Engineering AI
for the
real world.

Engineering judgment.
Applied to AI.

My work began with industrial automation and control systems. Today, I bring that same attention to reliability, failure modes, and real operating conditions to AI and machine learning.

I’ve worked across manufacturing, industrial analytics, research, and AI evaluation. I’m especially interested in the space where software meets sensors, equipment, and the people making operational decisions.

My work in AI for predictive maintenance began in 2007, applying neural networks to fault detection on industrial process data — at a time when almost no one around me believed it was possible. My graduate research on the idea was dismissed without real review. I carried it forward anyway: a decade later, the same concept, rebuilt with modern machine learning, became my published IEEE and Springer research. That arc — from dismissed idea to proven science — is the through line of my career.

I build AI that runs where the work happens: on local devices and plant infrastructure, not in someone else’s cloud. Security isn’t a feature of my systems; it’s the starting requirement.