Agents, data,
and software
that ships.

Give it a GitHub issue URL and it gives you back a pull request. A seven-node LangGraph pipeline clones the repo, classifies the issue, plans the fix, writes the code, runs the tests, self-reviews its own diff, and opens the PR — streaming every step to a live trace UI over Server-Sent Events.

Your agent scored 79% this week and 100% last week. Did you break it, or did it have a bad day? AgentGate answers with statistics instead of a guess: paired non-inferiority tests, cluster-robust standard errors, and Benjamini–Hochberg correction across 42 metrics, which cut false regression alarms from about 87% to 5%.

A decoder-only language model written from first principles in PyTorch — byte-level BPE, GQA, RoPE, QK-norm, SwiGLU, and Muon's Newton–Schulz orthogonalization — plus the full efficiency stack needed to actually serve it: distillation, GPTQ quantization, and speculative decoding. No high-level trainer library appears anywhere in the source.

Ranking customers by who is likely to buy puts the people you cannot influence at the top. This pipeline estimates the effect of contacting each person instead — five CATE estimators (S, T, X, and DR learners against a response-model baseline) scored by Qini and validated against synthetic data where every customer's true effect is known.

An initiative I am building with UW Libraries and the Scientific Software Engineering Center. It automatically collects, rights-classifies, and preserves close to 400,000 scholarly articles written by UW researchers in the ResearchWorks repository, designed as a model other universities can adopt. I own the database schema and system architecture.

An event-driven microservices platform so engineering teams never solve the same incident twice. It turns resolved Jira tickets into clean Confluence knowledge-base articles by pulling in the surrounding Slack and Zoom context, removing about 90% of the manual write-up work.