Yongdao Zhou

2005.9-2008.6  College of mathematics, Sichuan University, Ph.D

2002.9-2005.6  College of mathematics, Sichuan University, M.E. 
1998.9-2002.6  College of mathematics, Sichuan University, B.E.

2018.5-     School of Statistics and Data Science, Nankai University, Professor

2017.9-2018.4 Institute of Statistics, Nankai University, Professor

2015.9-2017.8 College of Mathematics, Sichuan University, Professor

2010.7-2015.8 College of Mathematics, Sichuan University, Associate Professor

2008.7-2010.6 College of Mathematic, Sichuan University, Lecturer

2020.1-2020.2 Department of Statistics and Applied Probability, National University of Singapore, Visiting scholar

2019.7-2019.8 School of Mathematics, The University of Manchester, UK, Visiting scholar

2018.6-2018.9 Department of Statistics and Actuarial Science, Simon Fraser University, Canada, Visiting scholar

2015.7-2015.8 Academy of Mathematics and System Sciences, Chinese Academy of Sciences,Visiting scholar

2012.2-2013.2 Department of Statistics, University of California, Los Angeles, Visiting scholar

2011.7-2011.8 BNU-HKBU United International College, Visiting scholar

2008.9-2009.8 HKBU-UIC, Joint Institute of Research Studies, Postdoctor

2008.6-2008.8 Department of IMSE, The University of HongKong, Research Assistant

2006.9-2008.6 BNU-HKBU United International College, Teaching Assistant

Yongdao Zhou earned a BS in pure mathematics in 2002 and MS and PhD degrees in statistics in 2005 and 2008, respectively, from Sichuan University, China. He was a postdoctoral fellow at HKBU-UIC Joint Institute of Research Studies. Then, he joined Sichuan University and became a full professor in 2015. In 2017, he joined Nankai University, where he is presently a full professor in statistics. He visited UCLA, the University of Manchester, the National University of Singapore, and Simon Fraser University as a visiting scholar. His research agenda focuses on experimental design and big data analysis. He published over 80 papers, such as in JRSSB, JASA, Biometrika, and IEEE TKDE, as well as 9 monographs and textbooks. His research publications have won two best paper awards.

Experimental design, Algorithm for big data

Postgraduates:

Advanced Statistics I, 2017 fall,

Advanced Mathematical Statistics, 2015 fall, 2017 spring,

            Design of experiments, 2013 spring, 2015 spring, 2017 spring, 2022 fall, 2023 fall, 2024 fall

Introduction to data mining, 2014 spring, 2016 spring

Nonparametric estimation, 2014 spring

Time series analysis, 2018 fall

Statistical Learning, 2019 spring, 2020 spring, 2021 spring

  

Undergraduates:

Calculus, 2009 fall

Design of experiments, 2010 spring, 2016 spring, 2017 spring, 2023 spring

2024 spring

            Mathematical Statistics, 2011 spring, 2020 fall, 2021 fall, 2023 spring, 2024 spring, 2025 spring

Regression Analysis, 2010 fall

Statistics and Probability, 2013 fall, 2014 spring, 2015 spring

Stochastic process, 2009 fall, 2010 fall, 2011 fall

Time series analysis, 2010 spring, 2018 fall

2022.1-2026.12 Research on the sampling techniques and statistical design theories for big data, National Natural Science Foundation of China (12131001), RMB¥2,520,000, Co-investigator

2021.1-2023.12 The national youth talent support program, RMB¥1,700,000, Principle investigator

2019.4-2022.3 The research of methods for choosing best initial parameters in algorithms and sampling methods of big data, National Natural Science Foundation of Tianjin, RMB¥200,000, Principle investigator

2019.1-2022.12 Research on augmented uniform designs, National Natural Science Foundation of China (11871288), RMB¥624,000, Principle investigator

2021.1-2024.12 Research funds of Nankai university, RMB¥1,000,000, Principle investigator

2018.1-2020.12 Research start-up funds of Nankai university, RMB¥500,000, Principle investigator

2015.1-2018.12 Research on experimental design in developing new materials, National Natural Science Foundation of China (11471229), RMB¥620,000, Principle investigator

2013.10-2016.12 “Experimental design in Drug studies”, the Fundamental Research Funds for the Central Universities(2013SCU04A43), RMB¥200,000, Principle investigator

2011.1-2013.12 “Some theoretic problems of uniform design”, National Natural Science Foundation of China (11001186), RMB¥170,000, Principle investigator

2010.1-2010.12 “Rubust efficient experimental designs”, National Natural Science Foundation of China (10926046), RMB¥30,000, Principle investigator

2009.1-2010.10 “Designs balance uniformity and efficiency”, Youth Science Foundation of Sichuan University (2008130), RMB¥20,000, Principle investigator

2011.1-2013.12 “The non-parametric structure and effect analysis of multiple outcome variables”, National Natural Science Foundation of China (11071197), RMB¥310,000, Main investigator

Elected published papers：(all the published papers can see google scholar)

25. Yang J., Xu S., Yang Z., Zhang A. and Zhou Y.(2025) Stable Subsampling under Model  Misspecification and Covariate Shift, ACM Transactions on Knowledge Discovery from Data, online, doi: 10.1145/3769077

24. Liu, Z., Zhou, Y. and Liu, M. Q. (2025). Universally optimal designs for symmetric models in order-of-addition   experiments. Journal of the American Statistical Association, online, doi: 10.1080/01621459.2025.2552515

23. Xu S. and Zhou Y. (2025). Robust control experiments for multivariate tests with covariates and network information. Statistica Sinica, online, doi: 10.5705/ss.202025.0157

22. Cao X., Wang S. and Zhou Y.(2025). Using early rejection Markov chain Monte Carlo and Gaussian processes to accelerate ABC methods, Journal of Computational and Graphical Statistics, 34 (2), 395-408

21. Feng Y. and Zhou Y.(2024). GGD: Grafting Gradient Descent, Journal of Machine Learning Research, 25(316),1-87

20.  Zhang, M. Zhou, Y.D., Zhou Z. and Zhang, A. (2024). Model-free Subsampling Method Based on Uniform Designs, IEEE Transactions on Knowledge and Data Engineering, 36(3):1210-1220.

19. Zhou Z., Yang Z., Zhang A. and Zhou Y.D.(2024). Efficient Model-free Subsampling Method for Massive Data, Technometrics, 66(2): 240-252.

18. Yang L.Q, Zhou Y. D., Fu H., Liu M.Q. and Zheng W. (2024). Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs, Journal of the American Statistical Association, 119 (547), 2294-2304.

17. Zhang X.R., Zhou Y.D., Liu M.Q. and Lin D.K.J (2024). Sequential Good Lattice Point Sets for Computer Experiments, Science China Mathematics, 67(9): 2153-2170.

16. Yi, S.Y., Ju, W., Qin, Y., Luo, X., Liu, L., Zhou Y.D. and Zhang, M. (2024). Redundancy-Free Self-Supervised Relational Learning for Graph Clustering, IEEE Transactions on Neural Networks and Learning Systems, 35(12), 18313-18327.

15.Yi S., Mao Z., Ju W.,Zhou Y., Liu L., Luo X. and Zhang M. (2023). Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts, IEEE Transactions on Big Data, 9(6), 1683-1696.

14.Liu, M., Mee, R. and Zhou Y.(2023). Augmenting Definitive Screening Designs: Going Outside the Box, Journal of Quality Technology, 55(3), 289-301

13. Yi S.Y. and Zhou Y.D. (2023). Model-free global likelihood subsampling for massive data, Statistics and Computing, 33:9, 1-16

12. Yi S.Y., Liu Z., Liu M.Q. and Zhou Y.D. (2023). Global Likelihood Sampler for Multimodal Distributions, Journal of Computational and Graphical Statistics, 32: 927-937.

11. Yang L.Q, Zhou Y.D. and Liu M.Q. (2023). Ordering factorial experiments, Journal of the Royal Statistical Society: Series B., 85(3): 869–885.

10. Zhou Z. and Zhou Y.D. (2023). Optimal row-column designs, Biometrika, 110 (2), 537-549.

9. Yang F., Lin C.D., Zhou Y.D. and He Y. (2023). Doubly Coupled Designs for Computer Experiments with both Qualitative and Quantitative Factors, Statistica Sinica, 33: 1923-1942

8. Chen J.B., Han X., Lin D.K.J, Yang L.Q. and Zhou Y.D.(2023). On Ordering Problems: A Statistical Approach, Statistica Sinica, 33: 1903-1922.

7. Liu, M.M., Mee, R. and Zhou Y.D.(2023). Augmenting Definitive Screening Designs: Going Outside the Box, Journal of Quality Technology, 55(3): 289-301

6. Yi S., Mao Z., Ju W.,Zhou Y., Liu L., Luo X. and Zhang M. (2023). Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts, IEEE Transactions on Big Data, 9(6): 1683-1696.

5. Zhou Y.D. and Tang B.(2019). Column-Orthogonal Strong Orthogonal Arrays of Strength Two Plus and Three Minus, Biometrika, 106, 997-1004.

4. Zhou Y.D. and Xu H. (2017). Composite Designs Based on Orthogonal Arrays and Definitive Screening Designs. Journal of the American Statistical Association, 112：1675-1683. 

3. Zhou Y.D. and Xu. H. (2015). Space-filling properties of good lattice point sets. Biometrika, 102(4): 959–966. 

2. Zhou Y.D. and Xu H. (2014). Space-filling fractional factorial designs. Journal of the American Statistical Association, 109(507):1134-1144.

1. Zhou Y.D., Fang K.T. and Ning J.H. (2013). Mixture discrepancy for quasi-random point sets, Journal of Complexity, 29: 283–301.