My work integrates latent variable modeling with statistical learning to advance statistical and psychometric methods, to address 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, 2020, 2023)

UIUC List of Teachers Ranked as Excellent by Students (SP 2021, FA 2022, FA 2023, SP 2024)

UIUC LAS Lincoln Excellence for Assistant Professors (LEAP) Scholar (2024 - 2026)