Garrison Cottrell: h-index, Total Citations, and Citation Map
Garrison Cottrell's h-index is 67 (181 i10-index, 30,658+ total citations across 374+ publications) according to Google Scholar as of June 2026. Garrison Cottrell is affiliated with Computer Science and Engineering, UCSD.
Garrison Cottrell is a researcher affiliated with Computer Science and Engineering, UCSD, specializing in Deep learning, cognitive modeling, computational cognitive neuroscience. Their work has been cited 30,658 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Garrison Cottrell's Citation Metrics
Bibliometric impact based on 374 indexed publications.
- H-Index
- 67
- i10-Index
- 181
- Total Citations
- 30,658
- Citing Countries
- 10
As of June 2026.
Garrison Cottrell has an h-index of 67 and 30,658 total citations across 374 publications, with research cited by institutions in 10 countries.
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We've mapped 5,000 of 30,658 citations for Garrison Cottrell
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Behavior recognition via sparse spatio-temporal features
20053,543
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Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Garrison Cottrell's research
Garrison Cottrell is a researcher in Deep learning, cognitive modeling and computational cognitive neuroscience at Computer Science and Engineering, UCSD. Their work has been cited 30,658 times across 374 publications (h-index 67), according to Google Scholar.
Their most-cited work, “Behavior recognition via sparse spatio-temporal features” (2005), has accumulated 3,543 citations. Other influential works include “Understanding convolution for semantic segmentation” (2018) with 2,872 citations and “A dual-stage attention-based recurrent neural network for time series prediction” (2017) with 2,250 citations.
Citations of Garrison Cottrell's research come primarily from China, United States and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
