A mix of sports models, developer tools, and research projects that were interesting enough to keep building long after the original excuse ran out.
A live table that re-ranks Premier League teams by expected points instead of the raw standings.
Built to answer a Chelsea-fan question: were the performances actually as bad as the table made them look? It pairs expected goals with a simpler presentation of the underlying model.
An Elo-based ratings model for tennis matches using historical data stretching back to 1980.
Part prediction engine, part historical visualization. The interesting part was not only rating current players, but making era-to-era comparisons legible.
A collaboration with the Cognitive and Neural Dynamics Lab at UC San Diego on how scientific certainty changes as findings move away from primary sources.
The project combined scraping, NLP, and analysis across thousands of scientific articles, press releases, and downstream coverage.
Automation for API Management so Azure Functions and APIs can fit more naturally into a real delivery workflow.
Started from frustration with highly manual management flows and turned into a GitHub Action aimed at making changes reproducible.
A research project on lossy compression for scientific computing, focused on preserving the features that actually matter.
This work explored whether large simulations could shrink dramatically without losing the signal researchers care about, and was presented at SuperComputing 2018.
A sample React application that implements the OAuth2 PKCE flow with Azure Active Directory from scratch.
Useful as a reference implementation for understanding the protocol itself rather than hiding it behind a library.