Physician–Radiologist · Computer Science (B.Sc. in progress)

I'm a physician and computer science undergraduate exploring how software engineering and artificial intelligence can advance radiological practice. My radiology experience is hospital-based diagnostic imaging, including emergency/trauma and oncologic care. I have hands-on contributions to DICOM viewers, PACS infrastructure, and exploratory AI tooling for healthcare and medical imaging, and I'm comfortable moving from clinical questions to code and back. My main technical interests include:

• DICOM viewers with 2D/MPR/volumetric rendering and DIMSE/DICOMweb integration
• Generative AI in medicine (LLMs/Agents, local inference, RAG/finetuning)
• Infrastructure for healthcare systems (PACS, NAS, EHR, and other systems)
• AI-assisted software engineering and developer tooling
• Full-stack development tailored to clinical workflows

• Languages: Python · Swift · C++ · Dart · JavaScript · Java
• Frameworks: SwiftUI · Django · Flutter · Metal
• AI/ML: PyTorch · TensorFlow · scikit-learn
• Medical Imaging: GDCM · DCMTK · VTK · ITK · DcmSwift · OsiriX/Horos · 3D Slicer · Orthanc
• Infra / DevOps: Docker · Proxmox · QNAP (& other open source NAS) · Tailscale · Git
• Setup: macOS + Windows, Linux VMs, NAS with Dockerized services
• Experimenting with: CUA (Computer Use Agents), Ollama, ComfyUI

• Local-first imaging platforms and DICOM viewers
• AI applications in medicine
• PACS servers in low-resource environments
• AI-assisted radiological decision-making

Isis DICOM Viewer (Windows, macOS, iOS, iPadOS): built natively in Swift for Apple platforms and developed in Qt/C++ for Windows, providing 2D visualization, MPR, volumetric rendering, PACS/DIMSE support, and ROI measurement tools (closed source).

JFlutter: Cross-platform interactive educational tool to design and simulate automata, regular expressions, formal languages and Turing machines. Mobile‑first, touch‑optimised UI.

Dicom-Tools: Test the major DICOM libraries across C++, Python, Rust, Java, C#, and Javascript using a single command interface, shared samples, and interchangeable backends.

MTK - Metal Toolkit: Modern Swift/Metal toolkit for high-fidelity medical imaging (volume rendering, SceneKit integration, SwiftUI components).

DICOM-Decoder: DICOM Pixel decode & windowing pipeline.

Radiology-Templates: Radiology report templates with Rust/Python tools to convert across DOCX, Markdown, and TXT.

DcmSwift: Swift-native DICOM core (DIMSE + DICOMweb).

TotalSegmentator Horos Plugin: Bringing the modern TotalSegmentator to the open-source Horos Project!

GitHub-Replicant: Fast, async Rust CLI for bulk GitHub repository backup. Sync repositories from users, starred repos, followers, or following accounts. Automatically detects existing repos (clone vs pull).

Code-Scanner: Python/Rust/Shell tools to generate clean text snapshots of your source code tree with one command. Perfect for reviews, audits, and AI prompts.

Orthanc‑PACS: Turn‑key Docker stack for medical imaging. Provides an Orthanc server with SQLite database, the Orthanc Explorer 2 UI, a DICOMweb API and an embedded OHIF viewer. Ships with Python, GDCM and Housekeeper plugins and defaults to enabling storage compression and allowing instance overwrite.

mammography-pipelines-py: Mammography experimentation repo delivering reproducible preprocessing, feature engineering, and modeling steps. Run ResNet50 or EfficientNet B0 extractors for full training loops and density classifiers.

brain‑mri‑pipelines‑py: Experimental framework for Alzheimer’s detection from brain MRI. Features multi‑stream deep learning backbones fused with clinical data, reinforcement‑learning hyperparameter refinement, a Tkinter GUI for slice navigation/segmentation and CLI scripts for reproducible experiments.

Lung‑Nodule‑app: Guideline calculators for Fleischner 2017 and ACR Lung‑RADS v2022. Educational use only.

Rust‑Neural‑Networks and Swift‑Neural‑Networks: Small neural network models in Rust and Swift for MNIST digit classification and XOR tasks. Includes MLP, CNN and single‑head self‑attention architectures, along with Python utilities for visualization and digit recognition.

reports‑to‑llm: Rust tool that converts DOCX/RTF medical reports into clean, UTF‑8‑encoded text optimized for LLM training. Removes RTF commands and XML tags, detects section headings and manages output splitting.

Horos‑Backup‑Script: Python script and macOS LaunchAgent to automate bulk backups of CT/MR studies from Horos.

OsiriX‑Backup‑Plugin: Swift plugin for OsiriX/Horos that automates sending DICOM studies to remote PACS.

WALL-ET: Mobile Bitcoin wallet (WIP).

jff‑to‑tex‑Turing‑Machine‑Diagram‑Converter: Python utility that converts JFLAP Turing machine diagrams into LaTeX/TikZ diagrams.

LaTeX‑Paper‑Template: Ready‑to‑use LaTeX template for SBC articles/monographs with modular chapters, placeholder content and centralized metadata.

Orthanc for QNAP: Custom .qpkg packaging