Yongdao Zhou

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

Selected published papers：

1. 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

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

3.  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.

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

5. 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.

6. 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.

7. 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.

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

9. 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.

10. 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.

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

12. 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

13. 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.

14. 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

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.

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

17. 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. 

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

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

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