Zirui Zhou

1.4k total citations · 1 hit paper
26 papers, 775 citations indexed

About

Zirui Zhou is a scholar working on Artificial Intelligence, Numerical Analysis and Computational Mechanics. According to data from OpenAlex, Zirui Zhou has authored 26 papers receiving a total of 775 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Numerical Analysis and 10 papers in Computational Mechanics. Recurrent topics in Zirui Zhou's work include Sparse and Compressive Sensing Techniques (10 papers), Advanced Optimization Algorithms Research (9 papers) and Stochastic Gradient Optimization Techniques (6 papers). Zirui Zhou is often cited by papers focused on Sparse and Compressive Sensing Techniques (10 papers), Advanced Optimization Algorithms Research (9 papers) and Stochastic Gradient Optimization Techniques (6 papers). Zirui Zhou collaborates with scholars based in Canada, Hong Kong and United States. Zirui Zhou's co-authors include Jian Pei, Yong Zhang, Lanjun Wang, Anthony Man–Cho So, Lingyang Chu, Jiangchuan Liu, Yutao Huang, Zhaosong Lu, Changxin Liu and Yinyu Ye and has published in prestigious journals such as Mathematical Programming, SIAM Journal on Optimization and Proceedings of the American Mathematical Society.

In The Last Decade

Zirui Zhou

26 papers receiving 749 citations

Hit Papers

Personalized Cross-Silo Federated Learning on Non-IID Data 2021 2026 2022 2024 2021 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Personalized Cross-Silo Federated Learning on Non-IID Data
    2021 426 citations Yutao Huang, Lingyang Chu et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers

Zirui Zhou
Sashank J. Reddi United States
Jianjun Zhang China
Didier El Baz France
Remigijus Paulavičius Lithuania
Hasan Dağ Türkiye
Walid Krichene United States
Bilian Chen China
Stephen Tu United States
Zhuoran Yang United States
John Barnes United Kingdom
Sashank J. Reddi United States
Zirui Zhou
Citations per year, relative to Zirui Zhou Zirui Zhou (= 1×) peers Sashank J. Reddi

Countries citing papers authored by Zirui Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Zirui Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zirui Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Zirui Zhou. A scholar is included among the top collaborators of Zirui Zhou 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 Zirui Zhou. Zirui Zhou 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.
Zhou, Zirui, et al.. (2025). Long-term dual-trajectories of TyG and LAP and their association with cardiometabolic multimorbidity in midlife: the CARDIA study. Cardiovascular Diabetology. 24(1). 198–198. 4 indexed citations
2.
Carenini, Giuseppe, et al.. (2025). Evaluating LLM Reasoning in the Operations Research Domain with ORQA. Proceedings of the AAAI Conference on Artificial Intelligence. 39(23). 24902–24910. 1 indexed citations
3.
Guo, Qi, et al.. (2024). Association between cumulative intake of sugar-sweetened and artificially sweetened beverages and progression of coronary calcification: Insights from the CARDIA study. Nutrition Metabolism and Cardiovascular Diseases. 34(12). 2807–2816. 2 indexed citations
4.
Carenini, Giuseppe, et al.. (2024). Optimization modeling and verification from problem specifications using a multi-agent multi-stage LLM framework. INFOR Information Systems and Operational Research. 62(4). 599–617. 6 indexed citations
5.
Foster, Benjamin R., et al.. (2023). Sparse bounds for the bilinear spherical maximal function. Journal of the London Mathematical Society. 107(4). 1409–1449. 4 indexed citations
6.
Lu, Zhaosong, et al.. (2022). Penalty and Augmented Lagrangian Methods for Constrained DC Programming. Mathematics of Operations Research. 47(3). 2260–2285. 1 indexed citations
7.
Zhou, Zirui, et al.. (2022). Cubic Regularization Methods with Second-Order Complexity Guarantee Based on a New Subproblem Reformulation. Journal of the Operations Research Society of China. 10(3). 471–506. 2 indexed citations
8.
Huang, Fang, Zirui Zhou, Jian Pei, et al.. (2022). Improving Fairness for Data Valuation in Horizontal Federated Learning. 2022 IEEE 38th International Conference on Data Engineering (ICDE). 2440–2453. 35 indexed citations
9.
Huang, Yutao, Lingyang Chu, Zirui Zhou, et al.. (2021). Personalized Cross-Silo Federated Learning on Non-IID Data. Proceedings of the AAAI Conference on Artificial Intelligence. 35(9). 7865–7873. 426 indexed citations breakdown →
10.
Wang, Peng, Zirui Zhou, & Anthony Man–Cho So. (2021). Non-convex exact community recovery in stochastic block model. Mathematical Programming. 195(1-2). 1–37. 3 indexed citations
11.
Zhou, Zirui, Lingyang Chu, Changxin Liu, et al.. (2021). Towards Fair Federated Learning. 4100–4101. 24 indexed citations
12.
Huang, Yutao, Lingyang Chu, Zirui Zhou, et al.. (2020). Personalized Federated Learning: An Attentive Collaboration Approach.. arXiv (Cornell University). 10 indexed citations
13.
Wang, Peng, Zirui Zhou, & Anthony Man–Cho So. (2020). A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model. International Conference on Machine Learning. 1. 10126–10135. 1 indexed citations
14.
Lu, Zhaosong & Zirui Zhou. (2019). Nonmonotone Enhanced Proximal DC Algorithms for a Class of Structured Nonsmooth DC Programming. SIAM Journal on Optimization. 29(4). 2725–2752. 22 indexed citations
15.
Gao, Jiaxin, et al.. (2019). Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms. 11(3). 33–63. 15 indexed citations
16.
Lu, Zhaosong, et al.. (2018). Enhanced proximal DC algorithms with extrapolation for a class of structured nonsmooth DC minimization. Mathematical Programming. 176(1-2). 369–401. 29 indexed citations
17.
Zhou, Zirui & Anthony Man–Cho So. (2017). A unified approach to error bounds for structured convex optimization problems. Mathematical Programming. 165(2). 689–728. 72 indexed citations
18.
Zhou, Zirui, et al.. (2015). $\ell_{1,p}$-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods. International Conference on Machine Learning. 1501–1510. 7 indexed citations
19.
Lin, Tianyi, et al.. (2014). Latent Aspect Mining via Exploring Sparsity and Intrinsic Information. 879–888. 7 indexed citations
20.
Zhou, Zirui, et al.. (2013). On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization. Neural Information Processing Systems. 26. 710–718. 19 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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