As we get closer to CRESSTCON’18, we are so excited to see our breakout sessions take form! This week we highlight the breakout session, Innovations in Statistical Methodology and Psychometrics. In this session, the presenters will cover recent developments in latent variable modeling, multilevel modeling, and computational approaches to the analysis of data from educational assessment and evaluation studies.  Topics include multidimensional item response theory, Bayesian hierarchical models, and deep learning. Please join us at CRESSTCON’18 on October 1-2 to hear these groundbreaking presenters.

Li Cai

Li Cai is a Professor of Education and Psychology at UCLA as well as the Director of CRESST. His research agenda involves the development, integration, and evaluation of innovative latent variable models that have wide-ranging applications in assessment research in educational, psychological, and health-related domains of study.

Minjeong Jeon

Minjeong Jeon is an Assistant Professor of Advanced Quantitative Methods in the Graduate School of Education and Information Studies at UCLA. Prior to coming to UCLA, she was an Assistant Professor of Quantitative Psychology at Ohio State University. She obtained her PhD in Quantitative Methods and Evaluation and MA in Statistics from UC Berkeley.

Her research interests include developing, applying, and estimating a variety of statistical/latent variable models, such as multilevel models, structural equation models, item response theory models, and growth models. She is also interested in developing computational algorithms and software. Her recent interests include item response tree/process models, network analysis, and joint modeling of multivariate data (such as behavior, cognitive, and neural data).

Kilchan Choi

Kilchan Choi is the Associate Director of Statistics and Methodology at CRESST. His expertise is in the development of advanced statistical methodologies including latent variable hierarchical models, Bayesian analysis, and latent variable measurement models with hierarchical data and applications in large-scale assessment, multisite evaluation, growth modeling, and the effectiveness/accountability of schools. He has developed latent variable regression approaches in modern psychometric models and hierarchical models. His current research focuses on integrating item response theory models, latent variable regressions, longitudinal analysis, and hierarchical models into a general comprehensive statistical model.

Ying Nian Wu

Ying Nian Wu received his PhD in Statistics from Harvard in 1996. He was an assistant professor from 1997 to 1999 in the Department of Statistics at the University of Michigan. He joined UCLA in 1999, and is currently a professor in the Department of Statistics at UCLA. His research interests include statistical modeling, computing, and learning, with applications in computer vision.