I build computational models of science and discovery, which help us understand how humans learn and how we can create better artificial intelligence (AI). I use tools from probability theory, statistics, machine learning, and program synthesis to model how people explore, learn about, and intervene in novel scientific environments, which I test using large online crowdsourced experiments. I then use the (meta)scientific insights from these models to improve our algorithms for conducting science autonomously.