Biography

Every cell in our bodies tells a story — I develop the machine learning tools that help scientists read millions of such stories at once. I lead the Biological Machine Learning group and serve as an Assistant Professor of Computer Science and Biology at New York University. My research develops probabilistic machine learning methods to uncover the biological mechanisms that govern cellular behavior and disease.

Before 2025, I was a Postdoctoral Fellow at Genentech and Stanford Medicine, hosted by Jonathan Pritchard and Aviv Regev. I received my PhD in Computer Science from UC Berkeley with Mike Jordan and Nir Yosef, and my MSc from École polytechnique. I grew up in Bédarieux, France.

My work pioneered deep generative modeling approaches for single-cell analysis with scVI and scvi-tools, now widely used for modeling cellular heterogeneity. More recently, my group has been extending these approaches to predict how cells respond to perturbations using flow matching and optimal transport, and developing causal machine learning methods for high-dimensional biological data. In parallel, we use these tools to study spatial organization in tumor microenvironments (DestVI) and cellular response to drugs and genetic perturbations.

Join us if you’re interested in machine learning research for biology.