My primary research interest lies in studying individual differences from a process modeling perspective. The general goal is to develop and apply state-of-the-art statistical approaches to particular areas of substantive research (in my case emotion and cognition) that would be difficult or impossible to study without novel methods of analysis.
One model of particular interest is the Ornstein-Uhlenbeck process model that describes temporal changes in terms of psychologically meaningful parameters. I am currently developing a multivariate version of the model to capture changes and synchronicity in different aspects of well-being over time.
I implement models in the Bayesian statistical framework. Bayesian methods represent a principled and flexible approach to turn complex theoretical models into viable research tools. I am especially interested in how they can be used for the analysis of continuously streaming data (from social media, health monitors etc.) through sequential updating routines. The goal is to allow continuous inference from multivariate latent variable models on voluminous information streams in a timely manner.