Zongming Ma

3.5k total citations
39 papers, 1.2k citations indexed

About

Zongming Ma is a scholar working on Statistics and Probability, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Zongming Ma has authored 39 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Statistics and Probability, 11 papers in Computational Mechanics and 8 papers in Artificial Intelligence. Recurrent topics in Zongming Ma's work include Statistical Methods and Inference (13 papers), Random Matrices and Applications (12 papers) and Sparse and Compressive Sensing Techniques (11 papers). Zongming Ma is often cited by papers focused on Statistical Methods and Inference (13 papers), Random Matrices and Applications (12 papers) and Sparse and Compressive Sensing Techniques (11 papers). Zongming Ma collaborates with scholars based in United States, China and Canada. Zongming Ma's co-authors include Tommaso Cai, Yihong Wu, Harrison H. Zhou, Chao Gao, Xiaodong Li, Chao Gao, Andreas Buja, Xianchao Xie, Zhuang Ma and Zhi Geng and has published in prestigious journals such as Journal of the American Statistical Association, Nature Biotechnology and Nature Immunology.

In The Last Decade

Zongming Ma

38 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Zongming Ma United States	19	498	370	271	192	173	39	1.2k
Afonso S. Bandeira United States	16	134 0.3×	252 0.7×	295 1.1×	74 0.4×	287 1.7×	44	1.0k
Stéphane Boucheron France	12	565 1.1×	785 2.1×	198 0.7×	113 0.6×	207 1.2×	22	1.7k
Harrison H. Zhou United States	20	634 1.3×	365 1.0×	172 0.6×	175 0.9×	112 0.6×	40	1.2k
Philippe Rigollet United States	12	303 0.6×	283 0.8×	121 0.4×	43 0.2×	63 0.4×	37	997
Raghunandan H. Keshavan United States	7	94 0.2×	249 0.7×	821 3.0×	70 0.4×	393 2.3×	11	1.3k
Chii-Ruey Hwang Taiwan	15	506 1.0×	471 1.3×	68 0.3×	172 0.9×	108 0.6×	39	1.3k
Murali Rao United States	15	413 0.8×	123 0.3×	100 0.4×	206 1.1×	235 1.4×	64	2.2k
Alain Berlinet France	11	374 0.8×	395 1.1×	123 0.5×	102 0.5×	134 0.8×	37	1.1k
Aarti Singh United States	18	107 0.2×	436 1.2×	190 0.7×	107 0.6×	141 0.8×	66	947
Noureddine El Karoui United States	14	501 1.0×	253 0.7×	184 0.7×	27 0.1×	43 0.2×	32	1.0k

Countries citing papers authored by Zongming Ma

Since Specialization

Citations

This map shows the geographic impact of Zongming Ma's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Zongming Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zongming Ma more than expected).

Fields of papers citing papers by Zongming Ma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zongming Ma. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Zongming Ma. The network helps show where Zongming Ma may publish in the future.

Co-authorship network of co-authors of Zongming Ma

This figure shows the co-authorship network connecting the top 25 collaborators of Zongming Ma. A scholar is included among the top collaborators of Zongming Ma based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Zongming Ma. Zongming Ma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Zhu, Bokai, Sheng Gao, Shuxiao Chen, et al.. (2025). CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data. Nature Immunology. 26(6). 963–974. 1 indexed citations

Fatemi, Ali M, Iraj Fooladi, Yonggan Zhao, & Zongming Ma. (2024). On the superior performance of SRI funds. International Review of Economics & Finance. 93. 567–581. 4 indexed citations

Zhang, Zhaojun, Divij Mathew, Sijia Huang, et al.. (2024). Recovery of biological signals lost in single-cell batch integration with CellANOVA. Nature Biotechnology. 43(11). 1861–1877. 4 indexed citations

Chen, Shuxiao, Bokai Zhu, Sijia Huang, et al.. (2023). Integration of spatial and single-cell data across modalities with weakly linked features. Nature Biotechnology. 42(7). 1096–1106. 45 indexed citations

Zhu, Bokai, Shuxiao Chen, Yunhao Bai, et al.. (2023). Robust single-cell matching and multimodal analysis using shared and distinct features. Nature Methods. 20(2). 304–315. 24 indexed citations

Ma, Zongming, et al.. (2023). Community Detection With Contextual Multilayer Networks. IEEE Transactions on Information Theory. 69(5). 3203–3239. 12 indexed citations

Xia, Cedric Huchuan, Zongming Ma, Zaixu Cui, et al.. (2020). Multi‐scale network regression for brain‐phenotype associations. Human Brain Mapping. 41(10). 2553–2566. 15 indexed citations

Ma, Zhuang, et al.. (2020). Universal Latent Space Model Fitting for Large Networks with Edge Covariates. Journal of Machine Learning Research. 21(4). 1–67. 27 indexed citations

Ding, Jian, Zongming Ma, Yihong Wu, & Jiaming Xu. (2020). Efficient random graph matching via degree profiles. Probability Theory and Related Fields. 179(1-2). 29–115. 39 indexed citations

10.

Gao, Chao & Zongming Ma. (2020). Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing. Statistical Science. 36(1). 20 indexed citations

11.

Ma, Zhuang & Zongming Ma. (2017). Exploration of Large Networks via Fast and Universal Latent Space Model Fitting.. arXiv (Cornell University). 4 indexed citations

12.

Gao, Chao, et al.. (2017). Achieving Optimal Misclassification Proportion in Stochastic Block Models. arXiv (Cornell University). 18(60). 1–45. 71 indexed citations

13.

Gao, Chao, Yu Lu, Zongming Ma, & Harrison H. Zhou. (2016). Optimal estimation and completion of matrices with biclustering structures. Journal of Machine Learning Research. 17(1). 5602–5630. 19 indexed citations

14.

Cai, Tommaso, Mark G. Low, & Zongming Ma. (2014). Adaptive Confidence Bands for Nonparametric Regression Functions. Journal of the American Statistical Association. 109(507). 1054–1070. 19 indexed citations

15.

Cai, Tommaso & Zongming Ma. (2013). Optimal hypothesis testing for high dimensional covariance matrices. Bernoulli. 19(5B). 74 indexed citations

16.

Ma, Zongming, et al.. (2013). A Sparse Singular Value Decomposition Method for High-Dimensional Data. Journal of Computational and Graphical Statistics. 23(4). 923–942. 26 indexed citations

17.

Buja, Andreas, et al.. (2013). Optimal denoising of simultaneously sparse and low rank matrices in high dimensions. 445–447. 1 indexed citations

18.

Johnstone, Iain M. & Zongming Ma. (2012). Fast approach to the Tracy–Widom law at the edge of GOE and GUE. The Annals of Applied Probability. 22(5). 1962–1988. 20 indexed citations

19.

Zhao, Hongjuan, Zongming Ma, Robert Tibshirani, et al.. (2009). Alteration of Gene Expression Signatures of Cortical Differentiation and Wound Response in Lethal Clear Cell Renal Cell Carcinomas. PLoS ONE. 4(6). e6039–e6039. 16 indexed citations

20.

Ma, Zongming, Xianchao Xie, & Zhi Geng. (2005). Collapsibility of Distribution Dependence. Journal of the Royal Statistical Society Series B (Statistical Methodology). 68(1). 127–133. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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