Research Interests

Main Interests

My research focuses on technological innovation and identity-based inequality. I see the two as closely connected: innovation moves society forward, but it also reshapes who gets ahead. On the innovation side, I study how institutions, incentives, and new technologies shape what gets invented and how new ideas create value. On the inequality side, I study how identity and social institutions shape who gets to participate, advance, and have influence. Much of my work sits at the intersection of these two areas, asking how technological and institutional change creates new opportunities—and how those opportunities are distributed across people and organizations.

Additional Interests

Beyond these core areas, I am broadly interested in human behavior and social patterns. I am drawn both to ideas that grow out of unresolved questions in the literature and to ideas that come from important real-world phenomena. I think both are essential to good research. My current projects also touch on environmental governance, political and social communication, and urban development.

Thoughts on AI

I was trained as an empirical researcher just as modern AI began to change how research is done, and I think about AI in two ways. First, I believe it will reshape the research process itself, and I am rebuilding my own toolkit around an AI-native research workflow without losing sight of research taste, intellectual depth, and real-world relevance. Second, I study AI as one of the defining technological changes of our time, especially AI for Science—how it changes scientific discovery, the value of expertise, and the organization of inventive work. More broadly, I am interested in what economics and management can contribute to understanding how AI should be organized and governed, and how its technological possibilities can be translated into organizational and social value.

Database

I also spend substantial time building and improving research datasets. One lesson I have learned is that a widely used variable is not necessarily a good measure of the concept we care about. Administrative records, patents, firm disclosures, and other commonly used data are generated through specific legal, technological, and organizational processes, and those processes often shape what ultimately appears in the data. I therefore try to understand how a measure is produced before using it, identify where existing measures combine conceptually different components, and, when possible, reconstruct measures that better capture the underlying behavior. I have developed and continue to expand large-scale datasets on innovation, labor markets, and economic history, and I welcome collaborations that open up new data sources or improve existing measures.

PS: I am currently working with collaborators and student research teams on several of these projects. If you are interested in an RA position or in collaborating on one of the datasets, please send me your CV and a short note about your interests or proposed project.