Mu Li

Mu Li is a researcher affiliated with Amazon, specializing in AI & Machine Learning. Their work has been cited 31,754 times. This profile visualizes their global influence, highlighting strong citation networks in HK.

AI & Machine Learning

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0i10-index

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0Works

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Amazon•

Google Scholar

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Mu Li's Citation Metrics

Bibliometric impact based on 1 indexed publication.

H-Index: 1
i10-Index: 1
Total Citations: 31,754
Citing Countries: 1

As of April 2026.

Mu Li has an h-index of 1 and 31,754 total citations across 1 publication, with research cited by institutions in 1 country.

Global Impact Map

Visualizing the geographic distribution of institutions that have cited your work.

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Top Citing Countries(Limited)

Hong Kong(HK)

Top Citing Institutions(Limited)

City University of Hong Kong

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.

Citer Influence Network

Visa-ready visualisation: each outer ring node is a country citing this scholar. Node size & edge weight scale with citing-paper count. Showing top 1 of 1 countries (3 total citations).

Scholar

Mu Li

Hong Kong

React Flow

Letter of Support — Candidate Recommenders

Ranked by EB-1A / O-1A relevance: independence from your home institution, affiliation with a top-ranked institution, repeat citations of your work, and recency. Reach out to the highest-scoring candidates to request support letters.

#1
Kin Wai Lau
7.0
City University of Hong Kong
Hong Kong· Cited your work 1 time
IndependentTop institution
#2
Lai-Man Po
7.0
City University of Hong Kong
Hong Kong· Cited your work 1 time
IndependentTop institution
#3
Yasar Abbas Ur Rehman
7.0
City University of Hong Kong
Hong Kong· Cited your work 1 time
IndependentTop institution

Scores combine 5 signals: independence (+3), prestige institution (+2), repeat citations (+2 per paper, cap 5), recent activity (+1), and geography diversity (+1.5). Identity matching uses name + institution; namesakes at the same org are merged.