Hi, I'm a PhD student at Meta and ENS, in Paris. My work focuses on two main fields:
My advisors are Jean-Rémi King (Brain & AI team, Meta AI) and Valentin Wyart (LNC2, ENS).
FAIR · Meta AI · 2026
Neuralset is a unified Python package and benchmark for neuro-AI: it standardises datasets, metrics, and baselines so deep-learning models can be directly compared on how well they predict neural activity.
FAIR · Meta AI · 2026
TRIBEv2 extends the TRIBE framework to jointly model fMRI responses across subjects, tasks, and modalities, producing a single foundation brain encoder that transfers across datasets.
arXiv:2508.18226 · 2025
We analyse when and why self-supervised vision models (DINOv3) align with human visual cortex responses, disentangling effects of scale, data, objective, and architecture on brain similarity.
arXiv:2512.01591 · 2025
Across a wide range of LLMs, scaling and in-context conditioning push model representations along a trajectory that matches how the human brain processes the same inputs — suggesting a shared computational axis between LLMs and cortex.
CCN · 2024
Using MEG recordings of subjects listening to continuous speech, we decode a hierarchy of linguistic inferences (phonemes → words → syntax → meaning) from cortical activity, aligned with successive layers of large language models.
Workshop · GenAI
Workshop on generative AI and its connections to brain-inspired modelling. Discussion of how foundation models can be used as computational probes of biological intelligence.
MAIN · Montréal
Talk at the Montréal AI & Neuroscience Conference on aligning large models with brain activity and what it reveals about representations in both systems.
NeurIPS · 2025
Talk at NeurIPS 2025 presenting DINOv3 and the DINO × Brain paper: a study of why and when large vision foundation models align with human visual cortex.
CCN
Contributed talk at CCN on decoding hierarchical inference in the human brain during speech processing, using large language models as computational probes.
I currently collaborate with other teams at Meta, working towards more interpretable models through neuro-inspired methods.
Before, I developed a model to predict the positive impact of neurofeedback on subjects based on their physiological data, for MBT, a neurotech startup.
I have a Master's degree from Mines Paris – PSL in mathematics, computer science, and AI, alongside dual programs at MVA (deep learning) and Cogmaster, ENS (neuroscience modeling). I also spent time in Canada and Switzerland through exchanges and internships at Polytechnique Montréal and EPFL.
During an exhibition about AI and Arts, we developed PERSONA: an RL model that adapts in real time, learning to communicate with humans through facial expressions.