Physics-Grounded Founders.
Physics-Grounded AI.

Three decades building real operational systems in industrial automation, computer vision, and systems integration. The kind of experience that comes from making things work in the field — not from benchmarks.

The core question driving this work: when a flood affects a dam, which affects a power grid, which affects a city's mobility — why does no single system see that cascade? The answer is architectural. GoatAI is the answer.

Physics-grounded agentic reasoning is not a research hypothesis. It is what you build when you've spent 30 years watching domain specialists miss the system they're embedded in.

AI researcher and systems developer focused on the gap between theoretical AI capability and what actually works in physical environments. The challenge isn't building models — it's building reasoning architectures that respect physical laws, geospatial topology, and real-world constraints simultaneously.

Current focus: physics-AI integration research with leading multiphysics simulation companies, and building the agentic orchestration layer that makes multi-agent reasoning over infrastructure operationally viable.

The question isn't whether agents can reason about floods. It's whether the reasoning is grounded enough in physics to be trusted when it matters.