Academic Background

I hold a PhD in Applied Mathematics from École Polytechnique, where my research focused on probability theory, partial differential equations, and their applications in fields such as micromagnetism and neuroscience. Over eight years of academic experience, I developed strong expertise in stochastic analysis, numerical methods, and mathematical modeling.

A list of publications is available here.

Professional Certifications

Applied Data Science Program
MIT Professional Education (August 2024) - (Download certificate here)

Completed a rigorous 12-week program covering advanced data science techniques, including machine learning, deep learning, recommendation systems, and real-world industry applications. Gained hands-on experience building end-to-end solutions using Python.

IBM Data Science Professional Certificate
IBM Skills Network / Coursera (November 2024) - (Download certificate here)

Mastered essential data science skills through a 10-course track. Highlights include expertise in relational databases (SQL), the CRISP-DM methodology, and creating interactive dashboards with Dash. Additionally, gained practical experience in web scraping using Beautifulsoup and developed geographic visualizations with Folium. Broadened analytical capabilities with exposure to R and RStudio.

Teaching and Mentorship

Throughout my academic career, I have been deeply involved in teaching and mentorship. I supervised master’s theses, conducted graduate-level courses on numerical analysis for stochastic PDEs, and guided undergraduate and graduate students through mathematical research projects. I strive to create an engaging and collaborative learning environment, connecting theoretical knowledge to practical applications.