Michael E. Papka

4.5k total citations
180 papers, 2.2k citations indexed

About

Michael E. Papka is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Information Systems and Management. According to data from OpenAlex, Michael E. Papka has authored 180 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Computer Networks and Communications, 47 papers in Computer Vision and Pattern Recognition and 45 papers in Information Systems and Management. Recurrent topics in Michael E. Papka's work include Distributed and Parallel Computing Systems (71 papers), Advanced Data Storage Technologies (63 papers) and Scientific Computing and Data Management (45 papers). Michael E. Papka is often cited by papers focused on Distributed and Parallel Computing Systems (71 papers), Advanced Data Storage Technologies (63 papers) and Scientific Computing and Data Management (45 papers). Michael E. Papka collaborates with scholars based in United States, United Kingdom and Germany. Michael E. Papka's co-authors include Venkatram Vishwanath, Mark Hereld, Rick Stevens, Jason Leigh, Khairi Reda, Zhiling Lan, Joseph A. Insley, Andrew Johnson, Aaron Knoll and Ian Foster and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computer Physics Communications and Neurocomputing.

In The Last Decade

Michael E. Papka

167 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael E. Papka United States 26 1.0k 545 442 396 331 180 2.2k
Kenneth Moreland United States 19 572 0.6× 524 1.0× 219 0.5× 149 0.4× 306 0.9× 58 1.4k
Dieter Kranzlmüller Germany 20 529 0.5× 236 0.4× 268 0.6× 205 0.5× 147 0.4× 147 1.4k
Jim Gray United States 18 804 0.8× 478 0.9× 103 0.2× 338 0.9× 210 0.6× 36 1.9k
Henri E. Bal Netherlands 33 3.6k 3.5× 358 0.7× 2.3k 5.2× 1.0k 2.6× 269 0.8× 246 4.4k
Jatin Chhugani United States 23 1.7k 1.7× 721 1.3× 1.3k 3.0× 483 1.2× 17 0.1× 39 3.0k
Daniel A. Reed United States 35 2.9k 2.9× 173 0.3× 1.7k 3.8× 869 2.2× 352 1.1× 161 3.6k
Kayvon Fatahalian United States 24 1.3k 1.2× 974 1.8× 1.5k 3.5× 305 0.8× 20 0.1× 63 3.0k
Dennis M. Ritchie United States 19 1.7k 1.6× 150 0.3× 1.2k 2.7× 691 1.7× 129 0.4× 29 3.7k
Robert F. Sproull United States 21 436 0.4× 1.2k 2.2× 342 0.8× 119 0.3× 26 0.1× 51 3.1k
Yuhao Zhu United States 24 468 0.5× 636 1.2× 343 0.8× 131 0.3× 15 0.0× 107 1.8k

Countries citing papers authored by Michael E. Papka

Since Specialization
Citations

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

Fields of papers citing papers by Michael E. Papka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael E. Papka. 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 Michael E. Papka. The network helps show where Michael E. Papka may publish in the future.

Co-authorship network of co-authors of Michael E. Papka

This figure shows the co-authorship network connecting the top 25 collaborators of Michael E. Papka. A scholar is included among the top collaborators of Michael E. Papka 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 Michael E. Papka. Michael E. Papka 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.
Côté, Benoît, Yanfei Guo, Ryan Chard, et al.. (2025). FIRST: Federated Inference Resource Scheduling Toolkit for Scientific AI Model Access. 52–60.
2.
3.
Zheng, Huihuo, Murat Keçeli, J. T. Childers, et al.. (2025). AskHPC: A ChatBot for High Performance Computing User Support. 727–739.
4.
Johnson, Andrew, Luc Renambot, G. Elisabeta Marai, et al.. (2024). Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past. PRESENCE Virtual and Augmented Reality. 33. 77–127. 1 indexed citations
5.
Wu, Qi, et al.. (2024). Distributed Neural Representation for Reactive In Situ Visualization. IEEE Transactions on Visualization and Computer Graphics. 31(9). 5199–5214. 2 indexed citations
6.
Hampton‐Marcell, Jarrad, et al.. (2023). Leveraging national laboratories to increase Black representation in STEM: recommendations within the Department of Energy. International Journal of STEM Education. 10(1). 3 indexed citations
7.
8.
Chard, Ryan, Jim Pruyne, Ben Blaiszik, et al.. (2023). Active Research Data Management with the Django Globus Portal Framework. Practice and Experience in Advanced Research Computing. 43–51. 4 indexed citations
9.
Clyde, Austin, et al.. (2023). ChemoGraph: Interactive Visual Exploration of the Chemical Space. Computer Graphics Forum. 42(3). 13–24. 2 indexed citations
10.
Sun, Maoyuan, et al.. (2023). The State of the Art in Visualizing Dynamic Multivariate Networks. Computer Graphics Forum. 42(3). 471–490. 7 indexed citations
11.
Reda, Khairi, et al.. (2021). Color Nameability Predicts Inference Accuracy in Spatial Visualizations. Computer Graphics Forum. 40(3). 49–60. 10 indexed citations
12.
Papka, Michael E., et al.. (2021). viewSq, a Visual Molecular Dynamics (VMD) module for calculating, analyzing, and visualizing X-ray and neutron structure factors from atomistic simulations. Computer Physics Communications. 264. 107881–107881. 39 indexed citations
13.
Xu, Yang, Venkatram Vishwanath, William Allcock, et al.. (2016). A data driven scheduling approach for power management on HPC systems. IEEE International Conference on High Performance Computing, Data, and Analytics. 56. 10 indexed citations
14.
Childers, J. T., Thomas Uram, T. LeCompte, Michael E. Papka, & B. Trocmé. (2016). Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads. Computer Physics Communications. 210. 54–59. 5 indexed citations
15.
Malakar, Preeti, Venkatram Vishwanath, Chris Knight, Todd Munson, & Michael E. Papka. (2016). Optimal execution of co-analysis for large-scale molecular dynamics simulations. IEEE International Conference on High Performance Computing, Data, and Analytics. 60. 7 indexed citations
16.
Finkel, Hal, Venkatram Vishwanath, Katrin Heitmann, et al.. (2014). Scalable Parallel I/O on a Blue Gene/Q Supercomputer Using Compression, Topology-Aware Data Aggregation, and Subfiling. 107–111. 17 indexed citations
17.
Ma, Kwan‐Liu & Michael E. Papka. (2009). Proceedings of the 2009 Workshop on Ultrascale Visualization. IEEE International Conference on High Performance Computing, Data, and Analytics. 2 indexed citations
18.
Levow, Gina‐Anne, Bennett I. Bertenthal, David McNeill, et al.. (2007). SIDGrid: A Framework for Distributed, Integrated Multimodal Annotation, Archiving, and Analysis. 35(25). 5177–9. 1 indexed citations
19.
Das, Sumit Kumar, et al.. (2002). A genetic programming application in virtual reality. 480–484. 13 indexed citations
20.
Stevens, Rick, Michael E. Papka, Mark J. Kilgard, Gareth Humphreys, & Thomas Funkhouser. (2001). Commodity graphics accelerators for scientific visualization. IEEE Visualization. 527–529. 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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