01 — About
From control theory to generative AI
I'm a machine learning engineer working at the intersection of generative AI and autonomous driving. At Torc Robotics, I build generative models and reinforcement-learning systems for scalable, closed-loop behavior simulation — the kind that lets self-driving trucks train and get evaluated against realistic, diverse traffic before ever touching a real highway.
What I care about most is closing the gap between research and what actually ships. In a fast-moving industry environment, that means taking ideas from papers and prototypes to production pipelines that a real engineering team can rely on — not just publishing a result, but making it fast, robust, and usable under real deadlines.
Before that, I spent three years at Honda Research Institute working on interaction-aware motion planning, multi-robot coordination, and human-AI teaming, and earned a Ph.D. in Mechanical Engineering from the University of Delaware, where I developed decentralized control frameworks for connected and automated vehicles. That range — from optimal control theory to modern generative AI — is the throughline of my work: making autonomous systems that reason well about the other agents around them.
02 — Experience
Where I've built this
03 — Research areas
Fields I've worked across
Research spans these areas — the publications below map onto each one.
04 — Publications
Selected work
Scroll for TL;DRs of representative papers across the fields above. Full list of publications, patents, and awards on the full CV page.
05 — Patents
Filed work
06 — Skills
Tools of the trade
07 — Contact
Let's talk
Open to conversations about generative AI, autonomous systems, and research collaboration.