About

I grew up on a cattle farm in Appalachian Ohio. The work was physical, the community was small — the kind of place where you grow up working with your hands and looking out for your neighbors.

Between the farm and college, I spent two years as a missionary in South Korea, walking the streets of Seoul and learning enough Korean to spend my days teaching — and learning — about God. It was about as far from rural Ohio as I could get, and the first time I gave myself fully to something bigger than my own plans. That set a pattern I never really broke.

I came back and went to Brigham Young University, where I walked on to the rugby team. We won a national championship while I was there, still one of the things I'm proudest of.

After college I started medical school. The plan was orthopaedic surgery — I liked the directness of it, the mechanical problems with mechanical solutions, the satisfaction of fixing something with your hands. For years, that was the path I assumed I'd take.

When money got tight in medical school, I started building custom hardwood furniture to get by. The garage operation grew, board by board, into an industrial-grade workshop — the first thing I ever built that was mine.

Those same years, I spent two summers on global-health expeditions, treating patients and assisting in surgeries in sub-Saharan Africa and the Himalayas. This was the medicine I'd wanted to practice all along: caring for people with almost no access to it. But it planted a worry I couldn't talk myself out of — a surgeon can only help the patients who make it to him, and I wasn't sure orthopaedics would ever take me back to the ones who needed help most.

Somewhere in the back half of it, I got quietly hooked on machine learning — nothing to do with what I was training for, just something I kept coming back to. So when a research year came up, I sought out a clinical AI position at Harvard Medical School, mostly to get to the bottom of what had been nagging at me. I taught myself to code to keep up with the engineers around me. About six months in, ChatGPT launched. I hadn't seen it coming, but it confirmed the instinct I'd been following on my own, and what had felt like a private interest suddenly looked like where all of medicine was heading. I didn't know what that meant for me yet; only that the question had stopped being whether AI would change how disease gets treated, and started being who would do that work.

Even then, the plan was still surgery. What changed it was the timing — if AI was going to remake medicine, it would do it over the same thirty years I'd have spent as a surgeon. And it spoke to the worry those summers had planted: a scalpel only ever reaches the patient on the table, but software could reach the ones who'd never get to a surgeon at all. So I had a choice. I could stay on track for a secure, respected career, more money than a kid from an Appalachian cattle farm is ever supposed to walk away from, or I could step off the path and help build it myself. I turned down the surgical residency and enrolled at Carnegie Mellon for computer science, to learn AI from the ground up — deeply enough to build new systems for medicine rather than apply off-the-shelf tools to clinical data. Along the way I published some theoretical physics too, mostly out of curiosity: a different field, but the same pull toward understanding how things actually work.

Now I live in San Francisco, where I'm building Galen Health. We're working toward a virtual cell — a model of the cell's machinery accurate enough to run experiments on: perturb a gene, simulate a drug, watch what happens in silico instead of at the bench. The point isn't to memorize biology but to capture cause and effect, so it can predict what it was never shown. Get that right, and two things open up. You can study disease from first principles, tracing what's actually broken and what would fix it instead of guessing by trial and error. And you can run a single person's biology through it to find the treatment that fits them — then do the same for millions of people, not just the few who make it into a trial. Cancer is where we start, because it's the hardest proving ground there is. It'll take years, and the system is far from finished, but it gets a little smarter every day, and that's the part of the work I find most worth doing.