基本信息
张三国 男   教授、博导 
电子邮件:sgzhang # ucas.ac.cn
联系电话: 010-88256077
通信地址: 北京市石景山区玉泉路19号(甲)中国科学院大学数学科学学院
邮政编码: 100049

研究领域

高维数据分析;参数与非参数统计;测量误差校正模型;变点分析;机器学习

招生信息

   
招生方向
高维数据分析 ;生物医学统计
应用统计

教育背景

   
教育背景
1998.7~2002.6 理学博士学位 中国科学技术大学
1994.7~1998.6 理学学士学位 中国科学技术大学
2003.7~2004.8 香港中文大学统计学系 博士后
2007.1~2008.8 美国Vanderbilt大学医学中心医学与公众健康研究所和生物统计系

出版信息

部分发表论文:

1) Zhang, S. & Chen, X. Consistency of modified MLE in EV model with replicated observations. Science in China (Series A), 304-310,2001.

2) Zhang, S. & Chen, X. Estimation in the polynomial errors-in-variables model. Science in China (Series A), 1-8, 2002.

3) Zhang, S. & Chen, X. Asymptotic normality of parameters estimation in EV model with replicated observations. Acta Mathematica Scientia (Series B), 107-114, 2002.

4) Zhang, S. & Chen, X. On asymptotic normality of parameters in linear EV model. Chinese Annals of Mathematics (Series B), 495-506, 2002.

5) Liu, J. Zhang, S. & Chen, X. Linear EV model with replicable observed independent variables, Science in China Series A: Mathematics, 752-769, 2006.

6) Zhang, S. & Liao, Y. On some problems of weak consistency of quasi-maximum likelihood estimates in generalized linear models, Science in China Series A: Mathematics, 1287-1296, 2008.

7) Jia, Y., Sun, J., Fan, L., Song, D., Tian, S., Yang, Y., Jia, M., Lu, L., Sun, X. Zhang, S., Kulczycki, A. & Vermund, H. S. Estimates of HIV prevalence in a highly endemic area of China: Dehong Prefecture,Yunnan Province, International Journal of Epidemiology, 1287-1296, 2008.

8) Yu, C., Zhang, S., Zhou, C. & Sile, S. A likelihood test of population Hardy Weinberg Equilibrium for case-control studies. Genetic Epidemiology, 275-280, 2009.

9) Ning, W., Zhang, S. & Yu, C. A Moment-based Test for the Homogeneity in Natural Exponential Family with Quadratic Variance Functions, Statistics and Probability Letters, 828-834, 2009.

10) Ning, W., Gupta, A., K., Yu, C. & Zhang, S., A moment-based test for homogeneity in finite mixture models, Communications in Statistics - Theory and Methods, 1371-1382, 2009. 

11) Zhang, B., Halder, K. S., Zhang, S. & Datta, K. P. Targeting transforming growth factor-beta signaling in liver metastasis of colon cancer. Cancer letters, 114-120, 2009.
12) Wang, G., Zhang, S., Joggerst, S. J., McPherson, J. & Zhao, X. D. Effects of the number and interval of balloon inflations during primary PCI on the extent of myocardial injury in patients with STEMI: Does postconditioning exist in real-world practice?  Journal of Invasive Cardiology, 451-455, 2009.

13) Huang, F., Jiang, Z., Zhang, S. & Gao, S. Reliability evaluation of wireless sensor networks using logistic regression, International Conference on Communications and Mobile Computing, IEEE Computer Society, 334-338, 2010.

14) Wang, S., Zhang, S. & Xue, H. Sieve least squares estimator for partial linear models with current status data, Journal of Systems Science and Complexity, 335-346, 2011.

15) Jiang, J., Zhang, S., Guo, T. Russo’s formula, uniqueness of the infinite cluster, and continuous differentiability of free energy for continuum percolation, Journal of  Applied Probability,  597-610, 2011. 

16) Zhang, S., Liao, Y. & Ning, W. Asymptotic properties of quasi-Maximum likelihood estimates in generalized linear models, Communications in Statistics - Theory and Methods, 4417-4430, 2011.

17) Shi, Y. Li, T. Wang, Y. Gao, Q. Zhang, S. & Li, H. Optical image encryption via ptychography, Optics Letters, 1425-1427, 2013.

18) Liu,J. Chang,N. Zhang,S. & Lei,Z. Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis,International Journal of Communication Systems,28:1884–1897, 2015.

19) Wu,X. Zhang,Q. & Zhang, S. Detecting difference between coeficients in linear model using jackknife empirical likelihood,Journal of Systems Science and Complexity,29:542-556, 2016.

20) Zhang, Q., Zhang, S. Liu, J. Huang, J. & Ma, S. Panelized integrative analysis under the accelerated failure time model, Statistica Sinica, 26:493-508, 2016.

21) Wu,X. Zhang,S. Zhang, Q. & Ma,S. Detecting change point in linear regression using jackknife empirical likelihood,Statistics and its interface,9: 113–122, 2016.

22)Zang, Y. Zhang, S.  Li, Q. Zhang, Q. Jackknife empirical likelihood test for high-dimensional regression coefficients, Computational Statistics & Data Analysis, 94:302–316, 2016.

23) Hu, X., Zhang W, Zhang S, Ma S, & Li. Q. Group-combined p-values with applications to genetic association studies. Bioinformatics, 32, 27372743, 2016.

24) Zang, Y., Zhao, Y., Zhang, Q., Cai, H., Zhang, S. & Ma, S. Identifying Gene-Environment Interactions with a Least Relative Error Approach, Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics: Selected Papers from the 2015 ICSA/Graybill Applied Statistics Symposium, Colorado State University, Fort Collins[M]. Springer, 305-321, 2016.

25) Zang, Y. Zhang, Q., Zhang, S.  Li, Q. & Ma, S. Empirical likelihood test for high dimensional generalized linear models. Invited book chapter. Big and Complex Data Analysis: Statistical Methodologies and Applications, Springer, 29-50, 2017. 

26) Wang, G., Zhang, Q., Zang, Y., Zhang, S. & Ma, S. Identifying gene-environment interactions associated with prognosis using penalized robust regression. Invited book chapter. Big and Complex Data Analysis: Statistical Methodologies and Applications, Springer. 347-367, 2017. 

27) Hu, X., Duan, X., Pan, D., Zhang, S., & Li, Q. A Model-embedded Trend Test with Incorporating Hardy-Weinberg Equilibrium Information. Journal of Systems Science and Complexity, 101-110, 2017.

28) Huang, Y., Zhang, Q., Zhang, S., Huang, J. & Ma, S. Promoting similarity of sparsity structures in intergrative analysis with penalization. Journal of American Statistical Association,  342-350, 2017.

29) Wu,X. Zhang,S. & Zhang,Q. A note on the two sample mean problem based on jackknife empirical likelihood,Communications in Statistics - Theory and Methods,7827-7836, 2017. 

30) Wang, G., Zhang,S., & Dai, P. A Robust image denoising algorithm based on Exponential squared loss and SELO penalty,Acta Mathematicae Applicatae Sinica, English Series,753-770, 2017. 

31) Zang, Y., Zhao, Q., Zhang, Q., Li, Y., Zhang,S. & Ma, S. Inferring gene regulatory relations using high-dimensional robust estimation. Genetic Epidemiology, 437-454, 2017. 

32) Wu, M.,Zang, Y., Zhang,S., Huang, J. & Ma, S. Accommodating missingness in environmental measurements in gene-environment interaction analysis. Genetic Epidemiology, 523-554, 2017. 

33) Fu, S., Zhang, S. & Liu, Y. Adaptively weighted large-margin angle-based classifiers,  Journal of multivariate analysis, 282-299, 2018.

34) Li, J., Zhang, W., Zhang, S. & Li, Q. A theoretic study of a distance-based regression model, Science China Mathematics, 979-998, 2019.

35) Fu, S., He, Q., Zhang, S. & Liu, Y. Robust outcome weighted learning for optimal individualized treatment rules,  Journal of biopharmaceutical statistics, 606-624, 2019.

36) Xue, Y., Wang, J., Ding, J., Zhang, S. & Li, Q. A powerful test for ordinal trait genetic association analysis,  Statistical Applications in Genetics and Molecular Biology, vol. 18, issue 2, 2019.

37) Xue, Y., Ding, J., Wang, J.,Zhang, S. & Pan, D. Two-phase SSU and SKAT in genetic association studies. Journal of Genetics, 99:9, 2020.

38) Zhang, S., Xue, Y., Zhang, Q., Ma, C., Wu, M. & Ma, S. Identification of gene–environment interactions with marginal penalization. Genetic Epidemiology, 44:159–196, 2020.

39) Bu, D., Yang, Q., Meng, Z., Zhang, S. & Li, Q. Truncated tests for combining evidence of summary statistics. Genetic Epidemiology. 44:687–701, 2020.

40) Liu, Y., Zhang, S., Ma, S. & Zhang, Q. Tests for regression coefficients in high dimensional partially linear models. Statistics and Probability Letters, 163: 108772-108777, 2020.

41) Zhang, S., Fan, Y., Zhong, T. & Ma, S. Histopathological imaging features‑ versus molecular measurements‑based cancer prognosis modeling. Scientific Reports, 10:15030-15038, 2020.

42) Sun, X., Zhang, S., Ma, R., Tao, Y., Zhu, Y., Yang, D. & Shi, Y. Natural speckle-based watermarking with random-like illuminated decoding.  Optics Express,  31832-31843, 2020.

43) Ren, M., Zhang, S. & Zhang, Q., Robust high-dimensional regression for data with anomalous responses, Annals of the Institute of Statistical Mathematics, 

 703-736, 2021.

44) Liu, Y., Ren, M., & Zhang, S. Empirical likelihood test for regression coefficients in high dimensional partially linear models. Journal of Systems Science and Complexity, 1135-1155, 2021. 

45) Ren, M., Zhang, S., Zhang, Q. & Ma, S., HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis, Bioinformatics, 3073-3074, 2021.

46) Zhang, S., Hu, X., Luo, Z., Jiang, Yu., Sun, Y. & Ma, S., Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion, Statistics in Medicine, 3915-3936, 2021. 

47) Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S., Hierarchical cancer heterogeneity analysis based on histopathological imaging features, Biometrics, 1579-1591, 2021.

48) Ren, M., Zhang, S., Ma, S. & Zhang, Q., Gene-environment interaction identification via penalized robust divergence, Biometrical Journal, 461-480, 2022.

49) Ren, M., Zhang, S., Zhang, Q. & Ma, S., Gaussian graphical model-based heterogeneity analysis via penalized fusion, Biometrics, 524-535, 2022.

50) Sun, X., Zhang, S., & Shi, Y.  Cryptanalysis of an optical cryptosystem with uncertainty quantification in a probabilistic model. Applied Optics, 5567-5574, 2022.

51) Li, S. , Ren, M., Gan, J., Zhang, S.., Kang, M, Li, H., et.al.  Machine learning to determine risk factors for myopia progression in primary school children: the anyang childhood eye study. Ophthalmology and Therapy, 573-585, 2022.

52) Fan, Y., Zhang, S., & Ma, S.  Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach. Genes, 13(9) 1674, 2022.

53) Bu, D., Zhang, S., & Li, N.  Analyzing Multiple Phenotypes Based on Principal Component Analysis. Acta Mathematicae Applicatae Sinica, English Series,  843-860, 2022.

54) 阮腾飞,张三国,申立勇. 基于比例优势模型的有序数据分类,系统科学与数学,  42(10):2817-2833, 2022.

55) Ren, M., Zhang, S., & Wang, J.  Consistent estimation of the number of communities via regularized network embedding, Biometrics,  https://doi.org/10.1111/biom.13815, 2022.

56) Yang, Y., Zhang, S.,  Local offset point cloud transformer based implicit surface reconstruction, Computer Graphics Forum,  https://doi.org/10.1111/cgf.14660, 2023.

57) Li, X., Zhang, X., He, W., Bu, D., & Zhang, S. Gene expression prediction based on neighbour connection neural network utilizing gene interaction graphs. Plos one, 18(2), e0281286, 2023.

58) Yan, H., Zhang, S., & Ma, S. Hierarchy‐assisted gene expression regulatory network analysis. Statistical Analysis and Data Mining: The ASA Data Science Journal,  https://doi.org/10.1002/sam.11609.




科研活动

主持和参与了多项纵向和横向课题,包括国家自然科学基金青年,面上项目,天元数学与医学交叉重点专项、中国科学院大学校长基金、企业和军队科研项目等。

工作经历

2010.6~ 中国科学院大学 教授
2005.6~2010.6 中国科学院大学 副教授
2002.7~2005.6 中国科学院大学 讲师