AI Medical Imaging

Building clinically deployed AI medical imaging systems.

10 years delivering MRI & CT solutions from research through FDA validation to clinical deployment.

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From research bench to clinical bedside.

I'm an imaging scientist and engineer specializing in AI-driven medical imaging — MRI, CT, segmentation, and biomarker development.

I bridge the gap between research and clinical deployment, with experience in FDA 510(k) validation and production infrastructure.

Principal-level engineer in AI medical imaging
FDA 510(k)-supporting validation experience
Built pipelines for real-world MRI dataset of 20,000+ patients
Production AI deployment (PyTorch, Triton, AWS)
Strong clinical + engineering bridge

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Portfolio

Selected Projects

Validation · Regulatory

FDA-Cleared CTA-Based Plaque Analysis

Impact FDA 510(k) cleared PlaqueIQ software
Highlight Algorithm validation against histology ground truth
Stack Python, C++, Insight Toolkit (ITK), Docker

AI Engineering · Development

AI-Enabled Automatic Coronary Vessel Extraction

Impact Streamlined analysis workflow
Highlight Data curation, model refinement, and post-processing
Stack Python, PyTorch, Triton, HTCondor

Research · Collaborations

Real-World Evidence in Multiple Sclerosis

Impact Enabled research to advance personalized medicine
Highlight Real-world dataset of 20,000+ patients
Stack Python, MySQL, bash, HPC

Clinical Translation · Deployment

Automated Image Analysis in Radiology Workflow

Impact Translation of metrics to monitor disease in routine care
Highlight Tool integration at 3 Multiple Sclerosis clinical centers
Stack Python, C, DICOM, Image Segmentation

Expertise

Core skills across the full clinical AI stack.

AI / ML

PyTorch, nnU-Net, deep learning model development, training pipelines, and post-processing for medical imaging tasks.

Medical Imaging

MRI, CT, segmentation, biomarker quantification, DICOM processing, and clinical validation study design.

Systems & MLOps

Docker, AWS, Triton Inference Server, TensorRT, scalable data pipelines, and production deployment in clinical environments.

Programming

Python (primary), C++ for performance-critical components. Strong emphasis on reproducibility and testable, maintainable code.

Regulatory & Clinical

FDA 510(k) validation workflows, clinical trial support, algorithm validation against clinical ground truth, and regulatory documentation.