Psychometrics & Responsible AI
PhD Student, University of Toronto
I'm a PhD student in the Department of Applied Psychology and Human Development at the University of Toronto. I work under the supervision of Dr. Feng Ji in the Psychometrics and Responsible AI Lab.
My research harnesses advances in machine learning, statistics, and quantitative methodology to tackle pressing questions in psychological and educational measurement, with a particular emphasis on the convergence of psychometrics and artificial intelligence.
Journal of Educational and Behavioral Statistics · 2026
A hands-on framework for fitting Bayesian IRT models — dichotomous, polytomous, and multidimensional — directly in PyTorch and TensorFlow: accurate, low-bias, and a bridge between modern deep-learning tooling and psychometric modeling.
arXiv preprint · 2026
Robust uncertainty quantification for Bayesian analysis: the infinitesimal jackknife approximates bootstrap standard errors from a single MCMC run — no refits, no analytic derivatives — and, unlike the posterior SD, stays accurate under model misspecification across mediation, ANOVA, and multilevel models.
Journal of Educational and Behavioral Statistics (under revision) · 2025
FedIRT calibrates IRT models across institutions without ever pooling raw responses; a differentially-private extension (FedIRT-DP) adds auditable, student-level (ε, δ) privacy while matching centralized accuracy — released as an open-source R package.