Projects Portfolio

My expertise lies in the full stack of computational research—from designing numerical algorithms and developing high-performance code (C++/Python/MATLAB) to analyzing statistical data and communicating findings. My work, funded by the National Institutes of Health (NIH) and the French National Center for Scientific Research (CNRS), has involved managing the entire data pipeline—running simulations on HPC clusters, creating data visualizations, and publishing results in LaTeX. I specialize in turning complex problems into validated, published insights.

At Capgemini Engineering, I have applied my research skills and knowledge in physics to contribute to two flagship projects aimed at decarbonizing transportation. For Train Léger Innovant (TELLi)—a proposed battery-powered train for rural France—I developed 3DExperience widgets and integrated Simulink models with Dassault Systèmes' Dymola.

And for Eraole 2—an initiative led by sailor and pilot Raphaël Dinelli—I modeled liquid hydrogen storage tanks and predicted their boil-off rates to help design a fossil fuel-free hybrid aircraft capable of circumnavigating the globe. Across both projects, I applied Model-Based Systems Engineering (MBSE) and Agile methodologies, supported by official Dassault Systèmes certifications.

I am the author of 5 peer-reviewed articles (arXiv, Google Scholar), including 4 as first author. My publications include work designated as an Editor's Suggestion in Physical Review Letters and a recent study in Chaos, Solitons & Fractals where I performed advanced statistical analysis on data from a simulated neural network.

My research draws on principles from statistical physics to model neural network dynamics including critical phase transitions and chaotic states. This approach is pivotal for decoding neural signals with high fidelity, directly informing the development of robust Brain-Computer Interfaces (BCIs) and novel therapeutic strategies for conditions like epilepsy.