Named for the physician who believed in understanding why.
Galen of Pergamon was a 2nd-century physician renowned not just for treating illness, but for relentlessly pursuing the why behind it. That ethos drives everything we build.
Solving cancer demands a new kind of instrument.
Cancer is the most complex disease humanity faces. Not one disease, but hundreds — each with its own molecular logic, its own vulnerabilities, its own evolving resistance. The research landscape is vast and deeply fragmented: millions of papers, thousands of clinical trials, dozens of databases that rarely talk to each other.
Existing tools treat pieces of this puzzle in isolation. A database here, a literature search there, a prediction model trained on a narrow slice. No single system holds the full picture — the molecular biology, the clinical evidence, the causal relationships that connect a mutation to a mechanism to a treatment.
Galen Health exists to build that system. A cancer superintelligence that integrates the world’s oncology knowledge into a single, reasoning engine — and gets it to everyone who needs it.
What we believe
Evidence over authority.
Every claim should be grounded in traceable evidence. Pattern-matched guesses dressed up as answers aren’t good enough when lives are at stake.
Accessible to everyone.
The best cancer intelligence should be available to every patient, every clinician, every researcher — not locked behind institutional walls or paywalls.
Informs, doesn’t replace.
Galen exists to deepen understanding, not to substitute for clinical judgment. The physician’s role is irreplaceable. We make it better informed.
Two paths. One conviction.
A physician who understood the clinical need. An ML engineer who understood the computational path. Both arrived at Carnegie Mellon with the same conviction — that AI could fundamentally change how we fight cancer. They met at a machine learning for healthcare hackathon, and Galen Health became inevitable.
Logan Nye, MD
Physician. Clinical AI researcher at Harvard Medical School. Taught himself to code. Came to Carnegie Mellon for computer science. Realized he could treat one patient at a time — or build the intelligence to help cure cancer. He chose the latter.
Kushagra Agarwal
ML engineer and computational biologist. Carnegie Mellon alumnus. Arrived at the same conclusion from the computational side — that the tools to fight cancer at scale didn’t exist yet, and someone had to build them.