I build data, automation, and applied software systems across Python, Rust, JavaScript, and web technologies.
My current technical focus is practical software: machine learning workflows, environmental and soil data systems, API integrations, reporting tools, and command-line utilities.
- Polymer degradation pathway prediction: a Python machine learning pipeline for molecular descriptor curation, feature engineering, validation, uncertainty analysis, and model selection.
- Vapor: a collaborative Rust game launcher project where I contributed to writing, architecture planning, launcher design, and integration direction.
- Melanoma model capstone: a collaborative EECS 582 machine learning capstone where I served as team lead for artifact coordination, deadline readiness, and supervisor communication.
- Rapid Math: a Rust command-line tool for fast arithmetic practice and systems programming fundamentals.
- Python for data science, automation, integrations, and machine learning
- Rust for command-line tools and systems-oriented programming
- JavaScript, HTML, CSS, and Blazor for web applications
- API design, geospatial data workflows, reporting, validation, and technical documentation
- Private applied integration work involving field-data APIs, GeoJSON processing, reporting workflows, and operational automation
My repositories now use clearer naming and labeling conventions:
portfolio: polished or portfolio-facing technical projectssoil-healthandgeospatial: applied environmental and field-data workmachine-learninganddata-science: notebooks, model pipelines, and analysis projectsrust,python,javascript,c, andcpp: language-specific work
Coursework and early learning repositories are archived/private so the public profile stays focused on stronger technical projects.
The next documentation pass will add deeper formal docs to active projects, including architecture notes, setup guides, examples, screenshots, and testing instructions.