Publications

Statistics and Computer Science Theory

• Corneli, M., Erosheva, E., Qian, X., et al. (2025). A Bayesian approach for clustering and exact finite-sample model selection in longitudinal data mixtures. Computational Statistics, 40, 509–545.
  DOI
• Qian, X., & Xing, Y. Ensuring Calibration Robustness in Split Conformal Prediction Under Adversarial Attacks. Submitted to ICML 2026.
• Qian, X., Cui, Y., Lin, Y., Wu, J., & Xing, Y. Multi-source online conformalized jailbreak detection. Submitted to UAI 2026.

Education

• Shin, N., Xing, Y., Qian, X., & Krajcik, J. (2025). Developing a Generative AI Framework for Analyzing Student Responses to Enhance Classroom Assessments. In ICLS 2025 (pp. 2711–2713).
• Shin, N., Qian, X., Chu, Y., Li, H., Miller, C., Krajcik, J., & Xing, Y. AI in assessment. Paper presented at the Global Initiative in AI and Emerging Technologies in STEM Education. 2025.
• Shin, N., Qian, X., Chu, Y., Li, H., Miller, C., Krajcik, J., & Xing, Y. Multi-agent systems for detecting uncertainty and weaknesses in elementary students' written responses to usable knowledge tasks. Paper presented at the Georgia Conference on AI and Education. 2025.
• Qian, X., Shin, N., Li, H., Chu, Y., Miller, C., Krajcik, J., Tang, J., & Xing, Y. Multi-agent Large Language Model systems for Analyzing Elementary Students' Constructed Response. Accepted to AERA 2026.
• Shin, N., Qian, X., Li, H., Chu, Y., Miller, C., Tang, J., Krajcik, J., & Xing, Y. An AI framework for identifying uncertainty and weaknesses in written responses to usable knowledge tasks. Paper to be presented at the 2026 AERA Annual Meeting.
• Shin, N., Qian, X., Li, H., Chu, Y., Miller, C., Tang, J., Krajcik, J., & Xing, Y. Enhancing scoring accuracy with multi-agent systems: Analyzing elementary students' written responses in science assessment. Poster to be presented at the 2026 NCME Annual Meeting.
• Shin, N., Qian, X., Li, H., Chu, Y., Miller, C., Tang, J., Krajcik, J., & Xing, Y. Leveraging generative AI to detect uncertainty in elementary students' written science responses. Paper to be presented at the 2026 NARST Annual Meeting.
• Shin, N., Qian, X., Li, H., Chu, Y., Miller, C., Krajcik, J., Tang, J., & Xing, Y. A Generative AI Framework for Analyzing Student Responses in Formative Assessments to Provide Targeted Feedback. Submitted.