Ramakrishna Vedantam: h-index, Total Citations, and Citation Map
Ramakrishna Vedantam's h-index is 19 (20 i10-index, 56,302+ total citations across 23+ publications) according to Google Scholar as of July 2026. Ramakrishna Vedantam is affiliated with Flagship Pioneering. ex:Meta AI (FAIR); Georgia Tech.
Ramakrishna Vedantam is a researcher affiliated with Flagship Pioneering. ex:Meta AI (FAIR); Georgia Tech, specializing in Deep Learning, Computer Vision, Machine Learning. Their work has been cited 56,302 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Ramakrishna Vedantam's Citation Metrics
Bibliometric impact based on 23 indexed publications.
- H-Index
- 19
- i10-Index
- 20
- Total Citations
- 56,302
- Citing Countries
- 54
As of July 2026.
Ramakrishna Vedantam has an h-index of 19 and 56,302 total citations across 23 publications, with research cited by institutions in 54 countries.
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Global Impact Map
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Grad-CAM: Why did you say that?
201643,559
Top Citing Countries
Top Citing Institutions
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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.
54 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered Grad-CAM for visualizing deep learning decisions, establishing a foundational method for model interpretability that has been widely adopted and extended by the independent research community.
The researcher advanced object counting in complex scenes and extended this framework to benchmark concept learning under uncertainty, establishing a foundational line of work in robust visual perception.
The researcher pioneered visually grounded word embeddings via Visual Word2Vec, establishing a framework for multimodal semantic representation that was subsequently extended to auditory domains.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Ramakrishna Vedantam's research
Ramakrishna Vedantam is a researcher in Deep Learning, Computer Vision and Machine Learning at Flagship Pioneering. ex:Meta AI (FAIR); Georgia Tech. Their work has been cited 56,302 times across 23 publications (h-index 19), according to Google Scholar.
Their most-cited work, “Grad-CAM: Why did you say that?” (2016), has accumulated 43,559 citations. Other influential works include “CIDEr: Consensus-based Image Description Evaluation” (2014) with 7,364 citations and “Microsoft coco captions: Data collection and evaluation server” (2015) with 3,701 citations.
Citations of Ramakrishna Vedantam'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.
