Jiaxi Ying

509 total citations
27 papers, 277 citations indexed

About

Jiaxi Ying is a scholar working on Artificial Intelligence, Computational Mechanics and Computer Networks and Communications. According to data from OpenAlex, Jiaxi Ying has authored 27 papers receiving a total of 277 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Computational Mechanics and 5 papers in Computer Networks and Communications. Recurrent topics in Jiaxi Ying's work include Sparse and Compressive Sensing Techniques (8 papers), Bayesian Modeling and Causal Inference (5 papers) and Tensor decomposition and applications (4 papers). Jiaxi Ying is often cited by papers focused on Sparse and Compressive Sensing Techniques (8 papers), Bayesian Modeling and Causal Inference (5 papers) and Tensor decomposition and applications (4 papers). Jiaxi Ying collaborates with scholars based in Hong Kong, China and United States. Jiaxi Ying's co-authors include Zhong Chen, Xiaobo Qu, Di Guo, Jian‐Feng Cai, Daniel P. Palomar, Hengfa Lu, José Vinícius de Miranda Cardoso, Jihui Wu, Gongguo Tang and Sandeep Kumar and has published in prestigious journals such as Chemical Communications, IEEE Transactions on Signal Processing and IEEE Access.

In The Last Decade

Jiaxi Ying

22 papers receiving 273 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jiaxi Ying Hong Kong 9 91 85 72 51 45 27 277
Caroline Chaux France 10 124 1.4× 36 0.4× 264 3.7× 22 0.4× 6 0.1× 27 390
Gui–Bo Ye United States 8 204 2.2× 48 0.6× 217 3.0× 77 1.5× 5 0.1× 9 427
A. Y. Yang China 9 131 1.4× 10 0.1× 212 2.9× 42 0.8× 38 0.8× 23 446
Paul Hand United States 8 99 1.1× 26 0.3× 78 1.1× 30 0.6× 4 0.1× 19 211
Debing Zhang China 7 108 1.2× 6 0.1× 179 2.5× 33 0.6× 33 0.7× 25 285
Emmanuel Soubies France 9 180 2.0× 27 0.3× 75 1.0× 26 0.5× 5 0.1× 30 368
Matthias Fey Germany 6 76 0.8× 21 0.2× 157 2.2× 118 2.3× 21 0.5× 13 327
Ankita Shukla India 10 74 0.8× 10 0.1× 75 1.0× 38 0.7× 7 0.2× 48 284
Takuma Yamaguchi Japan 10 20 0.2× 65 0.8× 87 1.2× 58 1.1× 4 0.1× 33 309
Zensho Nakao Japan 10 16 0.2× 53 0.6× 207 2.9× 47 0.9× 30 0.7× 74 400

Countries citing papers authored by Jiaxi Ying

Since Specialization
Citations

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

Fields of papers citing papers by Jiaxi Ying

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiaxi Ying

This figure shows the co-authorship network connecting the top 25 collaborators of Jiaxi Ying. A scholar is included among the top collaborators of Jiaxi Ying 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 Jiaxi Ying. Jiaxi Ying 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.
Ying, Jiaxi, et al.. (2025). Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary Signals. IEEE Transactions on Signal Processing. 73. 1153–1167. 3 indexed citations
2.
Ying, Jiaxi, et al.. (2024). CRB Solving For Two-Hop MIMO Collaborative Systems Based On Nested PARAFAC Model. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 36–41.
3.
Ying, Jiaxi, et al.. (2023). Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-$t$ Distribution. IEEE Transactions on Signal Processing. 71. 3726–3740. 1 indexed citations
4.
Ying, Jiaxi, et al.. (2022). Covariance Matrix Estimation Under Low-Rank Factor Model With Nonnegative Correlations. IEEE Transactions on Signal Processing. 70. 4020–4030. 5 indexed citations
5.
Ying, Jiaxi, et al.. (2022). A nested tensor-based receiver employing triple constellation precoding for three-hop cooperative communication systems. Digital Signal Processing. 133. 103862–103862. 2 indexed citations
6.
Ying, Jiaxi, et al.. (2022). Efficient Algorithms for General Isotone Optimization. Proceedings of the AAAI Conference on Artificial Intelligence. 36(8). 8575–8583.
7.
Zhao, Xinyuan, et al.. (2021). Tensor-Based Information Monitoring Receiver in UAV-Aided MIMO Communication Systems. IEEE Wireless Communications Letters. 11(1). 155–159. 3 indexed citations
8.
Ying, Jiaxi, José Vinícius de Miranda Cardoso, & Daniel P. Palomar. (2021). Minimax Estimation of Laplacian Constrained Precision Matrices. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 3736–3744. 3 indexed citations
9.
Cardoso, José Vinícius de Miranda, Jiaxi Ying, & Daniel P. Palomar. (2021). Graphical Models in Heavy-Tailed Markets. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 34. 8 indexed citations
10.
Ying, Jiaxi, José Vinícius de Miranda Cardoso, & Daniel P. Palomar. (2021). A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields. 2021 55th Asilomar Conference on Signals, Systems, and Computers. 15. 1520–1524. 1 indexed citations
11.
Kumar, Sandeep, Jiaxi Ying, José Vinícius de Miranda Cardoso, & Daniel P. Palomar. (2020). A Unified Framework for Structured Graph Learning via Spectral Constraints. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 21(22). 1–60. 12 indexed citations
12.
Ying, Jiaxi, José Vinícius de Miranda Cardoso, & Daniel P. Palomar. (2020). Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 33. 7101–7113. 10 indexed citations
13.
Kumar, Sandeep, Jiaxi Ying, José Vinícius de Miranda Cardoso, & Daniel P. Palomar. (2019). Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 18. 322–326. 2 indexed citations
14.
Kumar, Sandeep, Jiaxi Ying, José Vinícius de Miranda Cardoso, & Daniel P. Palomar. (2019). Structured Graph Learning Via Laplacian Spectral Constraints. arXiv (Cornell University). 32. 11647–11658. 8 indexed citations
15.
Ying, Jiaxi, Jian‐Feng Cai, Di Guo, et al.. (2018). Vandermonde Factorization of Hankel Matrix for Complex Exponential Signal Recovery—Application in Fast NMR Spectroscopy. IEEE Transactions on Signal Processing. 66(21). 5520–5533. 49 indexed citations
16.
Zeng, Kun, Di Guo, Jiaxi Ying, et al.. (2018). Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement. IEEE Access. 6. 57856–57867. 36 indexed citations
17.
Ying, Jiaxi, Hengfa Lu, Jian‐Feng Cai, et al.. (2017). Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals. IEEE Transactions on Signal Processing. 65(14). 3702–3717. 80 indexed citations
18.
Lu, Hengfa, Xinlin Zhang, Tianyu Qiu, et al.. (2017). Low Rank Enhanced Matrix Recovery of Hybrid Time and Frequency Data in Fast Magnetic Resonance Spectroscopy. IEEE Transactions on Biomedical Engineering. 65(4). 809–820. 26 indexed citations
19.
Qu, Xiaobo, Jiaxi Ying, Jian‐Feng Cai, & Zhong Chen. (2017). Accelerated magnetic resonance spectroscopy with Vandermonde factorization. PubMed. 11. 3537–3540. 4 indexed citations
20.
Hummels, D.M. & Jiaxi Ying. (2002). Locally optimal detection of unknown signals in non-Gaussian Markov noise. 1098–1101. 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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