I work on the integration of latent variable modeling and statistical learning to advance statistical and psychometric methods addressing practical problems in educational and psychological testing. Here are a few of my current research interests:

• Theory and methods for complex behavioral data (in particular, sequence data, e.g., log data, natural language data) in assessments;
• Latent variable modeling: missing data, response times, diagnostic classification, statistical computing;
• Longitudinal model for learning and interventions.

• PSYC 490 : Measurement and Test Development Lab
• STAT 428: Statistical Computing
• PSYC 593: Statistical Learning for Behavioral Data
• Online workshop on Statistical Learning of Process Data (Video recording)
• Online workshop on R Programming for Data Science

IES R324P210005 (co-PI): Analysis of NAEP Mathematics Process, Outcome, and Survey Data to Understand Test-Taking Behavior and Mathematics Performance of Learners with Disabilities

AERA NSF 112057 (PI): Revision and Review Behavior in Large-Scale Computer-Based Assessments: An Analysis of NAEP Mathematics Process Data 

Alicia Cascallar Award (NCME, 2022)

Excellent Reviewer Award (JEBS, 2021)

UIUC List of Teachers Ranked as Excellent by Students (SP 2021, FA 2022)