Daniel’s research blends computer science, statistics and probability theory; he studies “probabilistic programming” and develop computational perspectives on fundamental ideas in probability theory and statistics. Daniel is particularly interested in: representation theorems that connect computability, complexity, and probabilistic structures; stochastic processes, the use of recursion to define stochastic processes, and applications to nonparametric Bayesian statistics; and the complexity of probabilistic and statistical inference, especially in the context of probabilistic programming. Ultimately, Daniel is motivated by the long term goal of making lasting contributions to our understanding of complex adaptive systems and especially Artificial Intelligence.