Track record

Experience

My previous experiences span computational neuroscience, machine learning for clinical speech analysis, auditory neuroscience, and data engineering. In each of them the work was the same: start from a real question in a lab, build the model that answers it, build the infrastructure it runs on too.

VITAM / CERVO Research Centre, Université Laval

Quebec City, Canada (remote, based in Paris)

Research Collaborator, Contract

September 2025 – Present

Contract
  • Prototyped a speech-to-image pipeline for schizophrenia research, using self-hosted Whisper, transcript segmentation, and a multimodal model to link segments to images, feeding the lab’s existing analysis framework.
  • Benchmarked multiple architectures on 1000 audio files.
  • Responsible for the hosting side of the experiments: ran every model myself on my own AMD/ROCm GPU workstation with PyTorch.
  • Host a small web server giving the team direct access to the transcription results, speeding up a step that had been done entirely by hand until then.

ENS Paris, Group of Neural Theory

Paris, France

Master Thesis, Computational Neuroscience

October 2025 – June 2026

Internship
  • Developed a Python framework from scratch to model neural population firing dynamics using ODEs.
  • Built a custom fitting algorithm to fit the model to real biological recordings; the core challenge was finding parameters producing a bistable network matching the data in both high and low firing-rate states.

IFC (World Bank Group)

Washington, D.C. (remote, based in Paris)

Data Engineering Contractor

June – July 2025

Contract
  • Built a fault-tolerant integration tool across 4 different APIs, handling format mismatches and data exceptions to compile cross-country crop data for the World Bank.

Institut Pasteur, Institut de l'Audition

Paris, France

Research Intern (M1), Auditory Neuroscience

March 2025 – June 2025

Internship
  • Built a transformer-based model to predict music sequences, replacing the lab’s previous RNN approach, with measured improvement over that baseline.
  • Extracted prediction error as a proxy metric for musical surprise and used it in a GLM to test the link between neural excitation and surprise in music.