Building Digital Infrastructure — and Experimenting Inside It

The questions I care about — how platforms attract and govern the people who build on them, how competition and incentives shape innovation, how scientific collaborations form, how knowledge crosses disciplinary frontiers — are difficult to answer with observational data alone. The interesting variation usually does not occur naturally, and where it does, it is rarely clean.

So a large share of my work runs the other way around. In collaboration with industry partners, scientific organizations, and grant-making bodies — and, where needed, by assembling my own engineering and data-science teams — I design and deploy real digital infrastructure, prototype platforms, and scale their adoption in the field, then run explicit experiments that test, measure, and optimize these systems while they operate.

The aim is two-sided: to advance fundamental knowledge, and to improve the systems themselves. Partner organizations get working infrastructure and evidence about what makes it work. The research gets identification that observational data cannot supply.

Beyond the platforms and field sites above, the data behind this work spans Palm Computing, Handango, the Apple App Store, the Apple jailbreak community, Unknown Worlds Entertainment (a games studio), GE, and a university experiential-education platform and organization (Northeastern's engineering co-op).

None of this is solo work. Across London Business School, Harvard and the NBER, and Northeastern, more than sixty research assistants — trained in computer science, software engineering, data science, electrical and computer engineering, economics, and mathematics — have written the code, assembled the datasets, built the platforms, and run the experiments alongside me. Many are now at Amazon, Google, Microsoft, Netflix, Figma, Datadog, and Qualcomm; several hold faculty positions of their own.