I am a Machine Learning Engineer focused on applications of AI to science and healthcare. Currently pursuing a Master's degree in Computational Biology and Quantitative Genetics at Harvard University to deepen my understanding of biological systems with a particular interest in genomics.

I see myself as a connector, across disciplines (AI, biology, chemistry), and ecosystems (research, technology, investment, and public health).

I am a proud recipient of the Arthur Sachs Fullbright Scholarship and supported by the Harvard Chan Central Grant.

Prior to Harvard, I received a BSc and MSc (Ecole d'Ingénieur) in Applied Mathematics and Machine Learning from ENSTA and Ecole Normale Superieure Paris-Saclay (MVA master), and worked as a Machine Learning Engineer in Paris. At AQEMIA, where I focused on protein–ligand affinity prediction and led the hit identification team, and at Epigene Labs, where I developed ML models for clinical and transcriptomic data harmonization for precision oncology.

I have a foot in early stage investment where I intervene in due diligences, interviews and sourcing of great science projects. Recently, I was a VC investor at Daphni, where I contributed to the first investments through their newest Blue fund focused on science-backed ventures.

I am also active in scientific communication through the weekly Bits in Bio newsletter.

As I continue to explore the complexity of biological systems, I am guided by the conviction that impactful science depends on focus, funding, and visibility.