Pierre Boyeau
Postdoctoral Fellow In Machine Learning And Computational Biology
Cambridge, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolPierre Boyeau is a computational biologist and machine learning researcher with nine years of experience building interpretable, statistically rigorous models for single-cell and multi-omic data. Currently a Postdoctoral Fellow at the Eric and Wendy Schmidt Center (Broad Institute), he develops frameworks that turn black-box foundation models into tools for formal scientific inference. His PhD work at UC Berkeley produced methods like lvm-DE and MRVI—published in PNAS and Nature Methods—and practical, open-source implementations in the widely used scvi-tools ecosystem. Pierre combines deep probabilistic modeling with careful methodological validation on large-scale datasets, and has a track record of shipping reproducible software and synthetic-data techniques that improve model evaluation in low-sample regimes. Trained in rigorous mathematics and EECS (ENS, École des Ponts, UC Berkeley), he brings both theoretical depth and hands-on engineering to bridge ML and experimental biology.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Electrical Engineering & Computer Sciences, Doctor of Philosophy - PhD, Electrical Engineering & Computer Sciences at UC Berkeley Electrical Engineering & Computer Sciences (EECS)Master's degree (Double Degree), Mathematics / Computer Vision / Machine Learning (MVA), Master's degree (Double Degree), Mathematics / Computer Vision / Machine Learning (MVA) at Ecole normale supérieureMathématiques et Physique, Mathématiques et Physique at Aux Lazaristes La Salle LyonMaster of Science - MS, Computational and Applied Mathematics, Master of Science - MS, Computational and Applied Mathematics at École des Ponts ParisTech
French, English, Spanish
Github Skills (11)
variational-autoencoder10
machine-learning10
deep-learning10
python10
sc9
data-analysis9
variational-inference9
rna-seq9
pytorch9
seq9
testing8
Programming languages (7)
Github contributions (5)
scverse/scvi-tools
Apr 2019 - May 2022
Deep probabilistic analysis of single-cell and spatial omics data
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:58 reviews, 365 commits, 69 PRs in 3 years 2 months
Contributions summary:Pierre's contributions primarily focused on the development and testing of a synthetic correlated class dataset, implementing new features such as a confusion matrix setup, and refining existing synthetic classes. The user also made contributions to the core scvi-tools codebase by addressing a filtering operation. Code changes also include improvements to the handling of distributions and the implementation of a posterior predictive model.
hierarchicaldeep-generative-modelmixture-of-expertssingle-cell-rna-seqsingle-cell
PierreBoyeau/decision-making-vaes
Feb 2020 - Nov 2020
Contributions:19 commits, 4 PRs, 23 pushes in 9 months
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