Charlottesville, Virginia, United States
Software engineer driven by complex challenges. I learn quickly and iterate rapidly to deliver innovative, effective solutions.
• Identified and resolved a critical GSOM data pipeline bottleneck, implementing a high-performance solution using the Polars library that cut data processing time by over 90% • Projected to save thousands of hours annually by re-architecting the IBTrACS data pipeline from a legacy IDL process to a scalable Python and PostgreSQL model featuring automated Pydantic validation • Spearheaded AI/ML adoption across NOAA by leading the strategic direction, including Generative AI implementation and model evaluation, of the IPG AI Working Group and coordinating technical presentations and outreach that aligned multiple government and contractor teams on new initiatives • Increased software reliability and development velocity by automating code quality enforcement, integrating a modern static analysis and testing suite into the CI/CD pipeline, preventing potential runtime errors across all scientist-led projects • Elevated the technical proficiency and engineering standards for 40+ colleagues by creating and leading technical workshops and providing mentorship, culminating in selection to present key learnings at the AMS 2026 Conference
• Reduced uncertainty in climate forcing by implementing and evaluating CNN, FNN, and GBDT models, which were trained on hundreds of millions of NASA MODIS satellite data points processed with a custom interpolation and cleaning algorithm • Minimized discrepancies between scientific models and real-world observations by developing large-scale Slurm batch jobs for comprehensive feature engineering, dimensionality reduction (PCA), and feature importance evaluation (SHAP)
• Cut daily build failures by over 40% by automating the code-signing process with Python and Shell scripts on a Jenkins server
• Saved approximately $50,000 and hundreds of hours annually by streamlining the cybersecurity validation process, leveraging the DFMEA lifecycle to identify and automate a critical testing gap
• Optimized RFID anti-collision technology, achieving a 399% improvement in FSA protocol performance • Co-authored and published findings in an IEEE RFID paper