Anru R. Zhang

3.2k total citations
67 papers, 1.5k citations indexed

About

Anru R. Zhang is a scholar working on Computational Mechanics, Artificial Intelligence and Computational Mathematics. According to data from OpenAlex, Anru R. Zhang has authored 67 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computational Mechanics, 19 papers in Artificial Intelligence and 15 papers in Computational Mathematics. Recurrent topics in Anru R. Zhang's work include Sparse and Compressive Sensing Techniques (22 papers), Tensor decomposition and applications (15 papers) and Blind Source Separation Techniques (12 papers). Anru R. Zhang is often cited by papers focused on Sparse and Compressive Sensing Techniques (22 papers), Tensor decomposition and applications (15 papers) and Blind Source Separation Techniques (12 papers). Anru R. Zhang collaborates with scholars based in United States, China and Australia. Anru R. Zhang's co-authors include Tommaso Cai, Dong Xia, Hyunseung Kang, Dylan S. Small, Hongzhe Li, Pixu Shi, Bing‐Hui Yang, Wenping Wang, Yue-fang Shen and Boheng Zhang and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Anru R. Zhang

65 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anru R. Zhang United States 22 548 296 235 226 201 67 1.5k
Hua Zhou United States 22 245 0.4× 270 0.9× 282 1.2× 279 1.2× 90 0.4× 105 2.0k
Mingyuan Zhou United States 22 313 0.6× 158 0.5× 728 3.1× 27 0.1× 203 1.0× 113 1.9k
Praneeth Netrapalli United States 15 516 0.9× 80 0.3× 364 1.5× 65 0.3× 206 1.0× 44 1.1k
Raghunandan H. Keshavan United States 7 821 1.5× 94 0.3× 249 1.1× 104 0.5× 323 1.6× 11 1.3k
Jianhua Z. Huang United States 23 208 0.4× 949 3.2× 398 1.7× 24 0.1× 108 0.5× 68 2.1k
Gabriel A. Frank Israel 12 790 1.4× 18 0.1× 264 1.1× 62 0.3× 253 1.3× 22 2.1k
Vincent Y. F. Tan Singapore 27 242 0.4× 151 0.5× 783 3.3× 17 0.1× 189 0.9× 215 2.5k
Debashis Paul United States 15 159 0.3× 734 2.5× 377 1.6× 16 0.1× 174 0.9× 42 1.7k
Laurent Jacob France 16 281 0.5× 180 0.6× 314 1.3× 10 0.0× 57 0.3× 30 1.7k
Matan Gavish Israel 13 169 0.3× 65 0.2× 186 0.8× 12 0.1× 105 0.5× 30 847

Countries citing papers authored by Anru R. Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Anru R. Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anru R. Zhang. 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 Anru R. Zhang. The network helps show where Anru R. Zhang may publish in the future.

Co-authorship network of co-authors of Anru R. Zhang

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

All Works

20 of 20 papers shown
1.
Ma, Huifang, et al.. (2025). Vispro improves imaging analysis for Visium spatial transcriptomics. Genome biology. 26(1). 173–173. 1 indexed citations
2.
Vemulapalli, Sreekanth, Fawaz Alenezi, Hyeon Ki Jeong, et al.. (2025). Machine Learning Computer Vision Point of Care Decision Support of Echocardiographic Identification of Hypertrophic Cardiomyopathy. JACC Advances. 4(5). 101746–101746.
3.
Treggiari, Miriam M., et al.. (2024). Soft phenotyping for sepsis via EHR time-aware soft clustering. Journal of Biomedical Informatics. 152. 104615–104615.
4.
Batra, Richa, Bryan J. Neth, Cameron Martino, et al.. (2024). Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer’s disease. PubMed. 2(1). 15–15. 14 indexed citations
5.
Lu, Jianfeng, et al.. (2024). One-Dimensional Tensor Network Recovery. SIAM Journal on Matrix Analysis and Applications. 45(3). 1217–1244. 1 indexed citations
6.
Lei, Jing, Anru R. Zhang, & Zihan Zhu. (2024). Computational and statistical thresholds in multi-layer stochastic block models. The Annals of Statistics. 52(5). 1 indexed citations
7.
Towe, Sheri L., et al.. (2023). Longitudinal changes in neurocognitive performance related to drug use intensity in a sample of persons with and without HIV who use illicit stimulants. Drug and Alcohol Dependence. 251. 110923–110923. 1 indexed citations
8.
Hoff, Peter D., et al.. (2023). Core shrinkage covariance estimation for matrix-variate data. Journal of the Royal Statistical Society Series B (Statistical Methodology). 85(5). 1659–1679. 2 indexed citations
9.
Zhang, Anru R., et al.. (2022). Optimal High-Order Tensor SVD via Tensor-Train Orthogonal Iteration. IEEE Transactions on Information Theory. 68(6). 3991–4019. 10 indexed citations
10.
Cai, Tommaso, et al.. (2022). Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference. IEEE Transactions on Information Theory. 68(9). 5975–6002. 11 indexed citations
11.
Willett, Rebecca, et al.. (2022). An optimal statistical and computational framework for generalized tensor estimation. The Annals of Statistics. 50(1). 29 indexed citations
12.
Shi, Pixu, et al.. (2021). High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis. Biometrika. 109(2). 405–420. 10 indexed citations
13.
Zhang, Anru R., et al.. (2020). Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection. arXiv (Cornell University). 3852–3856. 8 indexed citations
14.
Zhang, Anru R. & Kehui Chen. (2020). Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health. Statistica Sinica. 1 indexed citations
15.
Wan, Changlin, Wennan Chang, Yu Zhang, et al.. (2019). LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data. Nucleic Acids Research. 47(18). e111–e111. 34 indexed citations
16.
Zhang, Anru R. & Dong Xia. (2018). Tensor SVD: Statistical and Computational Limits. IEEE Transactions on Information Theory. 64(11). 7311–7338. 99 indexed citations
17.
Cai, Tommaso & Anru R. Zhang. (2016). Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data. Journal of Multivariate Analysis. 150. 55–74. 20 indexed citations
18.
Cai, Tommaso & Anru R. Zhang. (2015). Inference for high-dimensional differential correlation matrices. Journal of Multivariate Analysis. 143. 107–126. 18 indexed citations
19.
Cai, Tianxi, Tommaso Cai, & Anru R. Zhang. (2015). Structured Matrix Completion with Applications to Genomic Data Integration. Journal of the American Statistical Association. 111(514). 621–633. 41 indexed citations
20.
Cai, Tommaso & Anru R. Zhang. (2012). Sharp RIP bound for sparse signal and low-rank matrix recovery. Applied and Computational Harmonic Analysis. 35(1). 74–93. 137 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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