About

Before I was writing Python, I was running structured cable through hospital ceilings and wiring up industrial PLCs — before that, I built, ran, and sold a licensed cannabis delivery business in a state that made you earn every inch of compliance. Thirteen employees, a banking relationship nobody thought a business like that would get, and a decade of learning that the hard problems are never the technical ones — they're the ones where a mistake costs someone real money or their trust in you.

That instinct is what pulled me toward security. I went back for a B.S. in Cybersecurity, picked up CySA+, PenTest+, SSCP, and a stack of other certs, and started asking the same question of every system I touched: what happens when this fails, and who's watching for it. From there, an M.S. in Applied AI was the obvious next step — the systems worth securing now are the ones making decisions on their own.

Most of my career has run on contracts rather than a single employer's org chart. I've built the sensor-testing software the National Weather Service used to validate 89 units for congressional approval. I've spent two years as the lead engineer on a computer vision system that catches $150K a month in medical waste before it happens. I've ranked LLM outputs for RLHF and have plenty of hands-on data experience putting projects together for my master's degree. Right now, I split my time between AI security work and sustainability engineering — designing waste-diversion plans that have kept 72% of material out of landfills across 581 events.

The common thread, if there is one: I go where the failure mode is expensive and figure out how to catch it earlier. 'Security' is just that instinct with a job title.