About

I grew up on a cattle farm in an Appalachian region of rural Ohio. The work was physical, the community was small, and the path forward was simple: work hard, be useful, take care of people. Those values stuck.

At Brigham Young University, I walked on to the rugby team and was part of a national championship squad. Rugby taught me something medicine later confirmed — that the hardest problems require both individual intensity and deep trust in a team.

I went to medical school to become an orthopaedic surgeon. I wanted to fix things with my hands, to be the person in the room who could make the broken whole again. For years, that was the plan.

Then came Harvard Medical School. During a clinical AI research experience following graduation, I taught myself to code. What started as curiosity became conviction: artificial intelligence would allow me to help patients and cure disease at a scale that surgery never could. Instead of treating one patient at a time, I could build systems that serve millions.

That same year, ChatGPT launched. Watching the world react to what I had been quietly studying confirmed the decision. Medicine would be transformed during my lifetime — and I wanted to be one of the people doing the transforming.

I turned down surgical residency and went to Carnegie Mellon University for computer science, to learn how to build AI that solves disease from first principles. Not to apply existing tools to medicine, but to understand the foundations deeply enough to create new ones.

Along the way, I also published research in theoretical physics — because the desire to understand how things work at a fundamental level has never been confined to one field.

Now I lead Galen Health in San Francisco, where we are building autonomous AI for cancer research. The system runs continuously, learning from biomedical data to build an ever-growing understanding of cancer biology. The goal is a cancer superintelligence — an AI that can answer any question a patient or physician could ask about cancer.

The through-line has always been the same: do as much good as possible. The tools changed — from hands in an operating room to code on a screen — but the purpose never did.