I aim to advance the use of models that capture individual differences in terms of person-specific process model parameters, and apply these models to large-scale (e.g., intensive longitudinal) data. These process models offer a framework for testing substantive theories by proposing concrete mechanisms that underlie observed data. Currently I am focusing on developing dynamic models of well-being. In my lab we are conducting ecological momentary assessment studies to collect intensive longitudinal data on people’s emotional experiences and cognitive evaluations of their daily life. By combining cognitive psychometric techniques and dynamical systems modeling, my goal is to unpack the mechanisms, pathways, and synchronicity dynamics of emotional and cognitive elements of well-being. Moreover, I am interested in studying how these well-being elements synchronize with physiological measures, such as blood volume pulse and electrodermal activity. The goal is to explore how person-specific patterns in these physiological variables can improve understanding of changes in self-reported well-being measures, and provide for deployment of real-time interventions.The complexity of these models requires flexible methods for parameter estimation. Bayesian statistics offers these methods, while also providing a principled framework for statistical inference. Therefore an interwoven theme in my research is promoting Bayesian methods.