Kave Eshghi

1.8k total citations · 1 hit paper
24 papers, 1.2k citations indexed

About

Kave Eshghi is a scholar working on Computer Networks and Communications, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kave Eshghi has authored 24 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Networks and Communications, 11 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kave Eshghi's work include Advanced Data Storage Technologies (8 papers), Caching and Content Delivery (7 papers) and Logic, Reasoning, and Knowledge (5 papers). Kave Eshghi is often cited by papers focused on Advanced Data Storage Technologies (8 papers), Caching and Content Delivery (7 papers) and Logic, Reasoning, and Knowledge (5 papers). Kave Eshghi collaborates with scholars based in United States, Austria and United Kingdom. Kave Eshghi's co-authors include Mark Lillibridge, Deepavali Bhagwat, Robert Kowalski, Darrell D. E. Long, Vinay Deolalikar, Mehran Kafai, George Forman, Bir Bhanu, Thomas Gschwind and Chris Preist and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Future Generation Computer Systems and IEEE Transactions on Multimedia.

In The Last Decade

Kave Eshghi

24 papers receiving 1.1k citations

Hit Papers

Sparse indexing: large scale, inline deduplication using ... 2009 2026 2014 2020 2009 50 100 150 200 250
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. Sparse indexing: large scale, inline deduplication using sampling and locality
    2009 292 citations Mark Lillibridge, Kave Eshghi et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kave Eshghi United States 14 768 677 491 115 59 24 1.2k
Daniel Warneke Germany 11 668 0.9× 589 0.9× 161 0.3× 156 1.4× 59 1.0× 17 845
R. Das United States 8 494 0.6× 509 0.8× 290 0.6× 40 0.3× 19 0.3× 15 731
Robert E. Filman United States 11 400 0.5× 901 1.3× 992 2.0× 28 0.2× 22 0.4× 59 1.2k
Daniele Braga Italy 14 532 0.7× 336 0.5× 530 1.1× 102 0.9× 81 1.4× 35 825
Chaofeng Sha China 12 249 0.3× 231 0.3× 402 0.8× 74 0.6× 27 0.5× 39 648
Fawzy A. Torkey Egypt 8 251 0.3× 339 0.5× 247 0.5× 89 0.8× 12 0.2× 18 575
Dionysios Logothetis United States 14 516 0.7× 473 0.7× 268 0.5× 292 2.5× 16 0.3× 18 831
Khuzaima Daudjee Canada 13 554 0.7× 399 0.6× 258 0.5× 353 3.1× 15 0.3× 56 810
Eric N. Hanson United States 16 950 1.2× 403 0.6× 411 0.8× 79 0.7× 72 1.2× 37 1.1k
Gaurav Somani India 15 705 0.9× 522 0.8× 307 0.6× 37 0.3× 30 0.5× 35 915

Countries citing papers authored by Kave Eshghi

Since Specialization
Citations

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

Fields of papers citing papers by Kave Eshghi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kave Eshghi

This figure shows the co-authorship network connecting the top 25 collaborators of Kave Eshghi. A scholar is included among the top collaborators of Kave Eshghi 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 Kave Eshghi. Kave Eshghi 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.
Kafai, Mehran & Kave Eshghi. (2017). CROification: Accurate Kernel Classification with the Efficiency of Sparse Linear SVM. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(1). 34–48. 38 indexed citations
2.
Eshghi, Kave & Mehran Kafai. (2016). The CRO kernel: Using Concomitant Rank Order hashes for sparse high dimensional randomized feature maps. 11. 721–730. 4 indexed citations
3.
Kafai, Mehran, Kave Eshghi, Le An, & Bir Bhanu. (2015). A reference-based framework for pose invariant face recognition. 37. 1–8. 1 indexed citations
4.
Deolalikar, Vinay & Kave Eshghi. (2014). Lightweight approximate top-k for distributed settings. 7. 835–844. 1 indexed citations
5.
Kafai, Mehran, Kave Eshghi, & Bir Bhanu. (2014). Discrete Cosine Transform Locality-Sensitive Hashes for Face Retrieval. IEEE Transactions on Multimedia. 16(4). 1090–1103. 36 indexed citations
6.
Lillibridge, Mark, Kave Eshghi, & Deepavali Bhagwat. (2013). Improving restore speed for backup systems that use inline chunk-based deduplication. File and Storage Technologies. 183–198. 118 indexed citations
7.
Forman, George, et al.. (2009). Efficient detection of large-scale redundancy in enterprise file systems. ACM SIGOPS Operating Systems Review. 43(1). 84–91. 20 indexed citations
8.
Cherkasova, Ludmila, Kave Eshghi, Charles B. Morrey, Joseph Tucek, & Alistair Veitch. (2009). Applying syntactic similarity algorithms for enterprise information management. 1087–1096. 13 indexed citations
9.
Eshghi, Kave, et al.. (2008). Locality sensitive hash functions based on concomitant rank order statistics. 221–229. 31 indexed citations
10.
Eshghi, Kave, et al.. (2007). Jumbo store: providing efficient incremental upload and versioning for a utility rendering service. File and Storage Technologies. 22–22. 36 indexed citations
11.
Bhagwat, Deepavali, Kave Eshghi, & Pankaj Mehra. (2007). Content-based document routing and index partitioning for scalable similarity-based searches in a large corpus. 105–112. 15 indexed citations
12.
Forman, George, et al.. (2005). Finding similar files in large document repositories. 394–400. 51 indexed citations
13.
Eshghi, Kave. (2003). Intrinsic references in distributed systems. 675–680. 5 indexed citations
14.
Gschwind, Thomas, et al.. (2003). WebMon: A performance profiler for web transactions. 171–176. 22 indexed citations
15.
Eshghi, Kave. (1993). A tractable class of abduction problems. International Joint Conference on Artificial Intelligence. 3–8. 21 indexed citations
16.
Preist, Chris & Kave Eshghi. (1992). Consistency-Based and Abductive Diagnoses as Generalised Stable Models.. Future Generation Computer Systems. 514–521. 4 indexed citations
17.
Eshghi, Kave. (1990). Computing stable models by using the ATMS. National Conference on Artificial Intelligence. 272–277. 6 indexed citations
18.
Eshghi, Kave & Robert Kowalski. (1989). Abduction Compared with Negation by Failure.. International Conference on Lightning Protection. 234–254. 138 indexed citations
19.
Eshghi, Kave. (1988). Abductive planning with event calculus. International Conference on Lightning Protection. 562–579. 97 indexed citations
20.
Eshghi, Kave. (1982). Application of Meta-language Programming to Fault Finding in Logic Circuits.. International Conference on Lightning Protection. 240–246. 3 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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