Welcome to my webpage

A few words about me.


Postdoctoral Researcher [ Inria ] x [ Paris Brain Institute ]
PhD in Machine Learning applied to neurosciences.

Deep Learning Lead Instructor [ Le Wagon ].

Member of the Climate Collage [ Fresque du Climat ]

Always involved in new projects.

About me

I'm a Postdoctoral fellow in Machine Learning at the Paris Brain Institute, developing methods & tools to improve clinical trials for neurodegenerative diseases as Alzheimer's, Parkinson's and Huntington diseases. I obtained my PhD in Machine Learning under the supervision of Stanley Durrleman and Stéphanie Allassonnière in January 2020, as part of the Center of Applied Maths of Ecole Polytechnique & Aramis Lab at the Paris Brain Institute.


My goal is to use my mathematical and coding skills to create impact where it matters. Beyond medical application, I love developing tools or sharing my knowledge in applications with motivating & positive impacts : a chatbot for a Social-and-Solidarity company, a web-app to choose and analyze the political parties at the French election, a cashless app for a music festival, teaching at a Data Science Summer School, ... I'm also the Deep Learning Lead Instructor of Data Science traning of Le Wagon, a company which teaches Machine Learning to students within a 9 week intensive program


My growing interest in climate and energy issues led me to be part of the Climate Collage association - also known as the Fresque du Climat.


Scientific Interests & Skills

Applied Maths
Machine Learning Deep Learning Statistical Learning Riemannian Geometry Bayesian Statistics EM-like Algorithms MCMC methods
Coding
C++ Python Git Pytorch React-Native Linux Environnements HTML/CSS JavaScript NodeJS Bootstrap scikit-learn Django Distributed computing
Language
French Ukrainian English Russian

Education

Postdoctoral fellow
Postdoctoral fellow
From 2020
Development of methods to improve clinical trials for neurodegenerative diseases Paris Brain Institute - Inria
Advanced Machine Learning Deep Learning Statistical Learning From Theory to Practice Python Library Clinical Trials
PhD - ICM x CMAP x EDMH
PhD - ICM x CMAP x EDMH
2016 - 2020
Statistical learning of spatio-temporal trajectories based on longitudinal data - application to neurogedenerative diseases. ICM - CMAP - EDMH
Machine Learning Statistical Learning Riemannian Geometry Deep Learning MCMC Methods EM-like algorithms
Master Data Science - Paris Saclay University
Master Data Science - Paris Saclay University
2015 - 2016
Theoretical courses in maths, various machine-learning projects and hackathons-like sessions. Ecole Polytechnique, Université Paris-Saclay, Télécom Paris, ENSAE. Website.
Advanced Machine Learning Convex optimization Deep Learning Statistical Learning Compressed Sensing Hackathons
Master of Science in Engineering - Ecole des Ponts et Chaussées
Master of Science in Engineering - Ecole des Ponts et Chaussées
2012 - 2016
Top engineering school (Grande Ecole) with a strong mathematical track. Website.
Probabilities & Statistics Analysis Operational research Scientific computing Programming C++ Design Thinking
Bachelor of Science in Applied Economics - Dauphine
Bachelor of Science in Applied Economics - Dauphine
2014 - 2015
Applied Economics bachelor - Track for Grand-Ecole students
Macroeconomics Microeconomics Econometrics
Civil Engineering Internship - GeorgiaTech
Civil Engineering Internship - GeorgiaTech
2013 (5 months)
Four months internship in a Civil Engineering Laboratory. Published paper : Comparison between geometrical and dynamic particle packing - presented at a Symposium in London

Civil Engineering Numerical modeling Simulations