Zhao Ren

1.1k total citations
25 papers, 529 citations indexed

About

Zhao Ren is a scholar working on Statistics and Probability, Molecular Biology and Computational Mechanics. According to data from OpenAlex, Zhao Ren has authored 25 papers receiving a total of 529 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Statistics and Probability, 9 papers in Molecular Biology and 6 papers in Computational Mechanics. Recurrent topics in Zhao Ren's work include Statistical Methods and Inference (15 papers), Statistical Methods and Bayesian Inference (8 papers) and Sparse and Compressive Sensing Techniques (6 papers). Zhao Ren is often cited by papers focused on Statistical Methods and Inference (15 papers), Statistical Methods and Bayesian Inference (8 papers) and Sparse and Compressive Sensing Techniques (6 papers). Zhao Ren collaborates with scholars based in United States, Australia and Canada. Zhao Ren's co-authors include Harrison H. Zhou, Tommaso Cai, Mengjie Chen, Cun‐Hui Zhang, Tingni Sun, Chao Gao, Wei Chen, Chao Gao, Hongyu Zhao and Mengjie Chen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and Bioinformatics.

In The Last Decade

Zhao Ren

23 papers receiving 513 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Zhao Ren United States	10	303	150	128	74	57	25	529
Yunzhang Zhu United States	10	220 0.7×	174 1.2×	75 0.6×	117 1.6×	29 0.5×	19	492
Kshitij Khare United States	13	283 0.9×	186 1.2×	64 0.5×	30 0.4×	20 0.4×	51	449
Nengfeng Zhou United States	5	200 0.7×	141 0.9×	192 1.5×	34 0.5×	25 0.4×	6	536
Eunho Yang South Korea	15	134 0.4×	405 2.7×	127 1.0×	65 0.9×	26 0.5×	74	691
Patricia Reynaud-Bouret France	12	124 0.4×	84 0.6×	108 0.8×	20 0.3×	7 0.1×	32	383
Makoto Aoshima Japan	13	471 1.6×	269 1.8×	160 1.3×	25 0.3×	60 1.1×	67	663
Kazuyoshi Yata Japan	13	341 1.1×	228 1.5×	159 1.2×	24 0.3×	60 1.1×	41	514
Minh‐Ngoc Tran Australia	10	236 0.8×	221 1.5×	25 0.2×	11 0.1×	18 0.3×	39	408
Quentin Berthet United States	8	114 0.4×	138 0.9×	89 0.7×	70 0.9×	43 0.8×	18	398
Ethan X. Fang United States	9	119 0.4×	129 0.9×	43 0.3×	129 1.7×	13 0.2×	24	383

Countries citing papers authored by Zhao Ren

Since Specialization

Citations

This map shows the geographic impact of Zhao Ren'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 Zhao Ren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhao Ren more than expected).

Fields of papers citing papers by Zhao Ren

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhao Ren. 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 Zhao Ren. The network helps show where Zhao Ren may publish in the future.

Co-authorship network of co-authors of Zhao Ren

This figure shows the co-authorship network connecting the top 25 collaborators of Zhao Ren. A scholar is included among the top collaborators of Zhao Ren 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 Zhao Ren. Zhao Ren is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Ren, Zhao, et al.. (2024). Gaussian differentially private robust mean estimation and inference. Bernoulli. 30(4). 1 indexed citations

Li, Yujia, Wenjia Wang, Wei Zong, et al.. (2024). Outcome-guided disease subtyping by generative model and weighted joint likelihood in transcriptomic applications. The Annals of Applied Statistics. 18(3). 1947–1964.

Zong, Wei, Li Zhu, Xiangrui Zeng, et al.. (2023). Transcriptomic congruence analysis for evaluating model organisms. Proceedings of the National Academy of Sciences. 120(6). e2202584120–e2202584120. 3 indexed citations

Ren, Zhao, et al.. (2021). Inference of large modified Poisson-type graphical models: Application to RNA-seq data in childhood atopic asthma studies. The Annals of Applied Statistics. 15(2). 1 indexed citations

Ren, Zhao, et al.. (2020). Minimax estimation of large precision matrices with bandable Cholesky factor. The Annals of Statistics. 48(4). 3 indexed citations

Ren, Zhao, et al.. (2020). Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings.. PubMed. 33. 16446–16456. 8 indexed citations

Minsker, Stanislav, et al.. (2019). User-Friendly Covariance Estimation for Heavy-Tailed Distributions. Statistical Science. 34(3). 32 indexed citations

Ren, Zhao, et al.. (2018). SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks. PLoS Computational Biology. 14(8). e1006369–e1006369. 30 indexed citations

Ren, Zhao, et al.. (2018). Tuning-Free Heterogeneous Inference in Massive Networks. Journal of the American Statistical Association. 114(528). 1908–1925. 3 indexed citations

10.

Ma, Tianzhou, Zhao Ren, & George C. Tseng. (2018). Variable screening with multiple studies. Statistica Sinica. 5 indexed citations

11.

Chen, Mengjie, Chao Gao, & Zhao Ren. (2018). Robust covariance and scatter matrix estimation under Huber’s contamination model. The Annals of Statistics. 46(5). 65 indexed citations

12.

Gao, Jiti, et al.. (2017). Variable selection for a categorical varying-coefficient model with identifications for determinants of body mass index. The Annals of Applied Statistics. 11(2). 3 indexed citations

13.

Wang, Ting, Zhao Ren, Ying Ding, et al.. (2016). FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks. PLoS Computational Biology. 12(2). e1004755–e1004755. 42 indexed citations

14.

Cai, Tommaso, Zhao Ren, & Harrison H. Zhou. (2016). Rejoinder of “Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation”. Electronic Journal of Statistics. 10(1). 3 indexed citations

15.

Cai, Tommaso, Zhao Ren, & Harrison H. Zhou. (2016). Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation. Electronic Journal of Statistics. 10(1). 100 indexed citations

16.

Chen, Mengjie, Chao Gao, & Zhao Ren. (2016). A general decision theory for Huber’s $\epsilon$-contamination model. Electronic Journal of Statistics. 10(2). 23 indexed citations

17.

Chen, Mengjie, Zhao Ren, Hongyu Zhao, & Harrison H. Zhou. (2015). Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model. Journal of the American Statistical Association. 111(513). 394–406. 30 indexed citations

18.

Ren, Zhao, Tingni Sun, Cun‐Hui Zhang, & Harrison H. Zhou. (2015). Asymptotic normality and optimalities in estimation of large Gaussian graphical models. The Annals of Statistics. 43(3). 113 indexed citations

19.

Cai, Tommaso, Zhao Ren, & Harrison H. Zhou. (2012). Optimal rates of convergence for estimating Toeplitz covariance matrices. Probability Theory and Related Fields. 156(1-2). 101–143. 49 indexed citations

20.

Ren, Zhao & Harrison H. Zhou. (2012). Discussion: Latent variable graphical model selection via convex optimization. The Annals of Statistics. 40(4). 9 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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