Kevin E. Stone

I am a scientific leader working at the intersection of life sciences and AI. Today 13 machine learning scientists, data engineers, and chemical engineers report to me, building the models, agent systems, and data products that biologics development runs on. I write my own algorithms: a neural-ODE architecture for mammalian cell culture, an open-source Bayesian optimization library now behind 160 users, and the chemical representation learning that lets optimization search catalyst and solvent space directly.

Before that I spent most of a decade as a reaction engineer on small-molecule programs, from pre-clinical development through filing and launch, contributing to eight approved medicines. Almost everything I know about machine learning I taught myself. What I have learned since is that adoption is the hard part, not the algorithm: the connectors that make lab data readable, and the evaluation that catches an answer which only looks right, matter more than the model.