Professor of Psychology at Arizona State University where he studies multivariate methods for the analysis of change, multiple group and latent class models for understanding divergent developmental processes, categorical data analysis, and machine learning techniques for psychological data. He teaches graduate quantitative courses including Longitudinal Growth Modeling, Machine Learning in Psychology, Structural Equation Modeling, Advanced Categorical Data Analysis, and Intermediate Statistics. Kevin has taught workshops sponsored by the American Psychological Association, Centers for Disease Control and Prevention, Statistical Horizons, Instats, and Stats Camp.

Professor of Communication and of Psychology at Stanford University where he studies the dynamic interplay of psychological and media processes and how they change from moment-to-moment and across the life span. Nilam’s research grows out of a history of studying change. After completing his undergraduate study of economics, he worked as a currency trader, frantically tracking and trying to predict the movement of world markets as they jerked up, down and sideways. Later, he moved on to the study of human movement, kinesiology, and eventually psychological processes - with a specialization in longitudinal research methodology. Generally, Nilam studies how short-term changes (e.g., processes such as learning, information processing, emotion regulation, etc.) develop across the life span, and how longitudinal study designs contribute to generation of new knowledge. Current projects include examinations of age-related change in children’s self- and emotion-regulation; patterns in minute-to-minute and day-to-day progression of adolescents’ and adults’ emotions; and change in contextual influences on well-being during old age. He is developing a variety of study paradigms that use recent developments in data science and the intensive data streams arriving from social media, mobile sensors, and smartphones to study change at multiple time scales.

Clinical Assistant Professor at the University of Illinois Chicago with expertise in longitudinal latent variable models, measurement, statistical programming and lifespan development. His research combines multivariate longitudinal methodology, open-source statistical software, and lifespan development. His methodological work pertains to developing new methods for the study of change and incorporating longitudinal and dynamic information into measurement. Ryne is a developer of OpenMx, an open-source statistical software package for structural equation modeling and general linear algebra. He applies his methodological and statistical research to the study of lifespan development, including work on early childhood behavior and personality in late life.