Available for scientific consulting
Process Modeling & Analytics · Merck
Kevin E.
Stone
More than a decade of process development across small molecules and biologics — bridging data-rich lab work and in-silico modeling to make development faster, cleaner, and more certain.
Turning hard development problems into models a team can reason about.
Technology enthusiast and inspired team leader, bringing more than a decade of accomplished process development experience across small molecules and biologics. Dedicated to advancing the use of both in-lab, data-rich tools and in-silico modeling to accelerate and enhance process research and commercialization.
My work spans reaction engineering, high-throughput experimentation, and the AI tools — Bayesian optimization, hybrid and Neural ODE models — that turn laboratory data into better decisions. I care as much about adoption and mentoring as about the algorithms: a capability only one person can run isn't really a capability.
- P.01
High-Throughput Algorithmic Optimization of In Vitro Transcription for SARS-CoV-2 mRNA Vaccine Production
Bayesian optimization of 11 IVT process parameters in 42 reactions across 5 rounds; 12% yield gain, 50% faster, up to 44% reagent reduction.
- P.02
Electrochemical Recycling of Adenosine Triphosphate in Biocatalytic Reaction Cascades
- P.03
A Kinase-cGAS Cascade to Synthesize a Therapeutic STING Activator
Diastereoselective one-pot enzymatic cascade to MK-1454, a clinical STING agonist.
Building a modeling capability, or stuck on an optimization?
I help pharmaceutical and chemical teams make better development decisions with data — from mechanistic and hybrid modeling to AI-driven experiment design.