My research interests center around the development of novel statistical approaches that aid in the study of complex real-world phenomena and provide new insights into related scientific queries. These problems arise in diverse application areas, including nutritional epidemiology, auditory and vocal communication neuroscience, and disease epidemiology. My research is fundamentally application-driven, intensive collaborations with scientists making up a significant component of my work. My overarching goal is to obtain novel statistical methods that improve results and practice in an initial motivating area while having much broader general utility. I enjoy, in particular, developing sophisticated Bayesian nonparametric methods that accommodate a wide range of data generating processes, adapting to different levels of data complexity and potentially automating various aspects of the analysis, including feature extraction, selection of variables, quantification of model uncertainty and testing hypotheses of interest. My research has resulted in highly efficient Bayesian nonparametric methods for measurement error problems and analyses of structured sequential and longitudinal data. These methods have appealing theoretical properties and practical advantages, exhibiting substantial gains in performance compared with existing approaches under a wide range of scenarios.