Juan Prieto
Research Assistant Professor at University of North Carolina at Chapel Hill
Pittsboro, North Carolina, United States
Summary
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Senior🎓
Top SchoolJuan Prieto is a Research Assistant Professor and computer vision expert with 12 years building AI-driven medical imaging tools that bridge research and clinical impact. He has led development of patented deep learning models for gestational age estimation integrated into low-cost, battery-powered ultrasound devices to enable novice users to achieve expert-level accuracy in underserved regions. At UNC Chapel Hill he supervises and ships open-source 3D Slicer extensions and end-to-end platforms for neuroimage analysis, tractography classification, and multimodal dental imaging, combining 3D modeling, simulation, and digital-twin approaches. His work emphasizes explainability and practical deployment, including a mobile AI classifier for trachomatous trichiasis now field-tested in Senegal and Ethiopia. Trained with a PhD from INSA Lyon, he pairs advanced shape-analysis and volumetric modeling research with production-grade software engineering and community-focused tooling.
11 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Institut national des Sciences appliquées de LyonBachelor's degree, Computer Science, Bachelor's degree, Computer Science at University of the AndesMaster 2, Informatique graphique et image, Assez Bien, Master 2, Informatique graphique et image, Assez Bien at INSA Lyon
Spanish, English, French
Stackoverflow
816reputation
93kreached
13answers
5questions
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excel
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Github Skills (200)
web-application10
python10
image-processing10
bounding10
distributed-computing10
unc10
deep-learning10
medical-imaging10
scalar10
qt10
computed-tomography10
resampling10
3d-models10
3d-images10
emscripten10
Programming languages (11)
Github contributions (5)
juanprietob/trachoma
Jan 2021 - Apr 2024
Trachoma prediction from natural images
Contributions:5 releases, 1 PR, 19 pushes in 3 years 3 months
predictiondeep-learningcomputer-visionmachine-learningnatural
NIRALUser/NeosegPipeline
Jul 2015 - Mar 2019
This tool allows segmenting neonate brain MRI using a subject-specific atlas. It generates an subject-specific atlas based on an atlas population and some diffusion images of the subject to segment. Then we run a single atlas method with this atlas and obtain some good results, especially for in subcortical midbrain structure.
Contributions:2 releases, 90 commits, 8 PRs in 3 years 8 months
brainsubjectimage-segmentationmethodmri
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