I participate in the development of high-impact open-source software across Python, Go, Java, and JavaScript/TypeScript, with merged contributions now reflected in the upstream contributor histories. My work ranges from preserving negative numeric values in node-csv — a 4.2k+ star CSV library used against large datasets — and restoring Windows release behavior in yarr, a 4k+ star RSS reader, to release-metadata validation in connect-py, deterministic HTTP API tests in mcpproxy-go, keyboard navigation in runs-on.dev, S3 pagination in Taskuary, scan-compatible minified builds in Perch, stale-highlight cleanup in Pelton, and other focused fixes that improve correctness, portability, and day-to-day developer experience. In compact codebases such as Perch, I have taken a core role in shaping the implementation; across larger ecosystems such as node-csv, yarr, and Connect, I contribute as a key upstream engineer.
My research focuses on making coding agents more capable, efficient, and measurable. As the first author of SWE-Explore, I work on trajectory-based evaluation of how agents explore large repositories and localize the code that matters. As a key contributor to SWE-Pruner Pro, I help develop an in-agent context-pruning system that reads signals from the coding model itself to preserve useful tool-response structure while reducing long-horizon context overhead; the project is accompanied by arXiv:2607.18213. Together, these projects connect empirical agent evaluation with practical systems research for the next generation of software engineering agents.