I study how measurement theory and psychometrics can guide the assessment of both human (e.g., psychological traits, knowledge and skills) and machine (e.g., AI model capability). My research identifies practical questions in these domains that cannot be adequately addressed with existing statistical tools, and I develop new statistical tools that better address them.

My current research interests include:

• Developing measurement theory and psychometric tools for unstructured test response data (e.g., process data, constructed responses);
• Adapting LLMs to support development of measurement theory-grounded, evidence-centered assessments for learning (e.g., diagnostic assessments, simulation-based tasks);
• Measurement theory and new psychometric tools for AI model evaluation and benchmark design.

• PSYC 490 : Measurement and Test Development Lab
• STAT 428: Statistical Computing
• PSYC 593: Statistical Learning for Behavioral Data
• STAT 410: Statistics and Probability II
• Online workshop on Statistical Learning of Process Data (Video recording)
• Online workshop on R Programming for Data Science

IES R324P230002 (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 

Schmidt Sciences Foundation AI Safety Science Grant (co-PI): Creating Effective Benchmarks for LLMs with Human AI Collaboration

Alicia Cascallar Award (NCME, 2022)

Excellent Reviewer Award (JEBS, 2020, 2023, 2024)

UIUC List of Teachers Ranked as Excellent by Students (SP 2021, FA 2022, FA 2023, SP 2024, SP 2025)

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