I build scientific software that makes heliophysics data more accessible, interoperable, and ready for discovery. My software work is driven by a simple idea: scientific data should not only be available, but usable. These projects bring together cloud infrastructure, machine learning workflows, browser-based tools, and scientific data systems to reduce barriers between raw data and meaningful analysis.
FitsFlow is a browser-based tool designed to make solar FITS data easier to inspect, organize, and prepare for machine learning workflows. It reflects my broader goal of lowering technical barriers for researchers, students, and future scientific software users working with complex imaging data.
Helio-Lite is an open cloud framework for advancing heliophysics research through flexible, scalable infrastructure. The project emphasizes accessibility, reproducibility, and the idea that scientific environments should be as thoughtfully designed as the science they support.
The Survival Solar Energetic Particle Dataset supports time-to-event analysis of SEP detection and provides a structured data foundation for forecasting studies. I see research datasets as software-adjacent infrastructure: they shape what kinds of questions can be asked, tested, and reproduced.
My software vision extends beyond individual tools. I am interested in building interoperable systems for heliophysics data, including machine-learning-ready file workflows, scalable cloud pipelines, and software that connects raw scientific formats to downstream research use. This includes ongoing work around browser-based scientific tooling, format conversion, and infrastructure for accessible space weather analysis.