Who's behind MechAI
From melting hardware to models that have to be trusted.
A short version of the path from physics and jet-engine heat transfer to production AI — and the principles I carry from one to the other.
An engineer who learned to teach machines.
I'm a PhD physicist (physics of semiconductors) with an engineering background from GE Aerospace, where I led heat-transfer and secondary-flow design on the GE Catalyst, ATP, and CF6 programs — environments where a wrong assumption melts hardware, not a unit test. That respect for physical limits never left me.
Today I bridge complex physical systems and production-grade AI: predictive maintenance, digital twins, time-series forecasting, and production-ready RAG and agentic systems — turning domain expertise into scalable, explainable solutions for industrial environments at STX Next.
MechAI is my own practice for engineering-aware analytics — Oil & Gas reliability, Remaining Useful Life, digital twins, and forecastability triage. I'm the author of dependence-forecastability, an open-source toolkit for telling whether a time series is even worth forecasting before the expensive modeling begins.
View full CV→Operating principles
- Physics firstConstraints are features, not obstacles.
- Ship the loopA model in production beats a notebook PB.
- Explainable by designIf an engineer can't trust it, it won't run.
- Measure the real costLatency, energy, and failure modes count.