Xingyao Xiao | Modeling | Best Researcher Award
Dr. Xingyao Xiao | Stanford University | United States
Dr. Xingyao Xiao is an emerging scholar in quantitative methodology, specializing in Bayesian modeling, psychometrics, and the integration of artificial intelligence into educational research. She is completing her Ph.D. in Social Research Methodologies at the University of California, Berkeley (2020–2025), where she has maintained a perfect 4.00 GPA, building expertise in Bayesian longitudinal and latent variable modeling, growth mixture modeling, multidimensional item response theory (mIRT), and many-facet Rasch modeling (MFRM). Beginning in July 2025, she will serve as a Postdoctoral Scholar with the LEVANTE Project at Stanford University, working with Professors Ben Domingue and Nilam Ram on developmental change, measurement invariance, and advanced psychometric approaches, as part of an international collaboration supported by the Jacobs Foundation. Her prior experiences include serving as Lead Researcher for the Influence Score Chat Project with The Munathara Initiative, where she developed AI-powered metrics to analyze public discourse, and as a Graduate Student Researcher at the BEAR Center at UC Berkeley, where she contributed to innovations in AI integration for scoring and measurement invariance detection. She has also consulted with the Chinese Academy of Sciences on advanced statistical modeling and held research positions at Boston College, the University of Minnesota, and in collaborative educational projects such as the California Computer Science Project. An experienced instructor, Xiao has taught graduate-level courses in hierarchical modeling and quantitative research methods at Berkeley and served as a teaching assistant in statistics at Boston College and the University of Minnesota. Her work has been recognized with fellowships and awards, including a $30,000 Dissertation Completion Fellowship and the Psychometric Society Travel Award. In addition, she contributes to the academic community as an Editorial Board Member of Measurement: Interdisciplinary Research and Perspectives and as a reviewer for major conferences and journals.
Featured Publications
Su, B., Xiao, X., Cheng, Y., Liu, C., & Yang, C. (2025). Trajectories of depressive symptom among college students in China during the COVID‐19 pandemic: Association with suicidal ideation and insomnia symptoms. Suicide and Life-Threatening Behavior.
Xue, M., Liu, Y., Xiao, X., & Wilson, M. (2025). Automatic prompt engineering for automatic scoring. Journal of Educational Measurement.
Xiao, X., Rabe-Hesketh, S., & Skrondal, A. (2025). Bayesian identification and estimation of growth mixture models. Psychometrika.
Ma, J., Shen, Z., Wang, N., Xiao, X., & Zhang, J. (2024). Developmental differences in children’s adaptation to vehicle distance and speed in street-crossing decision-making. Journal of Safety Research.
Xiao, X., Xue, M., & Cheng, Y. (2023). Bayesian partial credit model and its applications in science education. In Advances in Applications of Rasch Measurement in Science Education (pp. XX–XX).
Zhang, J., Liu, F., Chen, Z., Yu, Z., Xiao, X., Shi, L., & Guo, Z. (2023). A multi-level analysis on the causes of train-pedestrian collisions in Southwest China 2011–2020. Accident Analysis & Prevention.
Xiao, X., Cheng, Y., & Kim, J.-M. (2021). Movie title keywords: A text mining and exploratory factor analysis of popular movies in the United States and China. Journal of Risk and Financial Management, 14(2), 68.