I am a fifth year PhD candidate in the department of Electrical Engineering and Computer Sciences at UC Berkeley advised by Mike Jordan & Nir Yosef. My research interests lie at the intersection of statistics, computation and modeling.

A significant part of my research is driven by building more statistically accurate and faster machine learning software for analyzing biological data, with a focus on transcriptomics. I am a lead contributor to single-cell variational inference (scvi-tools), a set of tools for fully-probabilistic modeling of scRNA-seq data. To learn more about scVI, check out this Bioinformatics chat episode or this feature in Nature Methods.

Aside from that, I am interested in the broader area of ML + Science. Deep generative models provide an appealing and flexible paradigm for learning distributions, but quite some work is needed to fully exploit them as part of a scientific hypothesis testing pipeline (e.g., interpretability, disentanglement, decision-making).

Previously, I worked on counterfactual inference and offline policy learning methods in collaboration with technology companies. In 2018, I visited Le Song at Ant Financial in Hangzhou. In 2019, I visited Inderjit Dhillon at Amazon in Berkeley.

Before graduate school, I obtained a MSc in applied mathematics from Ecole polytechnique, Palaiseau in 2016. Additionally, I worked as an intern at the Harvard Medical School with Allon Klein in 2016. I was born and grew up in Bedarieux, France.


  • Machine Learning
  • Applied Statistics
  • Computational Biology


  • PhD in Electrical Engineering & Computer Sciences

    University of California, Berkeley

  • MSc in Applied Mathematics, 2016

    Ecole polytechnique, France