Bradley Caron

Bradley Alexander Caron, PhD

[email protected] · (317) 345-8879 · Cambridge, MA · LinkedIn · GitHub · GitLab · ORCiD

Technical Skills

AI & Machine Learning

Tools: PyTorch, Tensorflow, Monai, HuggingFace, Tensorboard, Claude, GitHub Copilot, scikit-learn

Methods: Unet, CNN, encoders, SVM, KNN, K-means, Decision Trees, Random Forest, XGBoost

Software Development, Engineering & Cloud Management

Tools: VS Code, Docker, Apptainer, Git, Flask, Django, PostgreSQL, NoSQL, Jira, Trello

Methods: ETL/ELT, CI/CD (Jenkins, GitHub Actions), containerization, server administration

Services: AWS, Azure, high-performance compute (HPC)

Statistics & Bioinformatics

Tools: Python, R, MatLab, Jupyter, RStudio, pandas, polars, numpy, pingouin, statsmodels, tidyverse, ggplot, seaborn, matplotlib

Methods: statistical modeling, data mining, linear/logistic regression, A/B testing, experimental design

Medical Imaging

Tools: FSL FreeSurfer, AFNI, ANTs, MRtrix3, Slicer3D, MIM, nibabel, pydicom, dipy

Methods: data management & organization, MRI/PET-CT preprocessing, segmentation, statistics

Services: flywheel.io, brainlife.io, neurodesk, openneuro, BIDS

Work Experience

Novartis Institute for Biomedical Research

Feb. 2024 – Present

Senior Data Scientist I — Medical Imaging  |  Cambridge, MA

  • Developed, implemented, and maintain company-wide imaging ingestion pipeline (MRI, PET/CT, OCT) replacing a manual process that required weeks per batch; deployed Flask GUI on AWS EC2 using LLM-assisted development (GitHub Copilot) with dedicated DB/logging, reducing ingestion to hours.
  • Adapted and trained UNETR++ (and other) models on 20+ TB of MRI from 100,000+ patients, building foundational segmentation models to accelerate clinical trial image analysis; deployed ML inference pipelines for automated PET/CT labeling and deployed neuroimaging apps to flywheel.io and endpoint platforms.
  • Led experiments leveraging diffusion MRI in diverse neurodegenerative populations and drug trials. Results used in go/no-go decision for diffusion MRI endpoints leading to their inclusion in future drug trials for neurodegenerative disease portfolio; built containerized, advanced diffusion MRI tools for automated processing and analysis; deployed to flywheel.io for wide-scale use by groups across Biomedical Research.

University of Texas, Austin

Feb 2022 – Feb 2024

Postdoctoral Research Scientist, NIH T32 Fellowship  |  Austin, TX

  • Core contributor to brainlife.io (open-source cloud SaaS neuroimaging platform) from inception; built 40+ containerized apps, CI/CD pipelines, and 9 globally adopted tutorials; led user outreach, testing, and platform validation across fMRI, dMRI, sMRI, and EEG.
  • Led experiments replicating brain structure and function relations to age across multiple medical imaging domains (fMRI, dMRI, sMRI, EEG) leveraging open-and-closed source datasets comprising over 3000 unique participants from 30+ researchers on 4 continents; data and code openly released for use by greater scientific community.
  • Developed multi-modal Python pipelines (OCT, structural/diffusion MRI, fMRI) to integrate structure-function measures of the visual system, enabling a novel retinotopic wiring analysis.

Education

Indiana University

Dec 2021

PhD, Neuroscience & Vision Science  |  IU George Rebec Fellowship  |  GPA: 3.8/4.0  |  Bloomington, IN

Dissertation: Developing open science and neuroinformatics tools to support reproducible research in vision and sports-related concussion  |  brainlife.io

Indiana University

May 2015

B.S., Neuroscience  |  GPA: 3.35/4.0  |  Bloomington, IN

Select Publications

*equal contribution