电子邮件： sgzhang@ucas.ac.cn
通信地址： 科研楼429
邮政编码： 100049
研究领域
生物医学统计； 机器学习
招生信息
招生专业
招生方向
工作经历
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, 2737–2743, 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, Vol 79, Issue 3, 2404–2416, 2023.
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, 16:272-294, 2023.
59) Zhang, S., Zhang, S. Yi, H., Ma, S. Aligned deep neural network for integrative analysis with high-dimensional input, Journal of Biomedical Informatics, vol. 144, 1044434, 2023.
60) Yan, H., Lu, S., Zhang, S. The cluster D-trace loss for differential network analysis, Journal of Applied Statistics, vol. 51(10), 1843-1860, 2024.
61) Han,W., Zhang，S., Ma，S., Ren，M. Information-incorporated sparse hierarchical cancer heterogeneity analysis，Statistics in Medicine，43:2280–2297, 2024.
62) Han,W., Zhang，S., Gao,H., Bu, D. Clustering on hierarchical heterogeneous data with prior pairwise relationships, BMC Bioinformatics, vol. 25, no. 40, 2024. https://doi.org/10.1186/s12859-024-05652-6
63) Zhang,F., Zhang, S., Li, S., Ren,M. Matrix regression heterogeneity analysis. Statistics and Computing, vol. 34, no. 95, 2024. https://doi.org/10.1007/s11222-024-10401-z
64) Yu, C., Zhang, S., Shen, L. GETr: A Geometric Equivariant Transformer for Point Cloud Registration, Computer Graphics Forum, vol. 43, no. 7, 2024. https://doi.org/10.1111/cgf.15216
65) Yu, C., Zhang, S., Shen, L. Equivariant Analysis of Point Clouds by Message Passing on Simplicial Complexes, ICIGP '24: Proceedings of the 2024 7th International Conference on Image and Graphics Processing, 286 - 293, 2024. https://doi.org/10.1145/3647649.3647695
66) Zhang, S., Zhang,S., He,W., Zhang,X. A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling, International Journal on Semantic Web and Information Systems, vol. 20(1), 1-23, 2024. https://doi.org/10.4018/IJSWIS.335113
67) Sun, X., Zhang,S., Ma, S. Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering, Entropy, vol. 26(4), 308, 2024. https://doi.org/10.3390/e26040308
68) Ma, C., Lin, C., Xue,Y., Zhang,S., Zhang,Q., Ma,S. Information-incorporated clustering analysis of disease prevalence trends, Annals of Applied Statistics, vol. 18(2): 1035-1050 , 2024. https://doi.org/10.1214/23-AOAS1821
69) Yu, C., Zhang, S., Shen, L. Y. CORNet: A consistency-based outlier rejection network for non-rigid registration. Computer-Aided Design, 103980, 2025.
70) Zhu, G., Zhang, S., Ren, M. Conditional Generative Learning from Invariant Representations in Multi-Source: Robustness and Efficiency. The 28th International Conference on Artificial Intelligence and Statistics, 2025.
71) Sun, X. & Zhang, S. Subclass consistency regularization for learning with noisy labels based on contrastive learning. Neurocomputing, 614: 128759, 2025.
72) Xiong, Y., Yang, X., Zhang, S. An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation. Communications in Statistics-Simulation and Computation, 1–26, 2025.
73) Chen, H., Shen, L. Y., Wang, C., Zhang, S. Multi Actors-Critic based particle swarm optimization algorithm. Neurocomputing, 624: 129460, 2025.
74) Xiong, Y., Ju, N., Zhang, S. Simulation-based Bayesian Inference from Privacy Protected Data. Transactions on Machine Learning Research.
75) Wang, R., Zhang, S., Ma, S. Network-based Hierarchical Heterogeneity Analysis and Application to Cancer Omics Data. Science China Mathematics English.
76) Zhu, G., Wang, R., Li, R., Zhang, S., Ma, S., Qiao, G., Mei, H. Latent Space Modeling for Human Disease Network with Temporal Variations: Analysis of Medicare Data. Annals of Applied Statistics.
77) Li, X., Zhang, S., Ren, M., Zhao, Q. Integrative Learning of Linear Non-Gaussian Directed Acyclic Graphs with Application on Multi-Source Gene Regulatory Network Analysis. Annals of Applied Statistics.