Ambitious technology needs an operating system for execution.

I began my career as a software engineer, and I still approach organizations as systems: understanding where information is lost, where decisions stall, where ownership becomes unclear, and where local incentives work against the larger goal.

Over the past two decades, I have worked at the boundaries between disciplines. I helped build Google's early developer ecosystem, led programs spanning Nest's apps, cloud services, and developer platform, and built the technical program organization responsible for aligning hundreds of people across Wear OS, Pixel Watch, and Android Health.

Today, I apply those lessons in AI research for biology. The underlying questions are different from consumer technology: progress is less predictable, the path to impact is longer, and scientific quality cannot be reduced to a ship date. But the leadership challenge is familiar: creating enough structure for teams to move together without constraining the exploration that produces breakthroughs.

My work focuses on three questions:

How should organizations operate when the work is technically uncertain?

What does effective program leadership look like when research, engineering, data, and infrastructure are deeply interdependent?

How can leaders create clarity and accountability without adding unnecessary process?

I believe technical program management is most valuable when it improves the quality of decisions, not merely the visibility of tasks.