Psychology via Dynamical Systems and Graph Theory

I develop innovative quantitative methods to model psychological phenomena as dynamic systems, uncovering hidden patterns in human cognition, emotion, and behavior.

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About Me

Jonathan J. Park, PhD

I am an Assistant Professor in Quantitative Psychology at the University of California, Davis. My research focuses on the development of quantitative methods at the intersection of Graph Theory and Dynamical Systems. These interests converge on generating novel approaches for modeling heterogeneous samples as complex, dynamical networks.

Ultimately, I work to create and disseminate methodological tools which grant us the ability to model individuals as complex systems which unfold through time. My works--discussed below--involve algorithmic approaches for reconciling person- from group-level dynamics without placing unnecessary constraints on person-specific models.

Psychological Networks

Development and Refinement of Methods for Psychometics Networks

Dynamical Systems

Development of Novel Approaches for Modeling Heterogeneous Time-Series

Computational Modeling

Extensive Application of Monte Carlo-based Methods for Validation of Novel Algorithms

Research Interests

My research interests can be broadly defined as the intersection of Graph Theory and Dynamical Systems Theory in an area that I refer to as: Dynamic Network Modeling. Below are some broad comments on my work:

Graph Theory and Network Analysis

My current work in this area revolves around addressing fundamental issues in Psychometrics such as:

The mathematical foundations for the [un]importance of central nodes in psychometric networks
Generalizing and identifying the limitations of well-known community detection methods
Expanding community detection approaches to incorporate uncertainty in group-membership

Dynamical Systems Theory

I have several lines of work pertaining to dynamical systems including:

Identification of optimial time-scales in psychological research
Unsupervised classification of heterogeneous time-series via Stochastic Differential Equation (SDE) models
Understanding and formalizing the relations between discrete- and continuous-time models

Featured Publications

Unsupervised Model Construction in Continuous-Time

Structural Equation Modeling: A Multidisciplinary Journal (2024) Park, J. J., Fisher, Z. F., Hunter, M. D., Shenk, C., Russell, M., Molenaar, P. C. M., & Chow, S. M.

An extension of the GIMME algorithm into Stochastic Differential Equation (SDE) models.

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Evaluating Discrete Time Methods for Subgrouping Continuous Processes

Multivariate Behavioral Research (2024) Park, J. J., Fisher, Z. F., Chow, S. M., & Molenaar, P. C. M..

Consequences of subgrouping continuous-time processes with models developed in the discrete-time framework.

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Subgrouping with Chain Graphical VAR Models

Multivariate Behavioral Research (2024) Park, J. J., Chow, S. M., Epskamp, S., & Molenaar, P. C. M..

Adds a subgrouping procedure to the fitting of Graphical VAR models.

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Get In Touch

Connect with me

I welcome collaborations, research discussions, and inquiries from students interested in quantitative psychology research.

University

University of California, Davis

Department of Psychology

Email

imJPark@UCDavis.edu

Location

174K Young Hall

1 Shields Ave., Davis, CA