I am broadly interested in statistical inference, informally defined as the computational process of turning data into statistics, prediction and understanding. I work with richly structured data, such as those extracted from texts, images and other spatiotemporal signals. In recent years I have gravitated toward a field in statistics known as Bayesian nonparametrics, which provides a fertile and powerful mathematical framework for the development of many computational and statistical modeling ideas. The spirit of Bayesian nonparametric statistics is to enable the kind of inferential processes according to which both the statistical modeling and computational complexity may adapt to increasingly large and complex data patterns in a graceful and effective way. In this framework, stochastic processes and random measures, along with latent variable models such as mixture, hierarchical and graphical models figure prominently. My motivation for all this came originally from an interest in machine learning, which continues to be a major source of research interest. A primary focus in my machine learning research is to develop more effective inference algorithms using variational, stochastic and geometric viewpoints.