Elizabeth Dupre
Postdoctoral Research Fellow at CRIUGM - Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (IUGM)
Montreal, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolElizabeth Dupre is a postdoctoral research fellow with a decade of experience at the intersection of neuroimaging, statistical modeling, and scientific software engineering. She combines rigorous academic training (PhD, McGill; MA/BS, Cornell) with practical contributions to high-profile open-source projects like fMRIPrep, Nipype, Nilearn, and state-space modeling toolkits in JAX, where she has improved workflows, interfaces, documentation, and reproducible demos. Her work spans back-end development, DevOps, and full-stack documentation—evidenced by hands-on commits adding AFNI wrappers, multi-echo EPI support, and tutorial-driven docs for Bayesian state-space models. Based in Montreal and active in SIMEXP, she brings a researcher’s attention to experimental detail with an engineer’s focus on robust, user-friendly pipelines. A less obvious strength is her knack for turning complex neuroimaging methods into clear, testable examples that accelerate adoption across both research and tooling communities.
10 years of coding experience
Master of Arts - MA, Master of Arts - MA at Cornell UniversityDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at McGill University
French, English
Stackoverflow
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Github Skills (41)
nipype10
sphinx-doc10
preprocessing10
python10
jupyter10
image-processing10
sphinx10
preprocess10
pandas10
prepros10
machine-learning10
workflow-engine10
project-structure10
bids10
fn10
Programming languages (16)
Github contributions (5)
nilearn/nilearn
Nov 2017 - Jul 2021
Machine learning for NeuroImaging in Python
Role in this project:
Data Scientist
Data ScientistContributions:85 reviews, 85 commits, 34 PRs in 3 years 8 months
Contributions summary:Elizabeth primarily contributed to example scripts within the `nilearn/nilearn` repository, which focuses on machine learning for neuroimaging. Their contributions involved integrating the Pandas library for handling behavioral data, updating code to improve readability, and patching example scripts to accommodate changes in the library or data formats. They also worked on refactoring existing code and modifying plotting examples demonstrating common data science workflows.
neuroimagingpythondecodingbrain-connectivitymachine-learning
nipreps/fmriprep
Nov 2017 - Aug 2020
fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:124 commits, 23 PRs, 4 pushes in 2 years 8 months
Contributions summary:Elizabeth's contributions primarily involve updating and modifying the core fMRIPrep workflow, with a focus on the multi-echo EPI data processing and integration. The code changes involve improving the handling of different data modalities and adapting existing workflows to accomodate the processing needs of multi-echo data. The user is also responsible for refactoring the main workflow and incorporating the T2* coregistration target, indicating work on improving the pipeline's core functionalities.
neuroimagingdiversetransparentpipelinefmriprep
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