How people navigate complex choices and judge what is good for others; how AI agents align with human welfare and paternalistic judgments.

Industrial organization, market design, and the economics of AI; current work examines AI agents as a new class of economic actors and their implications for organizations, markets, and policy.

Economic theory for market design, with a growing focus on the theoretical foundations of AI — including how users productively interact with AI agents that may be misinformed or misaligned.

Computational social science, with a focus on the intersection of AI and innovation.

How AI agents behave, where they diverge from humans, and how to redesign them to better align with human values.

Uses information retrieval to measure hard-to-capture economic phenomena (climate finance, production networks) and studies how algorithmic systems reshape economic outcomes, with a focus on fairness and financial stability.

Labor economics, organizational economics, and the economics of AI — how generative AI reshapes how firms organize work, set wages, and shape careers in emerging economies. Also works on matching and market design.

Labor economics and industrial organization; currently extracting and analyzing non-wage amenities from online job postings. Previously an Economic Researcher at the Egyptian Competition Authority.

Studies how AI and personalization algorithms reshape consumer attention, search behavior, and competition in digital markets, and the welfare effects of privacy regimes. Runs the Internet Behavior Experiment; also a Visiting Scholar at Stanford Economics.

Industrial organization and public economics, including the economics of platforms, AI, competition, and digital markets.

Applications of AI in developing countries.

Industrial organization, market design, and ML methods — combining the flexibility of ML with the interpretability of structural models, with applications in healthcare.

Human-AI interaction, algorithmic decision-making, and the evaluation of AI agents.

Health systems and health economics, examining how various shocks impact health outcomes and livelihoods. Previously worked at the World Bank and international NGOs advancing health policy.

Intersection of economic history, political economy, and labor economics.

Develops methods for marketing decision-making using AI alignment, causal ML, online experimentation, structural econometrics, reinforcement-learning theory, and AI agents.

Foundations for reliable AI in dynamic social contexts — how uncertainty, incentives, and policy mechanisms interact in real deployments, often motivated by healthcare.

Applied microeconomics and econometrics.

ML and causal inference for social outcomes, including LLMs and large text corpora in empirical economics and unstructured clinical narratives in emergency care.

Macroeconometrics and applied macroeconomics — business cycles, monetary and fiscal policy, and integrating unstructured data and ML into structural macro models.

Econometric theory, applied econometrics, and machine learning.

Macroeconomics, focused on how micro-level heterogeneity shapes monetary and fiscal policy, and the political economy of Europe's economic stagnation.

How technology affects healthcare markets, behavior, and outcomes in low- and middle-income countries, and how AI can be deployed to improve patient outcomes.

Market design and labor economics, focused on the effects of AI on labor markets and online platforms.

Econometric theory for robust causal inference and structural estimation, building on ideas from machine learning, statistical decision theory, and partial identification.

Design and evaluation of algorithmic and AI-supported decision systems under realistic informational constraints, at the intersection of statistics, ML, and economics.

Intersection of generative AI, sequential decision-making, and game theory, with applications to societal decision-making and human-AI alignment.

Large language models in asset pricing and the broader use of AI in finance, including lookahead and extrapolation bias and time-aware pretraining.

Behavioral finance, market microstructure, and information economics, with an AI focus on investor behavior, expectation formation, and AI's impact on investment decisions and belief formation.

Empirical industrial organization and the economics of AI.

Applied microeconomics and data science, combining causal inference with NLP and ML to study media, political economics, culture, and identity.

Microeconomic theory and behavioral models of AI and human-AI relations, including mechanism design to improve the learnability, interpretability, and performance of AI.