Subhashini Venugopalan: h-index, Total Citations, and Citation Map
Subhashini Venugopalan's h-index is 36 (51 i10-index, 32,964+ total citations across 84+ publications) according to Google Scholar as of September 2026. Subhashini Venugopalan is affiliated with University of Texas at Austin.
Subhashini Venugopalan is a researcher affiliated with University of Texas at Austin, specializing in Natural Language Processing, Computer Vision, Machine Learning. Their work has been cited 32,964 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Subhashini Venugopalan's Citation Metrics
Bibliometric impact based on 84 indexed publications.
- H-Index
- 36
- i10-Index
- 51
- Total Citations
- 32,964
- Citing Countries
- 65
As of September 2026.
Subhashini Venugopalan has an h-index of 36 and 32,964 total citations across 84 publications, with research cited by institutions in 65 countries.
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Global Impact Map
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
201610,077
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
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Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
19 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered semantic hierarchies and zero-shot recognition for describing arbitrary video activities, establishing a foundational framework for compositional visual understanding.
The researcher pioneered deep learning methods for analyzing high-resolution medical images, establishing a foundational approach for detecting cancer metastases and predicting diabetic eye disease severity.
The researcher advanced video and image captioning by integrating linguistic knowledge and diverse object recognition, establishing a highly cited framework for multimodal description.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Subhashini Venugopalan's research
Subhashini Venugopalan is a researcher in Natural Language Processing, Computer Vision and Machine Learning at University of Texas at Austin. Their work has been cited 32,964 times across 84 publications (h-index 36), according to Google Scholar.
Their most-cited work, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs” (2016), has accumulated 10,077 citations. Other influential works include “Long-term recurrent convolutional networks for visual recognition and description” (2015) with 8,920 citations and “Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities” (2025) with 4,560 citations.
Citations of Subhashini Venugopalan'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.
