Qunzhi Xu

About Me

I am a Postdoctoral Associate in the School of Global Public Health at New York University. I received a Ph.D in Industrial Engineering with concentration on Statistics 2024 from Georgia Institute of Technology, advised by Prof. Yajun Mei and Prof. Jianjun Shi. Before that, I received my B.S. in Statistics from the University of Science and Technology of China (USTC) in 2019, advised by Prof. Zemin Zheng.

I am on the 2024-2025 academic job market and would be happy to discuss any opportunities!

Research Interests

I am interested in developing statistical methodology and theory in machine learning and statistics for streaming data, particularly in active/reinforcement/transfer learning and change-point problems, and their application in Engineering and Biomedical Science.

Selected Awards

• Best Poster Award, the 8th Workshop on Biostatistics and Bioinformatics, May 2023.
• Travel Award, Third Annual Data Science Workshop in Augusta University, March 2023.
• Best Poster Presentation and People’ Choice for Best Poster Presentation, Second Annual Data Science Workshop in Augusta University, October 2021.
• M.H. Stewart Fellowship, Georgia Institute of Technology, Fall 2019.
• Guo. M. Scholarship, University of Science and Technology of China, April 2019.

Publications and Preprints

Journal Articles

• Q. Xu, Y. Mei and J. Shi, “Active quickest detection with compensation coefficients,” accepted by Sequential Analysis in December 2024.
• Q. Xu^#, H. Tian^#, A. Sarkar, and Y. Mei, “Rollout designs in lump-sum data,” accepted by Journal of Applied Statistics in November 2024.
• Q. Xu, Y. Mei and G. V. Moustakides, “Optimum multi-stream sequential change-point detection with sampling control”, IEEE Transactions on Information Theory, vol. 67, no. 11, pages 7627–7636, 2021.
• Q. Xu and Y. Mei, “Asymptotic optimality theory for active quickest detection with unknown post-change parameters,” Sequential Analysis, vol. 42, no. 2, pages 150-181, 2023.
• E. Fang^#, Y. Mei^#, Y. Shi^#, Q. Xu^#, and T. Zhao^#, “Pivotal estimation of linear discriminant analysis in high dimensions,” Journal of Machine Learning Research, vol. 24, pages 1-45, 2023.
• O. Canbek, Q. Xu, Y. Mei, N. R. Washburn, and K. E. Kurtis, “Predicting the rheology of limestone calcined clay cements (LC^3): Linking composition and hydration kinetics to yield stress through machine learning,” Cement and Concrete Research, vol.160, 2022.
• Q. Xu and Y. Mei, “Optimal Robust Sequential Tests of Circular Nonconforming Probability,” submitted to Statistica Sinica in October 2024.
• Q.Xu^#, A. Chaudhuri^# and Y. Mei, “Asymptotic optimality of detecting unknown correlation change in Gaussian distribution,” to be submitted to IEEE Transactions on Information Theory in Dec 2024.

Conference Papers

• Q. Xu, Y. Mei, and G. V. Moustakides, “Second-order asymptotically optimal change-point detection algorithm with sampling control,” Proceedings 2020 IEEE International Symposium on Information Theory, page 1136-1140, 2020.

• Q. Xu, and Y. Mei, “Multi-stream quickest detection with unknown post-change parameters under sampling control,” Proceedings 2021 IEEE International Symposium on Information Theory, page 112-117, 2021.

• Q. Xu and Y. Mei, “Active quickest detection when monitoring multi-streams with two affected streams.” Proceedings 2022 IEEE International Symposium on Information Theory, page 1915-1920, 2022.

• Q. Xu, Y. Yu and Y. Mei, “Quickest detection in high-dimensional linear regression models,” Proceedings 2024 IEEE International Symposium on Information Theory, page 1915-1920, 2022.

• Z. Li, Q. Xu, Z. Xu, Y. Mei, T. Zhao and H. Zha, “Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification,” accepted by 2024 International Conference on Machine Learning (ICML).

Teaching Experience

Instructor

• ISyE 3030 Basic Statistical Methods, Georgia Institute of Technology, Summer 2023. (38 students, CIOS score: Inclusive 4.9/5.0, Overall effectiveness 4.6/5.0).

• UGPH-GU 20 001 Biostatistics for Public Health, New York University, Fall 2024.

Teaching Assistant

• Co-lead GTA of ISyE 7406 Data Mining and Statistical Learning for Online Master Analytic Program, with 200-400 students, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Spring 2022.
• GTA of ISyE 6412 Theoretical Statistics, with 30-50 students, Fall 2021, 2020.
• GTA of ISyE 4031 Regression/Forecasting, with 69 students, Summer 2020.
• GTA of ISyE 3232 Stochastic Manufacturing and Service Systems, with 37 students, Spring 2020.
• GTA of ISyE 6805 Reliability Engineering, with 7 students, Fall 2019.

Services

Conference Reviewers

• IEEE International Symposium on Information Theory (ISIT)
• Neural Information Processing Systems (NIPS), 2023
• the International Conference on Machine Learning (ICML)

Journal Reviewers

• Journal of Applied Statistics
• Statistica Sinica
• IEEE Transactions on Information Theory
• Statistics and Computing
• Journal of Biomedical Informatics
• Sequential Analysis