Guido Putignano

I work on where computation can stand in for an experiment that is too slow, too small or too expensive to run.

That has meant mathematical models of engineered cell therapies, in vitro models that treat sex as a biological variable rather than a footnote, and population studies built on hospital administrative data. The through line is the same question each time: what can still be learned when you cannot simply run the study you would like to run.

At the Foundation that question turns into software. An agent is worth building when a clinician, a pharmacist or a discovery scientist already has a problem, owns it, and could not solve it with the time and the data in front of them.