Python workflows, reproducible analysis, and public-facing data tools built around real observational problems
Orbital analysis, photometry, geospatial comparison, and searchable scientific data — each showing a different part of the work.
A reproducible pipeline for quantifying and visualizing the risk of close satellite approaches in low Earth orbit using public orbital data. Framed around ~10,000 satellites, focused on extracting operationally meaningful summaries from large trajectory sets.
A modular Python pipeline for extracting relative photometric light curves from ground-based CCD time-series observations, including calibration, image alignment, WCS-based source localization, aperture photometry, and light-curve output.
Compares ground-based sky brightness measurements from a Unihedron SQM-L device against NASA VIIRS Black Marble nighttime radiance data. Interpolates field measurements, maps local variation, and compares on-the-ground darkness to satellite-derived pixels.
A browser-based searchable resource for exploring model parameters related to pollution around white dwarfs. Exposes scientific model choices through a structured interface so users can query and inspect results directly.
The most useful pattern in my work is translating research logic into maintainable code and interfaces that others can actually use.
My GitHub includes repositories for satellite conjunction analysis, time-domain photometry, dark sky comparison work, Stellarium scripts for night-sky demonstrations, and the site itself. It is the best place to see the current shape of my technical work and how I organize projects.