Katie (Kathleen) Gates

Katie Gates is an associate professor of Quantitative Psychology  in the Department of Psychology at the University of North Carolina at Chapel Hill. Katie’s work is motivated by problems in analyzing individual-level data. She develops algorithms and programs that may aid researchers in better quantifying behavioral, psychophysiological, and emotional processes across time.  The end goal is to help researchers identify patterns within individuals so we can provide person-specific prevention, treatment, and intervention protocols as well as better understand the varied basic physiological underpinnings for emotions, cognition, and behaviors

Sy-Miin Chow

Sy-Miin Chow is a Professor of Human Development and Family Studies at the Pennsylvania State University. She is an elected fellow of the Alexander von Humboldt Foundation in Germany and a winner of the Cattell Award from the Society for Multivariate Experimental Psychology as well as the Early Career Award from the Psychometric Society. Her work focuses on methodologies for handling intensive longitudinal data, methodological issues that arise in studies of change and human dynamics; and models and approaches for representing the dynamics of emotions, child development and family processes, as well as ways of promoting well-being and risk prevention.

Peter Molenaar

Peter C. M. Molenaar is a Distinguished Professor of Human Development and Family Studies at the Pennsylvania State University. He is a recipient of the Pauline Schmitt Russell Distinguished Research Career Award from the College of Health and Human Development at Penn State, the Aston Gottesman Lecture Award from the University of Virginia, the Sells Award for Distinguished Mulitvariate Research from the Society for Multivariate Experimental Psychology (SMEP), and the Tanaka Award from SMEP in 2017. His work instituted what many characterize as a conceptual and methodological paradigm-shift in the analysis of psychological, social, and behavioral processes from an inter-individual to an intra-individual variation perspective.