Hello! I build systems at the intersection of software engineering, applied machine learning, and scientific research; from production codebases to research pipelines to deployed ML demos. This repository is a collection of my projects across computer vision, generative AI, applied statistics, and general software development, reflecting a background that spans neuroscience research, healthcare software, and engineering.
This portfolio isn't one project. It's a record of how I work across different domains.
Area What it demonstrates
- Applied ML / Computer Vision research pipeline: Designing and building a full, reproducible research system end-to-end: signal processing, statistical anomaly detection, a rule-based decision engine, and academic-quality reporting
- Generative AI: Hands-on work with diffusion models and transformer-based pipelines
- ML app deployment: Shipping small, usable interfaces around ML/statistical logic (Gradio, Streamlit, Flask) rather than leaving models in notebooks
- Software engineering fundamentals: Config-driven design, CLI tooling, environment setup automation, structured logging, cross-language work (Python, MATLAB, JavaScript)
- Breadth and adaptability: Game development (Ren'Py, GameMaker), contributing to an existing lab codebase, and picking up new frameworks quickly
Several of these projects were built for a specific academic or event context (a thesis, an outreach demo, a class assignment) rather than as generic showcase pieces. I've kept them close to their original form so the code reflects real constraints and real use, not a cleaned-up portfolio version. This also means some of this work, while authored by me, may be owned by the academic institution it was created during. If you're evaluating this for a specific reason or want more context on any project (design decisions, what I'd do differently now, or how it maps to a specific problem space), I'm happy to walk through it!