I'm a software engineer drawn to operational problems — things that are slow, manual, or breaking under load. Most of my work is the same shape: profile it, find the friction, ship a measured fix. Lately that's pulled me toward AI systems, where the same reliability and latency problems show up but the tooling is younger.

The friction is usually something quiet — a deferred cost being misattributed, a default nobody benchmarked, a tool whose own numbers are wrong. At the Hao AI Lab I work on FastVideo, an open-source video-diffusion framework: I cut Cosmos 2.5 inference latency 15.6% after finding a "slow stage" that was really absorbing a half-gigabyte GPU→CPU transfer, built a model-agnostic caching module that takes 22–47% off Wan2.1, and ported the framework to NVIDIA's DGX Spark. Same instinct outside the lab — a Go log engine doing 42M rows/hour with sub-millisecond lookups, and an ML anomaly-detection pipeline that grades its own output.