I develop methods for educational and psychological measurement.
My work uses 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 →Diagnostic Classification 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 DIF clustering: A graph-theoretic approach to identifying many-group partial measurement invariance
- Using multi-label classification neural networks to detect intersectional DIF with small sample sizes
- Review of cognitive diagnostic models (CDMs): Recent methodological advancements for addressing practical challenges
News
- 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.
- April 2026Presented at NCME in Los Angeles, including an invited session on “Advancing Diagnostic Models for Fair and Insightful Educational Action.”
- 2026“Transitive DIF clustering” published in Psychological Methods.
- 2026InterDIFNet and a review of cognitive diagnostic models published in the British Journal of Mathematical and Statistical Psychology.