Morteza Ashraphijuo

403 total citations
20 papers, 267 citations indexed

About

Morteza Ashraphijuo is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Computational Mathematics. According to data from OpenAlex, Morteza Ashraphijuo has authored 20 papers receiving a total of 267 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Mechanics, 7 papers in Computer Vision and Pattern Recognition and 7 papers in Computational Mathematics. Recurrent topics in Morteza Ashraphijuo's work include Sparse and Compressive Sensing Techniques (14 papers), Tensor decomposition and applications (7 papers) and Optimal Power Flow Distribution (5 papers). Morteza Ashraphijuo is often cited by papers focused on Sparse and Compressive Sensing Techniques (14 papers), Tensor decomposition and applications (7 papers) and Optimal Power Flow Distribution (5 papers). Morteza Ashraphijuo collaborates with scholars based in United States. Morteza Ashraphijuo's co-authors include Javad Lavaei, Ramtin Madani, Xiaodong Wang, Vaneet Aggarwal, Alper Atamtürk, Salar Fattahi and Ross Baldick and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Power Systems and IEEE Transactions on Signal Processing.

In The Last Decade

Morteza Ashraphijuo

20 papers receiving 254 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Morteza Ashraphijuo United States 9 160 81 62 57 50 20 267
Xinyi Xu China 9 32 0.2× 21 0.3× 20 0.3× 72 1.3× 3 0.1× 17 175
Salar Fattahi United States 9 76 0.5× 33 0.4× 86 1.4× 7 0.1× 1 0.0× 26 208
Damien Scieur France 5 105 0.7× 26 0.3× 45 0.7× 6 0.1× 2 0.0× 11 179
Cédric Josz United States 7 195 1.2× 30 0.4× 72 1.2× 5 0.1× 24 282
Philippe Dreesen Belgium 9 17 0.1× 40 0.5× 108 1.7× 4 0.1× 22 0.4× 43 232
N.G. Maratos Greece 8 163 1.0× 15 0.2× 139 2.2× 6 0.1× 19 298
Yafei Tian China 10 220 1.4× 8 0.1× 25 0.4× 27 0.5× 3 0.1× 48 288
Ian Li-Jin Thng Singapore 11 187 1.2× 52 0.6× 4 0.1× 7 0.1× 4 0.1× 30 355
Tian Ding China 7 144 0.9× 31 0.4× 8 0.1× 12 0.2× 2 0.0× 16 225
Kon Max Wong Canada 6 320 2.0× 41 0.5× 24 0.4× 5 0.1× 8 441

Countries citing papers authored by Morteza Ashraphijuo

Since Specialization
Citations

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

Fields of papers citing papers by Morteza Ashraphijuo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Morteza Ashraphijuo

This figure shows the co-authorship network connecting the top 25 collaborators of Morteza Ashraphijuo. A scholar is included among the top collaborators of Morteza Ashraphijuo 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 Morteza Ashraphijuo. Morteza Ashraphijuo 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.
Ashraphijuo, Morteza & Xiaodong Wang. (2020). Structured Alternating Minimization for Union of Nested Low-Rank Subspaces Data Completion. IEEE Journal on Selected Areas in Information Theory. 1(3). 632–644. 1 indexed citations
2.
Ashraphijuo, Morteza & Xiaodong Wang. (2020). Union of Low-Rank Tensor Spaces: Clustering and Completion. Journal of Machine Learning Research. 21(69). 1–36. 6 indexed citations
3.
Ashraphijuo, Morteza, Xiaodong Wang, & Vaneet Aggarwal. (2020). Fundamental sampling patterns for low-rank multi-view data completion. Pattern Recognition. 103. 107307–107307. 5 indexed citations
4.
Ashraphijuo, Morteza & Xiaodong Wang. (2020). Characterization Of sampling patterns for low-tt-rank tensor retrieval. Annals of Mathematics and Artificial Intelligence. 88(8). 859–886. 2 indexed citations
5.
Ashraphijuo, Morteza, Vaneet Aggarwal, & Xiaodong Wang. (2019). Deterministic and Probabilistic Conditions for Finite Completability of Low-Tucker-Rank Tensor. IEEE Transactions on Information Theory. 65(9). 5380–5400. 10 indexed citations
6.
Ashraphijuo, Morteza, et al.. (2019). Low-Rank Data Completion With Very Low Sampling Rate Using Newton's Method. IEEE Transactions on Signal Processing. 67(7). 1849–1859. 8 indexed citations
7.
Ashraphijuo, Morteza & Xiaodong Wang. (2019). Fundamental conditions on the sampling pattern for union of low-rank subspaces retrieval. Annals of Mathematics and Artificial Intelligence. 87(4). 373–393. 4 indexed citations
8.
Ashraphijuo, Morteza & Xiaodong Wang. (2018). A Characterization of Sampling Patterns for Union of Low-Rank Subspaces Retrieval Problem.. 5 indexed citations
9.
Ashraphijuo, Morteza, et al.. (2018). On the DoF of Two-Way <inline-formula> <tex-math notation="LaTeX">$2\times 2\times 2$</tex-math> </inline-formula> MIMO Relay Networks. IEEE Transactions on Vehicular Technology. 67(11). 10554–10563. 1 indexed citations
10.
Ashraphijuo, Morteza & Xiaodong Wang. (2018). Clustering a union of low-rank subspaces of different dimensions with missing data. Pattern Recognition Letters. 120. 31–35. 10 indexed citations
11.
Ashraphijuo, Morteza, Vaneet Aggarwal, & Xiaodong Wang. (2017). A characterization of sampling patterns for low-tucker-rank tensor completion problem. 531–535. 11 indexed citations
12.
Ashraphijuo, Morteza, Xiaodong Wang, & Vaneet Aggarwal. (2017). A characterization of sampling patterns for low-rank multi-view data completion problem. 16. 1147–1151. 8 indexed citations
13.
Ashraphijuo, Morteza, Xiaodong Wang, & Vaneet Aggarwal. (2017). An approximation of the CP-rank of a partially sampled tensor. 16. 604–611. 7 indexed citations
14.
Fattahi, Salar, Morteza Ashraphijuo, Javad Lavaei, & Alper Atamtürk. (2017). Conic relaxations of the unit commitment problem. Energy. 134. 1079–1095. 36 indexed citations
15.
Madani, Ramtin, Morteza Ashraphijuo, Javad Lavaei, & Ross Baldick. (2016). Power system state estimation with a limited number of measurements. 12 indexed citations
16.
Ashraphijuo, Morteza, Ramtin Madani, & Javad Lavaei. (2016). Characterization of rank-constrained feasibility problems via a finite number of convex programs. 6544–6550. 12 indexed citations
17.
Ashraphijuo, Morteza, Salar Fattahi, Javad Lavaei, & Alper Atamtürk. (2016). A strong semidefinite programming relaxation of the unit commitment problem. 694–701. 9 indexed citations
18.
Madani, Ramtin, Morteza Ashraphijuo, & Javad Lavaei. (2015). Promises of Conic Relaxation for Contingency-Constrained Optimal Power Flow Problem. IEEE Transactions on Power Systems. 31(2). 1297–1307. 94 indexed citations
19.
Ashraphijuo, Morteza, Ramtin Madani, & Javad Lavaei. (2015). Inverse function theorem for polynomial equations using semidefinite programming. 6589–6596. 7 indexed citations
20.
Madani, Ramtin, Morteza Ashraphijuo, & Javad Lavaei. (2014). Promises of conic relaxation for contingency-constrained optimal power flow problem. 1064–1071. 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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