Or Yair

426 total citations
12 papers, 260 citations indexed

About

Or Yair is a scholar working on Signal Processing, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Or Yair has authored 12 papers receiving a total of 260 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Signal Processing, 5 papers in Computational Mechanics and 5 papers in Artificial Intelligence. Recurrent topics in Or Yair's work include Blind Source Separation Techniques (7 papers), Sparse and Compressive Sensing Techniques (5 papers) and Neural Networks and Applications (3 papers). Or Yair is often cited by papers focused on Blind Source Separation Techniques (7 papers), Sparse and Compressive Sensing Techniques (5 papers) and Neural Networks and Applications (3 papers). Or Yair collaborates with scholars based in Israel, United States and Finland. Or Yair's co-authors include Ronen Talmon, Yonina C. Eldar, Déborah Cohen, Mirela Ben‐Chen, Ioannis G. Kevrekidis, Ronald R. Coifman, Mordechai Segev, Eran Lustig, Shahar Stein and Danny Eytan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and IEEE Transactions on Signal Processing.

In The Last Decade

Or Yair

12 papers receiving 256 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Or Yair Israel	6	98	81	56	48	33	12	260
Xiefeng Cheng China	11	67 0.7×	92 1.1×	37 0.7×	32 0.7×	45 1.4×	46	389
R. Moddemeijer Netherlands	4	110 1.1×	28 0.3×	74 1.3×	87 1.8×	46 1.4×	6	362
Yonghui Liu China	12	24 0.2×	40 0.5×	53 0.9×	85 1.8×	33 1.0×	27	413
Aboozar Ghaffari Iran	11	50 0.5×	122 1.5×	49 0.9×	39 0.8×	35 1.1×	37	415
Fetsje Bijma Netherlands	12	96 1.0×	40 0.5×	228 4.1×	37 0.8×	59 1.8×	21	472
Kamiar Rahnama Rad United States	10	33 0.3×	44 0.5×	169 3.0×	87 1.8×	40 1.2×	20	349
Julius Olaniyan Nigeria	4	147 1.5×	19 0.2×	67 1.2×	30 0.6×	43 1.3×	18	315
S. Qian United States	5	124 1.3×	31 0.4×	29 0.5×	37 0.8×	53 1.6×	11	338
Hanna Becker France	8	115 1.2×	50 0.6×	154 2.8×	14 0.3×	20 0.6×	9	298
Juan Guo China	7	28 0.3×	20 0.2×	30 0.5×	111 2.3×	52 1.6×	23	393

Countries citing papers authored by Or Yair

Since Specialization

Citations

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

Fields of papers citing papers by Or Yair

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Or Yair

This figure shows the co-authorship network connecting the top 25 collaborators of Or Yair. A scholar is included among the top collaborators of Or Yair 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 Or Yair. Or Yair 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:

12 of 12 papers shown

1.

Dietrich, Felix, et al.. (2022). Spectral Discovery of Jointly Smooth Features for Multimodal Data. SIAM Journal on Mathematics of Data Science. 4(1). 410–430. 5 indexed citations

2.

Lustig, Eran, Or Yair, Ronen Talmon, & Mordechai Segev. (2020). Identifying Topological Phase Transitions in Experiments Using Manifold Learning. Physical Review Letters. 125(12). 23 indexed citations

3.

Yair, Or, et al.. (2019). Domain Adaptation Using Riemannian Geometry of Spd Matrices. 4464–4468. 5 indexed citations

4.

Yair, Or, Mirela Ben‐Chen, & Ronen Talmon. (2019). Parallel Transport on the Cone Manifold of SPD Matrices for Domain Adaptation. IEEE Transactions on Signal Processing. 67(7). 1797–1811. 72 indexed citations

5.

Yair, Or, et al.. (2018). Data‐driven Evolution Equation Reconstruction for Parameter‐Dependent Nonlinear Dynamical Systems. Israel Journal of Chemistry. 58(6-7). 787–794. 3 indexed citations

6.

Yair, Or, et al.. (2017). CaSCADE: Compressed Carrier and DOA Estimation. IEEE Transactions on Signal Processing. 65(10). 2645–2658. 86 indexed citations

7.

Yair, Or, Ronen Talmon, Ronald R. Coifman, & Ioannis G. Kevrekidis. (2017). Reconstruction of normal forms by learning informed observation geometries from data. Proceedings of the National Academy of Sciences. 114(38). E7865–E7874. 34 indexed citations

8.

Yair, Or & Ronen Talmon. (2016). Local Canonical Correlation Analysis for Nonlinear Common Variables Discovery. IEEE Transactions on Signal Processing. 65(5). 1101–1115. 10 indexed citations

9.

Yair, Or & Ronen Talmon. (2016). Multimodal metric learning with local CCA. 1–5. 2 indexed citations

10.

Stein, Shahar, Or Yair, Déborah Cohen, & Yonina C. Eldar. (2015). Joint spectrum sensing and direction of arrival recovery from sub-Nyquist samples. 331–335. 17 indexed citations

11.

Yair, Or, Shahar Stein, Déborah Cohen, & Yonina C. Eldar. (2015). Uniform Linear Array Based Spectrum Sensing from Sub-Nyquist Samples. 2015 IEEE Global Communications Conference (GLOBECOM). 6. 1–6. 2 indexed citations

12.

Yair, Or, Shahar Stein, Deborah A. Cohen, & Yonina C. Eldar. (2014). Uniform Linear Array Based Spectrum Sensing from Sub-Nyquist Samples. 2015 IEEE Global Communications Conference (GLOBECOM). 1–6. 1 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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