My primary research pertains to the use of latent variable modeling, machine learning, and other quantitative methods to solve practical problems in educational and psychological testing. Here are a few of my current research interests:

• Latent variable modeling: missing data, response times, diagnostic classification, Bayesian estimation;

• Longitudinal models for learning and interventions;

• Analysis of complex data (e.g., log data) in computer-based testing and learning environments.

• STAT 428
• PSYC 490 
• 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)