Kalin Kanov

547 total citations
10 papers, 355 citations indexed

About

Kalin Kanov is a scholar working on Atmospheric Science, Computer Networks and Communications and Computational Mechanics. According to data from OpenAlex, Kalin Kanov has authored 10 papers receiving a total of 355 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Atmospheric Science, 4 papers in Computer Networks and Communications and 4 papers in Computational Mechanics. Recurrent topics in Kalin Kanov's work include Meteorological Phenomena and Simulations (6 papers), Fluid Dynamics and Turbulent Flows (4 papers) and Plant Water Relations and Carbon Dynamics (4 papers). Kalin Kanov is often cited by papers focused on Meteorological Phenomena and Simulations (6 papers), Fluid Dynamics and Turbulent Flows (4 papers) and Plant Water Relations and Carbon Dynamics (4 papers). Kalin Kanov collaborates with scholars based in United States, Netherlands and Australia. Kalin Kanov's co-authors include Randal Burns, Gregory L. Eyink, Alexander S. Szalay, Charles Meneveau, Cristian C. Lalescu, Jason Graham, Xiang I. A. Yang, Myoungkyu Lee, Nicholas Malaya and Robert Moser and has published in prestigious journals such as Nature, Journal of Parallel and Distributed Computing and Computing in Science & Engineering.

In The Last Decade

Kalin Kanov

10 papers receiving 343 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kalin Kanov United States	6	213	96	62	51	44	10	355
Cristian C. Lalescu United States	9	236 1.1×	121 1.3×	69 1.1×	53 1.0×	69 1.6×	19	446
Patrick Fischer France	12	249 1.2×	29 0.3×	42 0.7×	51 1.0×	25 0.6×	43	465
Daniela Tordella Italy	12	252 1.2×	105 1.1×	90 1.5×	71 1.4×	52 1.2×	66	414
O. N. Boratav United States	13	430 2.0×	53 0.6×	126 2.0×	84 1.6×	37 0.8×	21	526
Michele Buzzicotti Italy	11	228 1.1×	33 0.3×	36 0.6×	80 1.6×	46 1.0×	28	413
W. P. Dannevik United States	9	169 0.8×	42 0.4×	51 0.8×	95 1.9×	27 0.6×	22	425
Moritz Linkmann United Kingdom	14	203 1.0×	112 1.2×	37 0.6×	48 0.9×	32 0.7×	33	352
Kartik P. Iyer United States	11	232 1.1×	35 0.4×	57 0.9×	89 1.7×	28 0.6×	17	330
Vassilios Dallas United Kingdom	9	169 0.8×	60 0.6×	20 0.3×	22 0.4×	23 0.5×	17	282
A. G. Darbyshire United Kingdom	7	199 0.9×	71 0.7×	41 0.7×	81 1.6×	32 0.7×	11	364

Countries citing papers authored by Kalin Kanov

Since Specialization

Citations

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

Fields of papers citing papers by Kalin Kanov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kalin Kanov

This figure shows the co-authorship network connecting the top 25 collaborators of Kalin Kanov. A scholar is included among the top collaborators of Kalin Kanov 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 Kalin Kanov. Kalin Kanov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Livescu, Daniel, et al.. (2018). Remote visual analysis of large turbulence databases at multiple scales. Journal of Parallel and Distributed Computing. 120. 115–126. 7 indexed citations

2.

Graham, Jason, Kalin Kanov, Xiang I. A. Yang, et al.. (2015). A Web services accessible database of turbulent channel flow and its use for testing a new integral wall model for LES. Journal of Turbulence. 17(2). 181–215. 144 indexed citations

3.

Kanov, Kalin, Randal Burns, Cristian C. Lalescu, & Gregory L. Eyink. (2015). The Johns Hopkins Turbulence Databases: An Open Simulation Laboratory for Turbulence Research. Computing in Science & Engineering. 17(5). 10–17. 20 indexed citations

4.

Graham, John H., Kalin Kanov, Gregory L. Eyink, et al.. (2013). A Web-Services accessible database for channel flow turbulence at $Re_\tau$=1000. Bulletin of the American Physical Society. 3 indexed citations

5.

Eyink, Gregory L., Ethan T. Vishniac, Cristian C. Lalescu, et al.. (2013). Flux-freezing breakdown in high-conductivity magnetohydrodynamic turbulence. Nature. 497(7450). 466–469. 110 indexed citations

6.

Graham, Jason, et al.. (2013). Run-time creation of the turbulent channel flow database by an HPC simulation using MPI-DB. 151–156. 1 indexed citations

7.

Kanov, Kalin, et al.. (2012). Data-intensive spatial filtering in large numerical simulation datasets. 29. 1–9. 3 indexed citations

8.

Yu, Huidan, Kalin Kanov, Eric Perlman, et al.. (2012). Studying Lagrangian dynamics of turbulence using on-demand fluid particle tracking in a public turbulence database. Journal of Turbulence. 13. N12–N12. 58 indexed citations

9.

Kanov, Kalin, Eric Perlman, Randal Burns, Yanif Ahmad, & Alexander S. Szalay. (2011). I/O streaming evaluation of batch queries for data-intensive computational turbulence. 1–10. 5 indexed citations

10.

Szalay, Alexander S., et al.. (2011). An architecture for a data-intensive computer. 57–64. 4 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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