Welcome! I am a Research Fellow 2 at the Australian Council for Educational Research (ACER) in Melbourne. I develop statistical methods that make educational and psychological measurement more reliable, valid, and fair, particularly as AI enters the assessment pipeline.
Before joining ACER, I was a Postdoctoral Scholar at the Stanford University Graduate School of Education, where I worked on the LEVANTE Project with Ben Domingue, Mike Frank, and Nilam Ram.
My research bridges methodological innovation with practical assessment design and large-scale data analysis. I specialize in:
- Statistical Psychometrics: multidimensional IRT (mIRT) and Many-Facet Rasch Models (MFRM)
- Bayesian Modeling: longitudinal and latent variable models, including Growth Mixture Modeling (GMM)
- AI-Integrated Measurement: evaluating ML-based scoring of constructed responses and the integration of LLMs into educational assessment
- Large-Scale Assessment: scaling, linking, and technical evaluation for national and international assessment programs
Recent Highlights
- Publication (October 2026): Recovering Accuracy From Miscalibrated Item Banks: Scoring Error Is Fixable, Selection Error Is Not (with Domingue, Ram, & Frank) in Educational and Psychological Measurement · DOI · Interactive demo
- Publication (October 2026): Which Algorithm Hurts Most? Differential Associations of Task Allocation, Scheduling, and Performance Monitoring with Worker Well-Being across 27 EU Countries (with Cheng) in International Archives of Occupational and Environmental Health · DOI · Free full text
- Preprint (2026): Enhancing Routing Fairness in Hybrid Scoring with Group-Conditional Coverage (with Xue & Cheng) · EdArXiv
- Preprint (2026): How Far Can a Language Model Carry a Systematic Review? Preregistered Reliability Evidence from Screening to Measurement-Quality Coding (with Cheng) · MetaArXiv
- Preprint (2026): Beyond Acceptance: A Pre-Attitudinal “Uninformed” Stage in Teachers’ Engagement with AI across 53 Education Systems (with Cheng) · EdArXiv
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