Research associate, LiGHT
Meditron core team: RL, reasoning, open medical LLMs
I'm a research associate with the LiGHT Lab (Meditron) at EPFL, embedded in the Meditron program end-to-end across training, evaluation, and deployment. I started in 2023 in the sprints that produced early Meditron finetunes. As I took on more of the stack, I was asked to lead sub-projects in Meditron-Reasoning (RLHF + reasoning) and Meditron-Synthetic (safe clinical data generation).
My work focuses on making medical LLMs efficient, interpretable, and auditable: I co-designed evaluation pipelines, led RL and reasoning post-training on HPC, and built Meditree, a multi-agent inference method that mirrors clinical reasoning. Recently, I co-led Apertus-Meditron-FO, a fully open adaptation of the Swiss Apertus models for medicine, including an open medical corpus and fine-tuning of 8B/70B variants.
I hold a Master's degree in Data Science from EPFL (GPA 5.4/6) and a Bachelor's degree in Communication Systems. When I'm not iterating on training runs, I like to indulge in a little bit of running, climbing, random wikipedia articles reading sessions, café hopping (just this year i went from tea afficionado to chai lover to black coffee extremist). I have also been dubbed king of brunch by my friends.
Master thesis
Findings on finetuning open-source LLMs with synthetic and reasoning datasets, reporting 13-30% improvements on reasoning benchmarks and 5-9% on medical benchmarks. Compares distillation with and without reasoning traces, with better results observed without traces in this setting.
Read thesis →AAAI Gen-AI for Health Workshop 2025
Presents Meditree, a clinical reasoning inference method, and results from the open-weight Meditron suite.
Read paper →ACL SIGHUM 2023
Fine-tuned GPT-2 on a corpus of synthetic poems to generate rhyming text with higher consistency than baseline models.
Read paper →December 2024
Invited to the Meta Open Science Innovation Summit to share ongoing work with Llama models in Meditron, an open-source suite of medical large language models developed between Yale and EPFL, and to discuss MOOVE, Meditron's open validation and evaluation framework for reliable global health AI.
October 2024
Joined the Biomedical Informatics & Data Science (BIDS) department as a Short-Term Scholar and Postgraduate Associate to continue core Meditron work across training, evaluation, and publication.
I'm always open to discussing new projects or collaborations. Feel free to reach out via email or connect with me online.