I develop methods for educational and psychological measurement.
My research focuses on the development of statistical and machine learning tools to help tests and surveys measure fairly and accurately, even when data are limited.
Research areas
Integrating Machine Learning and Psychometrics
Bringing neural networks, graph-based methods, and other machine learning tools into psychometric modeling, from detecting item bias to estimating parameters and scoring.
Read more →Cognitive Diagnostic Models
Methodological frameworks that make diagnostic models practical, from design and specification to model fit, so they give reliable, actionable feedback.
Read more →Measurement in Complex Data
Methods for settings that challenge standard models: small samples, many groups, sparse responses, and missing data.
Read more →I also partner with K–12 schools and community organizations to study student belonging and chronic absenteeism.
Featured publications
- Transitive differential item functioning clustering: A graph-theoretic approach to identifying many-group partial measurement invariance
- Using multilabel classification neural network to detect intersectional DIF with small sample sizes
- Review of cognitive diagnostic models (CDMs): Recent methodological advancements for addressing practical challenges
Recent news
- November 2026Received a mini-grant from the Southern Miss University Libraries AI Pro Tool Access Program to study the use of large language models to write psychological survey items.
- July 2026Joined The University of Southern Mississippi as Assistant Professor of Education (RESA).
- June 2026Completed my Ph.D. in Measurement & Statistics at the University of Washington.