George Ostrouchov

1.3k total citations
55 papers, 594 citations indexed

About

George Ostrouchov is a scholar working on Computer Networks and Communications, Artificial Intelligence and Hardware and Architecture. According to data from OpenAlex, George Ostrouchov has authored 55 papers receiving a total of 594 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Networks and Communications, 17 papers in Artificial Intelligence and 11 papers in Hardware and Architecture. Recurrent topics in George Ostrouchov's work include Distributed and Parallel Computing Systems (12 papers), Advanced Data Storage Technologies (11 papers) and Parallel Computing and Optimization Techniques (11 papers). George Ostrouchov is often cited by papers focused on Distributed and Parallel Computing Systems (12 papers), Advanced Data Storage Technologies (11 papers) and Parallel Computing and Optimization Techniques (11 papers). George Ostrouchov collaborates with scholars based in United States, Germany and Colombia. George Ostrouchov's co-authors include Nagiza F. Samatova, Auroop R. Ganguly, Shiraj Khan, David J. Erickson, Sharba Bandyopadhyay, Sunil Saigal, Al Geist, V. Protopopescu, Anatoli V. Melechko and William Q. Meeker and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Technometrics and Water Resources Research.

In The Last Decade

George Ostrouchov

51 papers receiving 569 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
George Ostrouchov United States 14 144 139 87 68 61 55 594
Matthew Rocklin United States 5 158 1.1× 100 0.7× 55 0.6× 84 1.2× 44 0.7× 9 630
Jan Martinovič Czechia 11 82 0.6× 109 0.8× 26 0.3× 62 0.9× 45 0.7× 92 421
Xiaojing Wang China 11 105 0.7× 188 1.4× 46 0.5× 54 0.8× 12 0.2× 57 465
Anthony Brockwell United States 13 143 1.0× 171 1.2× 15 0.2× 88 1.3× 19 0.3× 37 753
Xinxin Fan China 22 232 1.6× 411 3.0× 191 2.2× 216 3.2× 75 1.2× 98 1.2k
Milton Halem United States 16 138 1.0× 121 0.9× 327 3.8× 118 1.7× 11 0.2× 94 906
Wancheng Zhang China 15 74 0.5× 239 1.7× 39 0.4× 25 0.4× 28 0.5× 68 961
Ramazan Aygün United States 14 287 2.0× 138 1.0× 111 1.3× 80 1.2× 78 1.3× 115 1.0k
Yongrui Chen China 19 326 2.3× 58 0.4× 64 0.7× 41 0.6× 35 0.6× 75 971
John E. Savage United States 15 247 1.7× 300 2.2× 33 0.4× 34 0.5× 48 0.8× 77 1.1k

Countries citing papers authored by George Ostrouchov

Since Specialization
Citations

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

Fields of papers citing papers by George Ostrouchov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of George Ostrouchov

This figure shows the co-authorship network connecting the top 25 collaborators of George Ostrouchov. A scholar is included among the top collaborators of George Ostrouchov 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 George Ostrouchov. George Ostrouchov 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.
Hong, Yili, et al.. (2025). A Spatially Correlated Competing Risks Time-to-Event Model for Supercomputer GPU Failure Data. Technometrics. 67(3). 531–545. 1 indexed citations
2.
Ostrouchov, George, et al.. (2024). Understanding GPU Memory Corruption at Extreme Scale: The Summit Case Study. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 188–200. 8 indexed citations
3.
Ostrouchov, George, et al.. (2016). Programming with Big Data – Interface to MPI. 3 indexed citations
4.
Chen, Wei-Chen, et al.. (2016). Programming with BIG Data in R: Scaling Analytics from One to Thousands of Nodes. Big Data Research. 8. 1–11. 13 indexed citations
5.
Huang, Jian, et al.. (2013). Contrasting Climate Ensembles: A Model-based Visualization Approach for Analyzing Extreme Events. Procedia Computer Science. 18. 2347–2356. 4 indexed citations
6.
Jiang, Lei, et al.. (2012). OpenMP-style parallelism in data-centered multicore computing with R. ACM SIGPLAN Notices. 47(8). 335–336. 1 indexed citations
7.
Huang, Jian, et al.. (2011). Visualizing Life Zone Boundary Sensitivities Across Climate Models and Temporal Spans. Procedia Computer Science. 4. 1582–1591. 10 indexed citations
8.
Shoshani, Arie, Terence Critchlow, Scott Klasky, et al.. (2011). Scientific Discovery at the Exascale. 19 indexed citations
9.
Naksinehaboon, Nichamon, et al.. (2009). Blue Gene/L Log Analysis and Time to Interrupt Estimation. 173–180. 20 indexed citations
10.
Ostrouchov, George. (2009). A Matrix Computation View of FastMap and RobustMap Dimension Reduction Algorithms. SIAM Journal on Matrix Analysis and Applications. 31(3). 1351–1360. 1 indexed citations
11.
Scott, Stephen L., Christian Engelmann, Geoffroy Vallée, et al.. (2009). A tunable holistic resiliency approach for high-performance computing systems. ACM SIGPLAN Notices. 44(4). 305–306. 2 indexed citations
12.
Khan, Shiraj, Sharba Bandyopadhyay, Auroop R. Ganguly, et al.. (2007). Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data. Physical Review E. 76(2). 26209–26209. 122 indexed citations
13.
Joy, Kenneth I., Mark Carl Miller, Hank Childs, et al.. (2007). Frameworks for visualization at the extreme scale. Journal of Physics Conference Series. 78. 12035–12035. 2 indexed citations
14.
Park, Byung Hoon, George Ostrouchov, & Nagiza F. Samatova. (2007). Sampling streaming data with replacement. Computational Statistics & Data Analysis. 52(2). 750–762. 8 indexed citations
15.
Kühn, Gerhard, et al.. (2006). Quantification and visualization of the human impacts of anticipated precipitation extremes in South America. AGU Fall Meeting Abstracts. 2006. 3 indexed citations
16.
Khan, Shiraj, Auroop R. Ganguly, Sharba Bandyopadhyay, et al.. (2006). Nonlinear statistics reveals stronger ties between ENSO and the tropical hydrological cycle. Geophysical Research Letters. 33(24). 42 indexed citations
17.
Ostrouchov, George & Nagiza F. Samatova. (2005). On FastMap and the convex hull of multivariate data: toward fast and robust dimension reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27(8). 1340–1343. 17 indexed citations
18.
Abu-Khzam, Faisal N., Nagiza F. Samatova, George Ostrouchov, Michael A. Langston, & Al Geist. (2002). Distributed Dimension Reduction Algorithms for Widely Dispersed Data.. 167–174. 6 indexed citations
19.
Downing, Darryl J., et al.. (2000). Large data series: modeling the usual to identify the unusual. Computational Statistics & Data Analysis. 32(3-4). 245–258. 9 indexed citations
20.
Ostrouchov, George & Edward L. Frome. (1993). A model search procedure for hierarchial models. Computational Statistics & Data Analysis. 15(3). 285–296. 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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