Building AI systems that are measurable, reliable, and ready to ship.

I am an AI Systems / ML Engineer focused on LLM systems, generative models, multimodal perception, and robot-learning data infrastructure. I like problems where research ideas have to survive real constraints: limited compute, noisy data, latency budgets, reproducibility requirements, and production operations.

My current work spans production AI infrastructure at Abundant and VLA / robot-learning research at Carnegie Mellon, where I am building reliable data pipelines for manipulation datasets. That combination keeps my focus on AI systems that are not only interesting, but measurable, debuggable, and useful.

Alongside AI research and systems work, I have built full-stack products across mobile, backend services, infrastructure, payments, media pipelines, and admin operations. That product background shapes how I design AI systems: practical, maintainable, and ready to ship.

My research path also includes EEG and wearable-sensing work at Khan Lab at USC, where I built experiment software, firmware support, data-processing pipelines, and machine-learning classifiers for cognitive and affective-state analysis.