RGHTEOUS GAMBT

That tension is the thread connecting everything I do. As CTO of Polaris EcoSystems, I lead technology for a professional services firm that works in tribal energy, conservation planning, and infrastructure. That work led us to build custom computer vision tools for infrastructure inspection and time-lapse analysis, which led to 3D reconstruction, which led to prototyping our own sensor hardware, which led to learning metrology. Each step pulled me deeper into the same question: when does a model's output become a measurement you can defend?

Monocular depth estimation, SLAM, and multi-view reconstruction produce geometry that looks convincing but lacks the calibrated uncertainty, sensor traceability, and error characterization that metrology requires. I believe closing that gap will unlock new frontiers in computer perception, and by extension, in what AI-based systems are capable of when they have to be right, not just plausible. I lay out the research direction in Your Depth Map Is Not a Measurement and the epistemological scaffolding in The State Space of Research.

Cyber risk assessment, where the tools are large language models and reasoning systems, and physical infrastructure assessment, where the tools are computer vision and 3D reconstruction. The common problem is the same: how do you trust an automated system's judgment when the cost of being wrong is high? I also founded TrainGRC, a platform for continuing professional education and certification training for governance, risk, and compliance professionals. Previously I was Associate Director of the Center for Internal Auditing and Cybersecurity Risk Management in LSU's E.J. Ourso College of Business, teaching internal audit, cybersecurity risk management, AI, and ESG to undergraduate and graduate students.

I built Quiet Riot, an open-source cloud security research tool featured in TL;DR Sec and cloudseclist and adopted by several closed-source security platforms. I'm also a contributor to Hacking the Cloud, the community knowledge base for offensive cloud security techniques. Tooling that practitioners actually use keeps the academic work grounded in operational reality.

Auditors, engineers, attorneys, physicians, board members. Professionals who carry liability for their decisions and need to evaluate AI systems without treating abstract model scores as a substitute for accountability. That's the audience for Practical AI for Professionals: Understanding, Using, and Surviving AI (Chapman & Hall / CRC Press, 2026), which I coauthored with William Yarberry.