My interests span the study of intelligence (natural), intelligence (artificial), and intelligence (moral). My goal is to understand how the human mind works with enough precision that it can be implemented on a computer. I also draw on insights from how people learn and think to engineer smarter and more human-like algorithms for artificial intelligence. I primarily research social intelligence where the scale, scope, and sophistication of humans is distinct in the natural world and where even a kindergartener exceeds the social competence of our best AI systems. I build computational cognitive models of social intelligence using tools from Bayesian inference, reinforcement learning, and evolutionary game theory. These models give precise accounts of human social cognition and make fine-grained predictions that I test empirically in multi-agent behavioral experiments.