I'm a computational chemist who became a product manager, and spent fifteen years doing it at the frontier of molecular simulation and AI. I grew up in the Pacific Northwest, studied chemistry at Eastern Washington University, and did my Ph.D. at Oregon State applying density functional theory to transition-metal catalysis: understanding how rhodium, copper, and palladium catalysts work at the electronic level, with results published in JACS, ACS Catalysis, and Angewandte Chemie.
During grad school I built Eta_Scripts, an open-source automation framework for transition state searching and reaction coordinate mapping. That project seeded an idea that has shaped everything since: the most leveraged thing you can do isn't to run the simulations yourself, it's to build the tools that let everyone else run them.
I spent nearly a decade at Schrödinger as Technical Product Manager and Principal Scientist, where I built the catalysis market vertical from scratch and launched AutoRW, an automated catalyst screening workflow that democratized a process previously requiring rare specialist expertise. Before AutoRW, fewer than 100 people worldwide could properly execute computational catalyst screening manually; AutoRW enabled 2,000+ screenings per year at 12× the cost efficiency of experimental synthesis, and secured deployments with Fortune 50 clients. I also drove product strategy across the advanced materials portfolio, polymers, semiconductors, organic electronics, including a key R&D collaboration with Panasonic, and supported pharma and biotech clients through the full DMTA cycle in LiveDesign, translating computational capabilities into accelerated small-molecule drug discovery programs.
Now I'm Staff Technical Product Manager at SandboxAQ, defining the product vision for an agentic AI platform that transforms the company's proprietary Large Quantitative Models (LQMs) and specialized datasets into standardized, enterprise-grade API products, the scientific Building Blocks that customers deploy to solve real physical problems in drug and materials discovery. The work spans designing Model Context Protocol (MCP) server roadmaps that position specialized models as domain-specific tools foundational LLMs call dynamically, leading product strategy for drug and materials discovery, and building multi-agent workflows that let medicinal chemists execute complex computational pipelines through natural language. Along the way I shipped an AI-powered invention disclosure workflow that compressed a two-week manual process into a sub-day automated pipeline, and hold two patents pending in AI agent systems, with a third filing imminent.
The through-line is the same problem at every scale: take something hard, expensive, and requiring rare expertise, and build software that puts it in more hands. The science is the entry point; the product is always the goal. Get in touch if you want to talk either one.
Staff Technical Product Manager
SandboxAQTechnical Product Manager · Principal Scientist II
SchrödingerGraduate Research Assistant
Oregon State University · Ph.D. ChemistryThe "Software Moat" is Dry: Why Scientists are the New Engineering Interns
The competitive advantage of scientific software is shifting. When AI can replicate proprietary workflows, the real moat becomes the data underneath them — and the scientists who know how to use it.
Read on LinkedInThe Evolution of a Catalytic Idea: From Academic Concept to Automated Discovery
Tracing the arc from a graduate-school question — can we automate catalyst design? — through a decade of building AutoRW, and into the current age of agentic scientific computing.
Read on LinkedInGo-to-Market for Deep Tech is Different: Here are 4 Principles for Success
The standard go-to-market playbook breaks when the product requires a PhD to evaluate. Four principles for navigating it, drawn from a decade of launching technical platforms for Fortune 50 clients.
Read on LinkedInHigh Throughput Reaction Screening for Accelerated Materials Research
A live demonstration and discussion of automated high-throughput computational screening for catalysis and materials research, presented for the Royal Society of Chemistry's Chemistry World channel.
Watch on YouTubeHow to Adopt the Next-Generation of Materials Screening for Catalysis Discovery: In Silico Design at the Enterprise Scale
A walkthrough of enterprise-scale deployment of automated catalyst screening — from foundational workflow architecture to implementation at Fortune 50 scale — presented as Scientific Lead of Catalysis and Reactivity.
Watch webinarAutomated High-Throughput In Silico Reaction Screening for Design and Discovery of Enhanced Reactivity and Tailored Selectivity
An introduction to the AutoRW automated reaction workflow — covering chemo-, regio-, and stereo-selectivity prediction at high throughput, and the computational framework that makes it possible — presented as Principal Scientist.
Watch webinar