About

I am an economist working at the intersection of market design, causal inference, and policy. My work focuses on how institutions and platforms can use evidence to design incentives, regulate risks, and improve social and economic outcomes.

I trained in engineering, management, and economics (IIT, IIM, UC Berkeley) and began my career as a faculty member at the London School of Economics.

I now apply research in large-scale, real-world settings as a Principal Economist in the tech sector, designing experiments and analyses that inform decisions affecting millions of users and workers. I focus on turning rigorous evidence into actionable strategy and public-impact insights.

How I Think About Evidence

I believe the most useful economics is:

• Empirical – grounded in data and behavior.
• Credibly causal – careful about identification, counterfactuals, and external validity.
• Decision-focused – tied to real choices, trade-offs, and constraints.
• Communicable – explainable to a non-technical decision-maker in a single page or short conversation.

I like problems where there is some friction, ambiguity, or institutional detail to unravel: platform incentives, regulation, safety, fairness, and market design in settings with incomplete information.

Current Interests

• The economics of AI agents and how they change shopping, ads, and platform incentives.
• Market design and auctions in online marketplaces, especially the link between relevance, density, and prices.
• Workplace safety and organizational practices, including how evidence and incentives interact.
• Policy implications of large platforms as they become increasingly AI-mediated.