My research examines how technology—particularly artificial intelligence—affects labor markets, productivity, inequality, and organizational decision-making. I use tools from economics, statistics, and machine learning to measure real-world impacts, often combining large administrative datasets with novel data sources. Much of my work is motivated by a simple question: how do new technologies change what people do, and who benefits as a result?
Download CVThese publications are particularly relevant to expert witness matters involving platform economics, website blocking damages, AI performance evaluation, and technology-driven market dynamics.
Review of Economic Research on Copyright Issues
Analyzes substitution between piracy and legal streaming after platform shutdown, with heterogeneous adoption effects by income.
Read summaryCommunications of the ACM
Argues that judicial website blocking — already used by Canada, the U.K., much of the E.U., Australia, Brazil, India, and South Korea — is the supply-side enforcement tool best suited to reducing online crime, because it requires neither arrests nor server seizures in foreign jurisdictions.
Read summaryManagement Science
Documents growth gains from API adoption and platform openness, while quantifying associated governance and security tradeoffs.
Read summaryMIS Quarterly
Finds that coordinated blocking of multiple piracy sites can meaningfully shift behavior toward legal consumption channels.
Read summaryQuick-reference summaries for journalists covering AI, labor, and technology policy.
Finding: API adoption drives firm growth by enabling external developers to build on internal capabilities, but introduces governance and security tradeoffs.
Why it matters: Explains the economic logic behind platform openness—and why restricting API access can be anticompetitive.
Finding: Machine learning models trained on satellite imagery can detect conflict-related building destruction at scale.
Why it matters: Won the 2023 BBVA Foundation Award. Enables damage monitoring in conflict zones where ground access is impossible.
Finding: Coordinated blocking of piracy sites shifts consumer behavior toward legal channels.
Why it matters: Provides the empirical foundation for judicial site-blocking policy in the US and internationally.
Review of Economic Research on Copyright Issues
2025
Analyzes substitution between piracy and legal streaming after platform shutdown, with heterogeneous adoption effects by income.
Communications of the ACM
2025
Argues that judicial website blocking — already used by Canada, the U.K., much of the E.U., Australia, Brazil, India, and South Korea — is the supply-side enforcement tool best suited to reducing online crime, because it requires neither arrests nor server seizures in foreign jurisdictions.
Explorations in Economic History
2023
Estimates large historical welfare gains from imported consumption variety and changing food baskets in Europe.
Journal of Economic Behavior & Organization
2022
Links smartphone diffusion and network coverage to measurable increases in accident risk, with policy implications for road safety.
Management Science
2022
Documents growth gains from API adoption and platform openness, while quantifying associated governance and security tradeoffs.
Machine Learning with Applications
2022
Introduces a multi-scale segmentation architecture that improves detection of conflict damage in high-resolution satellite images.
World Bank Economic Review
2022
Uses high-resolution imagery to estimate consumption and poverty with robust out-of-sample performance.
Proceedings of the National Academy of Sciences
2021
Develops machine-learning methods to detect conflict-related infrastructure destruction at scale from satellite imagery.
Information Technology for Development
2021
Shows that open satellite feature sets can improve poverty prediction performance and reduce cost barriers for policy analytics.
MIS Quarterly
2020
Finds that coordinated blocking of multiple piracy sites can meaningfully shift behavior toward legal consumption channels.
IZA World of Labor
2018
Explains how high-frequency, high-volume data and machine learning methods are transforming empirical economics and policy design.
NeurIPS 2017 ML for the Developing World Workshop
2017
Demonstrates how CNN models trained on satellite imagery can estimate poverty distribution with meaningful predictive power in low-data settings.
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