My research explores how behavioral science, economics, and artificial intelligence can inform legal design, legal reasoning, and the solution of social problems. I apply empirical, experimental, and computational methods to study the interaction between humans and algorithmic decision-making, the application of market design to legal institutions, and the use of AI (machine learning, large language models) to analyze legal reasoning.

A central strand of my work examines how humans perceive algorithmic decision-making in high-stakes legal contexts. In a series of collaborative projects, I have investigated the human-AI fairness gap—people's tendency to evaluate automated legal proceedings as less procedurally fair than human-led ones—and explored strategies to mitigate it. Related projects investigate how large language models reason about law, their inherent biases in normative contexts, and ways to improve their legal outputs.

A second strand draws on economic theory, observational data, and laboratory experiments to study how allocation mechanisms—from school choice to refugee protection—can be designed to balance efficiency, fairness, and incentive compatibility. My habilitation thesis Mechanical Justice develops a comprehensive framework for the design of public matching markets.

A third strand applies insights from behavioral economics to regulation, democracy, compliance, privacy, and repugnant markets. This work combines game-theoretic modeling with experimental methods to understand how legal rules shape behavior—and how empirical insights can improve institutional design.