Position Title
Statistical modeling of behavioral data
Modeling individuals through time
Dr. Jonathan Park is an assistant professor of quantitative psychology in the Department of Psychology at UC Davis. Formally, his research focuses on how to best handle and model heterogeneity in dynamic networks. People exhibit prototypical behavioral patterns that fluctuate over time, and individual dynamic networks tend to be largely idiosyncratic—very few people change and fluctuate in the same way. This makes identifying key dynamic patterns across individuals quite challenging.
For example, depression may manifest differently across individuals and their patterns of mood and behavior can vary significantly from person to person. By examining these dynamic patterns, we may uncover similarities in how depression unfolds across groups of people. However, different statistical models (e.g., multilevel, person-specific modeling) impose varying constraints on how individuals can differ relative to one another (e.g., continuous versus discrete differences) and whether we conclude whether individuals are similar at all.