David S. Greenberg

3.1k total citations
49 papers, 1.9k citations indexed

About

David S. Greenberg is a scholar working on Computer Networks and Communications, Hardware and Architecture and Electrical and Electronic Engineering. According to data from OpenAlex, David S. Greenberg has authored 49 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Networks and Communications, 14 papers in Hardware and Architecture and 11 papers in Electrical and Electronic Engineering. Recurrent topics in David S. Greenberg's work include Parallel Computing and Optimization Techniques (10 papers), Interconnection Networks and Systems (9 papers) and Neural dynamics and brain function (8 papers). David S. Greenberg is often cited by papers focused on Parallel Computing and Optimization Techniques (10 papers), Interconnection Networks and Systems (9 papers) and Neural dynamics and brain function (8 papers). David S. Greenberg collaborates with scholars based in United States, Germany and Switzerland. David S. Greenberg's co-authors include Jason N. D. Kerr, Fritjof Helmchen, Arthur R. Houweling, Damian J. Wallace, J Sawiński, Giuseppe Notaro, Randy M. Bruno, Christiaan P. J. de Kock, Bert Sakmann and Winfried Denk and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

David S. Greenberg

42 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David S. Greenberg United States 18 1.0k 990 340 292 218 49 1.9k
Daniel Soudry Israel 18 678 0.7× 576 0.6× 131 0.4× 190 0.7× 92 0.4× 41 2.2k
Friedrich T. Sommer United States 28 1.5k 1.5× 780 0.8× 298 0.9× 83 0.3× 123 0.6× 93 2.4k
Andrew P. Davison France 18 831 0.8× 481 0.5× 227 0.7× 195 0.7× 70 0.3× 47 1.5k
Arjen van Ooyen Netherlands 33 1.9k 1.9× 1.5k 1.5× 542 1.6× 313 1.1× 83 0.4× 95 3.5k
Mária Ercsey-Ravasz Romania 21 1.3k 1.2× 290 0.3× 256 0.8× 52 0.2× 132 0.6× 47 2.3k
Marc-Oliver Gewaltig Germany 13 1.6k 1.6× 861 0.9× 148 0.4× 115 0.4× 141 0.6× 34 2.0k
Felix Schürmann Switzerland 21 792 0.8× 571 0.6× 250 0.7× 197 0.7× 50 0.2× 56 1.5k
Yu Mu China 20 846 0.8× 849 0.9× 779 2.3× 310 1.1× 69 0.3× 86 2.5k
Sharon Crook United States 16 609 0.6× 295 0.3× 252 0.7× 134 0.5× 122 0.6× 57 1.0k
Padraig Gleeson United Kingdom 17 792 0.8× 608 0.6× 527 1.6× 264 0.9× 24 0.1× 45 1.7k

Countries citing papers authored by David S. Greenberg

Since Specialization
Citations

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

Fields of papers citing papers by David S. Greenberg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David S. Greenberg. 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 David S. Greenberg. The network helps show where David S. Greenberg may publish in the future.

Co-authorship network of co-authors of David S. Greenberg

This figure shows the co-authorship network connecting the top 25 collaborators of David S. Greenberg. A scholar is included among the top collaborators of David S. Greenberg 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 David S. Greenberg. David S. Greenberg 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.
Wallace, Damian J., Kay-Michael Voit, J Sawiński, et al.. (2025). Eye saccades align optic flow with retinal specializations during object pursuit in freely moving ferrets. Current Biology. 35(4). 761–775.e10. 1 indexed citations
2.
Greenberg, David S., et al.. (2025). SuperdropNet: A Stable and Accurate Machine Learning Proxy for Droplet‐Based Cloud Microphysics. Journal of Advances in Modeling Earth Systems. 17(6).
3.
Tosi, Nicola, et al.. (2025). Accelerating the Discovery of Steady‐States of Planetary Interior Dynamics With Machine Learning. elib (German Aerospace Center). 2(1).
4.
Arnold, Caroline, et al.. (2024). Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5). Geoscientific model development. 17(9). 4017–4029. 1 indexed citations
5.
Rose, Patrick, et al.. (2022). Measurement of arbitrary scan patterns for correction of imaging distortions in laser scanning microscopy. Biomedical Optics Express. 13(7). 3983–3983. 1 indexed citations
6.
Greenberg, David S., Damian J. Wallace, & Jason N. D. Kerr. (2014). Imaging Neuronal Population Activity in Awake and Anesthetized Rodents. Cold Spring Harbor Protocols. 2014(9). pdb.top083535–pdb.top083535. 4 indexed citations
7.
Greenberg, David S. & Jason N. D. Kerr. (2008). Automated correction of fast motion artifacts for two-photon imaging of awake animals. Journal of Neuroscience Methods. 176(1). 1–15. 121 indexed citations
8.
Kerr, Jason N. D., David S. Greenberg, & Fritjof Helmchen. (2005). Imaging input and output of neocortical networks in vivo. Proceedings of the National Academy of Sciences. 102(39). 14063–14068. 351 indexed citations
9.
Womble, David E., et al.. (2002). Applications of boundary element methods on the Intel Paragon. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 35. 680–684.
10.
Brightwell, Ron, et al.. (2000). Massively parallel computing using commodity components. Parallel Computing. 26(2-3). 243–266. 57 indexed citations
11.
Wilson, David B., David S. Greenberg, & Cynthia A. Phillips. (1997). Beyond Islands: Runs in clone-probe matrices. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2 indexed citations
12.
Greenberg, David S., et al.. (1997). Digest of Social Experiments. Medical Entomology and Zoology. 21 indexed citations
13.
Womble, David E., et al.. (1994). Applications of boundary element methods on the Intel Paragon. Conference on High Performance Computing (Supercomputing). 680–684. 1 indexed citations
14.
Greenberg, David S. & Sorin Istrail. (1994). The chimeric mapping problem: Algorithmic strategies and performance evaluation on synthetic genomic data. Computers & Chemistry. 18(3). 207–220. 8 indexed citations
15.
Greenberg, David S., et al.. (1993). Achieving high performance on the Intel Paragon. University of North Texas Digital Library (University of North Texas). 1 indexed citations
16.
Greenberg, David S.. (1993). Efficient wiring of reconfigurable parallel processors. 318–324. 2 indexed citations
17.
Greenberg, David S. & Gadi Taubenfeld. (1992). Choice coordination with multiple alternatives. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
18.
Greenberg, David S., Lenwood S. Heath, & Arnold L. Rosenberg. (1990). Optimal embeddings of butterfly-like graphs in the hypercube. Theory of Computing Systems. 23(1). 61–77. 27 indexed citations
19.
Greenberg, David S., Lenwood S. Heath, & Arnold L. Rosenberg. (1988). Optimal Embeddings of the FFT Graph in the Hypercube. Nucleic Acids Research. 5(12). 4547–62. 4 indexed citations
20.
Greenberg, David S.. (1965). The New Accelerator: List "Narrows". Science. 149(3691). 1484–1484.

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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