Greg Hamerly

5.5k total citations · 1 hit paper
35 papers, 3.6k citations indexed

About

Greg Hamerly is a scholar working on Hardware and Architecture, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Greg Hamerly has authored 35 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Hardware and Architecture, 16 papers in Computer Networks and Communications and 13 papers in Artificial Intelligence. Recurrent topics in Greg Hamerly's work include Parallel Computing and Optimization Techniques (17 papers), Software System Performance and Reliability (9 papers) and Advanced Clustering Algorithms Research (7 papers). Greg Hamerly is often cited by papers focused on Parallel Computing and Optimization Techniques (17 papers), Software System Performance and Reliability (9 papers) and Advanced Clustering Algorithms Research (7 papers). Greg Hamerly collaborates with scholars based in United States. Greg Hamerly's co-authors include Brad Calder, Erez Perelman, Timothy Sherwood, Charles Elkan, Jeremy Lau, Michael Van Biesbrouck, Suleyman Sair, J. Lau, John J. Sampson and Bryan F. Shaw and has published in prestigious journals such as Science Advances, Journal of Machine Learning Research and ACM SIGPLAN Notices.

In The Last Decade

Greg Hamerly

33 papers receiving 3.4k citations

Hit Papers

Automatically characterizing large scale program behavior 2002 2026 2010 2018 2002 400 800 1.2k
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. Automatically characterizing large scale program behavior
    2002 1.3k citations Timothy Sherwood, Erez Perelman 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
Greg Hamerly United States 20 2.3k 1.9k 948 774 653 35 3.6k
Kimberly Keeton United States 25 1.5k 0.7× 2.6k 1.4× 580 0.6× 437 0.6× 1000 1.5× 72 3.3k
Walid Najjar United States 29 1.9k 0.8× 1.8k 0.9× 609 0.6× 427 0.6× 182 0.3× 148 2.8k
Henri E. Bal Netherlands 33 2.3k 1.0× 3.6k 1.9× 212 0.2× 789 1.0× 1.0k 1.6× 246 4.4k
Eduard Ayguadé Spain 33 3.5k 1.5× 3.6k 1.9× 598 0.6× 428 0.6× 1.4k 2.1× 315 4.8k
Mateo Valero Spain 43 6.2k 2.8× 5.8k 3.0× 1.6k 1.7× 499 0.6× 1.3k 2.0× 452 7.7k
Robert Ross United States 36 1.9k 0.8× 4.1k 2.1× 185 0.2× 594 0.8× 1.2k 1.8× 206 4.9k
W. Richard Stevens United States 13 479 0.2× 3.1k 1.6× 1.3k 1.4× 497 0.6× 325 0.5× 21 3.6k
Howard Jay Siegel United States 40 3.9k 1.7× 6.5k 3.4× 1.2k 1.3× 606 0.8× 3.6k 5.5× 353 7.7k
Xipeng Shen United States 32 2.3k 1.0× 2.3k 1.2× 363 0.4× 2.3k 3.0× 1.5k 2.3× 213 5.3k
Feng Chen China 21 432 0.2× 1.3k 0.7× 177 0.2× 648 0.8× 301 0.5× 151 2.4k

Countries citing papers authored by Greg Hamerly

Since Specialization
Citations

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

Fields of papers citing papers by Greg Hamerly

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Greg Hamerly

This figure shows the co-authorship network connecting the top 25 collaborators of Greg Hamerly. A scholar is included among the top collaborators of Greg Hamerly 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 Greg Hamerly. Greg Hamerly 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
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Hamerly, Greg, et al.. (2023). Is ReLU Adversarially Robust?. 2 indexed citations
4.
McManamay, Ryan A., A. Rodriguez Perez, Greg Hamerly, et al.. (2023). Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility. Aquacultural Engineering. 104. 102389–102389. 8 indexed citations
5.
Mukai, Shizuo, et al.. (2019). Autonomous early detection of eye disease in childhood photographs. Science Advances. 5(10). eaax6363–eaax6363. 24 indexed citations
6.
Hamerly, Greg, et al.. (2016). Geometric methods to accelerate k-means algorithms. 324–332. 9 indexed citations
7.
Rivas, Pablo, Erich J. Baker, Greg Hamerly, & Bryan F. Shaw. (2014). Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings. BMC Ophthalmology. 14(1). 110–110. 17 indexed citations
8.
Rivas, Pablo, et al.. (2014). A Convolutional Neural Network approach for classifying leukocoria. 9–12. 8 indexed citations
9.
Hamerly, Greg. (2010). Making k-means even faster. 130–140. 111 indexed citations
10.
Hamerly, Greg, Erez Perelman, Jeremy Lau, Brad Calder, & Timothy Sherwood. (2006). Using Machine Learning to Guide Architecture Simulation. Journal of Machine Learning Research. 7(12). 343–378. 10 indexed citations
11.
Hamerly, Greg, Erez Perelman, & Brad Calder. (2006). Comparing multinomial and K-means clustering for SimPoint. 131–142. 3 indexed citations
12.
Lau, J., Erez Perelman, Greg Hamerly, Timothy Sherwood, & Brad Calder. (2005). Motivation for Variable Length Intervals and Hierarchical Phase Behavior. 135–146. 51 indexed citations
13.
Lau, J., John J. Sampson, Erez Perelman, Greg Hamerly, & Brad Calder. (2005). The Strong correlation Between Code Signatures and Performance. 236–247. 61 indexed citations
14.
Perelman, Erez, Greg Hamerly, & Brad Calder. (2004). Picking statistically valid and early simulation points. 244–255. 97 indexed citations
15.
Perelman, Erez, Greg Hamerly, & Brad Calder. (2003). Picking statistically valid and early simulation points. International Conference on Parallel Architectures and Compilation Techniques. 244–255. 145 indexed citations
16.
Hamerly, Greg & Charles Elkan. (2003). Learning the k in k-means. eScholarship (California Digital Library). 16. 281–288. 419 indexed citations
17.
Perelman, Erez, Greg Hamerly, Michael Van Biesbrouck, Timothy Sherwood, & Brad Calder. (2003). Using SimPoint for accurate and efficient simulation. 318–319. 198 indexed citations
18.
Hamerly, Greg & Charles Elkan. (2002). Alternatives to the k-means algorithm that find better clusterings. 600–607. 284 indexed citations
19.
Sherwood, Timothy, Erez Perelman, Greg Hamerly, & Brad Calder. (2002). Automatically characterizing large scale program behavior. ACM SIGOPS Operating Systems Review. 36(5). 45–57. 37 indexed citations
20.
Hamerly, Greg & Charles Elkan. (2002). Alternatives to the k-means algorithm that find better clusterings. 32 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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