Reliability is the through-line.

I've been solving business problems with statistics, data modeling, and machine learning for more than a decade — long before "AI" became a headline. In roles across nuclear energy, advanced materials, and healthcare, I used JMP, Minitab, R, and Python to build the predictive and statistical models that drove real decisions. That foundation is why I treat AI as an engineering and reliability discipline, not a trend.

My career started in places where being wrong was not an option: a DOE-Q nuclear modernization program, an FDA-regulated medical-device line, a Shingo Prize facility. High-reliability, data-driven discipline became second nature. When AI arrived as the defining business technology, I brought that same lens to it — asking not just "can we ship something we can call AI," but "does it actually work, solve a real problem, and hold up in front of a customer."

Today I set AI and data strategy that ties modeling and analytics to measurable results; I build AI governance and reliability into how organizations operate so AI is trustworthy, auditable, and safe at scale; and I lead and grow teams — translating between the boardroom and the engineering floor. I'm technical enough to earn engineers' trust and strategic enough to earn the board's.

Increasingly, I'm also a voice in the field. I authored The Operator's Guide to AI Agents, hold a pending U.S. patent for AI-reliability methods, and teach AI as an adjunct professor at East Tennessee State University. I care about making AI a trusted, value-creating capability across the enterprise — reliably, responsibly, and at scale.

Capabilities

What I bring to the table

Three things I pin above the rest — AI strategy, data & statistical modeling, and AI governance & reliability — built on a deep technical toolkit.