Jin Shang: h-index, Total Citations, and Citation Map
Jin Shang's h-index is 7 (7 i10-index, 208+ total citations across 12+ publications) according to Google Scholar as of May 2026. Jin Shang is affiliated with Applied Scientist II, Amazon.
Jin Shang is a researcher affiliated with Applied Scientist II, Amazon, specializing in Machine Learning, Deep Learning, Recommender Systems. Their work has been cited 208 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Jin Shang's Citation Metrics
Bibliometric impact based on 12 indexed publications.
- H-Index
- 7
- i10-Index
- 7
- Total Citations
- 208
- Citing Countries
- 20
As of May 2026.
Jin Shang has an h-index of 7 and 208 total citations across 12 publications, with research cited by institutions in 20 countries.
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Global Impact Map
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Top Cited Works
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Effect of folded and crumpled morphologies of graphene oxide platelets on the mechanical performances of polymer nanocomposites
201560
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.
102 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a framework for modeling temporal user-item interactions using local low-rank Hawkes processes, subsequently extending this approach to geometric graph structures and fairness criteria.
The researcher established how folded and crumpled graphene oxide morphologies influence polymer nanocomposite mechanical performance, a foundational finding widely adopted by independent scholars.
The researcher advanced demographic inference in cross-domain recommender systems by pioneering knowledge transfer techniques to address data sparsity and cold-start challenges.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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