Welcome to the Measurement, Analytics, and Psychometrics (MAP) Lab at UW!

Dr. Weicong Lyu (2023-24)

Co-advised by Prof. Gongjun Xu

Dr. Lyu received his Ph.D. in Educational Psychology (Quantitative Methods), M.S. in Computer Science, and M.A. in Mathematics from the University of Wisconsin-Madison. His research interests include item response theory, Bayesian methods and causal inference. He is currently working on variational Bayesian algorithms for item response models.

Ruoyi Zhu

Her research focuses on methodology advances for improving the reliability and fairness of educational assessments. She is primarily interested in developing regularization approaches to detect differential item functioning (DIF). In addition to quantitative methodological research, she also works on applied projects using item response theory (IRT) models in health measurement.

Jiaying Xiao

Her research interests include applying machine learning methods to parameter estimation in the multidimensional item response models (MIRT), extending the Cognitive Diagnostic Model (CDM) for longitudinal settings, and improving item selection criterion for Computerized Adaptive Testing (CAT).

Yale Quan

His research passion lies at the intersection of Applied Statistics, Psychometrics, and Education and is focused on issues of education inequality that exist in higher education, and the use of computer adaptive testing for high-stake assessments.  His primary research interest focuses on the development and interpretation of multidimensional nonlinear latent variable modeling and their applications to Psychometric models. His secondary research interest focuses on the refinement and development of statistical models used to perform nonlinear multidimensional clustering in education data and how those clusters can be used in Item Response Theory models.

He Ren

His research interests focus on combining advanced statistical methods and computer technology with educational measurement models to develop novel methodologies, improving educational quality and equity. He is primarily interested in the Item Response Theory (IRT), Computerized Adaptive Testing (CAT), Differential Item Functioning (DIF), and the application of Machine Learning (ML). Feel free to visit his personal page: www.heren.life</a”>.

Cansu Ayan

Dr. Cansu Ayan, completed her PhD in Ankara University, Turkey in 2018. She is now a visiting scholar at UW for one year. Her general research areas are the evaluation of student success, specifically cognitive diagnostic modeling and applications in classroom assessment and learning. She is also interested in Item Response Theory (IRT) models and Computerized Adaptive Testing (CAT) with applications in general.