Preparatory Program for entry to French Grandes Écoles d’Ingénieurs, 2018
MSc in Computer Science and Mathematics Engineering, 2022
MSc Computer vision and machine learning, 2022
Work experience
April 2023 – September 2023: Research Engineer at INRIA, PreMeDICaL
Working under the supervision of Julie Josse and Erwan Scornet on Causal inference.
April 2022 – October 2022: Research Intern at EPFL, LCAV
Worked under the supervision of Julien Fageot on statistical analysis of sparse inverse problem algorithm.
Adapted and re-wrote a paper on sparse inverse problem to the periodic case.
April 2021 – July 2021: Research Intern at ENS ULM & CNRS
Worked on biology applications of machine learning under the supervision of Guillaume Dugu ́e and Pierre Latouche.
Built an acquisition setup and calibrated multiple cameras in order to reconstruct a 3D model of rats.
Trained a neural network to track the behaviors of the rats using multiple camera views with DeepLabCut.
January 2021 – April 2021: Research Intern at Air France & CERMICS
Worked under the supervision of Axel Parmentier on the prediction of the delays of airplanes using a stochastic model.
Applied a Lagrangian relaxation on the pricing sub-problem to derivate a lower bound of the minimization problem
Implemented a sub-gradient method to minimize delay costs based on the previous stochastic model.
Projects
October 2019: Operations Research Project
Finalist in an operations research contest co-organized by Renault and École des Ponts.
Implemented an algorithm that dictates when and where to deliver materials to the Renault factories in order to optimize time, CO2 emissions and costs.
The solution we developed was 40% more efficient than the standard solution by Renault.
November 2018 – March 2020: Self Driving Vehicle
In a group of three supervised by Fawzi Nashashibi, we designed and programmed a semi-autonomous driving vehicle for disabled people capable of moving freely in public areas or museums.
Implemented a SLAM algorithm in order to localize the vehicle in closed and known environments.
Trained a CNN to detect and recognise objects near the car to ensure a safer drive.