I am a 5th-year PhD student at the University of Michigan School of Information, advised by Eric Gilbert and Ceren Budak. My research focuses on pluralistic human-AI systems, computational social science, and AI alignment.
I design experiments that measure AI's population-scale effects on democratic processes and human cooperation. My work bridges technical AI development with social applications, developing both system-building capabilities and experimental methodologies for measuring collective-level human-AI dynamics.
We introduce an evaluation framework that tests whether LLMs learn fundamental human values or merely surface-level preferences. Across 9 models, the average Deep Value Generalization Rate is just 0.30, meaning all models generalize deep values less than chance.
We introduce Plurals, a system and Python library for pluralistic AI deliberation. Three randomized experiments show simulated focus groups produced output resonant with relevant audiences (chosen over zero-shot generation in 75% of trials). Python Library | Docs
We conducted an experiment (800+ participants, 40+ countries) using a dynamic "many-worlds" design. We find that high AI exposure increased collective diversity but not individual creativity. AI made ideas different, not better.
I create generative art using programming, randomness, and geometry. My work explores the intersection of mathematical patterns and aesthetic expression.