Sharon Li: h-index, Total Citations, and Citation Map
Sharon Li's h-index is 60 (103 i10-index, 29,701+ total citations across 156+ publications) according to Google Scholar as of July 2026. Sharon Li is affiliated with Associate Professor, University of Wisconsin-Madison.
Sharon Li is a researcher affiliated with Associate Professor, University of Wisconsin-Madison, specializing in Machine learning, Reliable AI. Their work has been cited 29,701 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Sharon Li's Citation Metrics
Bibliometric impact based on 156 indexed publications. Of these, 18 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 60
- i10-Index
- 103
- Total Citations
- 29,701
- Citing Countries
- 52
As of July 2026.
Sharon Li has an h-index of 60 and 29,701 total citations across 156 publications, with research cited by institutions in 52 countries.
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We've mapped 5,000 of 29,701 citations for Sharon Li
We've shown the most-cited 5,000. Unlock the full crawl (28,444 more citations) to see every institution citing this scholar.
Global Impact Map
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Stacked Generative Adversarial Networks
20174,850
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
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.
369 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for understanding the learning dynamics of alignment with human feedback, subsequently extending this work to address generalization under noise and social reasoning.
The researcher advanced weakly supervised pretraining limits and extended these insights to process reward modeling with Q-value rankings, establishing a foundational trajectory in scalable learning.
The researcher introduced Stacked Generative Adversarial Networks, a foundational architecture that significantly advanced the field of generative modeling and image synthesis.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Sharon Li's research
Sharon Li is a researcher in Machine learning and Reliable AI at Associate Professor, University of Wisconsin-Madison. Their work has been cited 29,701 times across 156 publications (h-index 60), according to Google Scholar.
Their most-cited work, “Stacked Generative Adversarial Networks” (2017), has accumulated 4,850 citations. Other influential works include “Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks” (2018) with 3,434 citations and “Energy-based Out-of-distribution Detection” (2020) with 2,645 citations.
Citations of Sharon Li'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.
