision or Automatic Feature extraction for Remote Sensing applications. All these projects’ repositories are hosted on Github.
PhD in Applied Physics, 2020-2023
SONDRA Laboratory, CentraleSupélec, Gif-sur-Yvette, France
MSc in Artificial Intelligence & Multimodal Interaction, with Distinction, 2019-2020
Heriot-Watt University, Edinburgh, Scotland
Engineering degree in Computer Science, 2017-2020
EISTI, Cergy, France
BSc degree in Computer Science, 2015-2018
Cergy-Pontoise University, Cergy, France
Working on problematics of change detection in SAR Time Series of forests with the help of Deep Learning methods.
SONDRA Laboratory is a laboratory mixing 4 entities: French ONERA, Supélec (known today as CentraleSupélec), the National University of Singapore and the DSO of Singapore.
As a deep learning Intern, I have trained, tuned and tested a model capable of doing building segmentation using satellite imagery. In this internship, I have tried multiple models (Mask RCNN, UNet, Deep UNet) and tried to take the best out of them all. The code was written using Keras with Tensorflow Back-End and was manipulated using a web-based RESTFul GUI with Flask and HTML5 technologies.
Also, multiple postprocessing technologies were considered and tried such as Logistic Regression (using scikit-learn) or Conditional Random Field (using pycrf).