About Me
Academic ML Researcher and Engineer specializing in bridging Deep Learning research with practical software engineering. Expert in building complex full-stack applications (Python/React) for multimodal medical image analysis.
Featured Engineering Projects
- MSXplain
- Full-stack web application (Python/React) for automatic Multiple Sclerosis lesion segmentation.
- Integrated ORTHANC/OHIF for PACS connectivity and advanced medical visualization workflows.
- GitHub: msxplain-report-viewer
- QuantImage
- Medical ML training platform currently evolving through architectural upgrades and feature development.
- Focused on robust experimentation workflows and scalable engineering patterns for research teams.
- Backend Repository
- Frontend Repository
Selected Publications
- Hereditary project (Briefings in Bioinformatics) — Evaluated and synthesized methodologies of state-of-the-art self-supervised genomic models (DNABert, HyenaDNA, Nucleotide Transformer). Review paper accepted in Briefings in Bioinformatics.
- SPIE Medical Imaging 2024 — A full pipeline to analyze lung histopathology images, combining self-supervised learning and multiple instance learning on 1,000+ Whole Slide Images.
- Autism fMRI Study — Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder.
Tech Stack
- Agentic AI: Claude Code, GitHub Copilot Pro
- Deep Learning: PyTorch, Hugging Face, TensorFlow, MONAI
- Compute: CUDA, Docker, Linux, Multiprocessing
Developer Philosophy
I use agentic AI tooling to accelerate implementation and testing so I can spend more time on high-level architectural problem-solving, experiment design, and delivering reliable research software.