Sanjeev Arora

Sanjeev Arora is a researcher affiliated with Professor of Computer Science, Princeton University, specializing in AI & Machine Learning. Their work has been cited 46,843 times. This profile visualizes their global influence, highlighting strong citation networks in US.

AI & Machine Learning

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0i10-index

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Professor of Computer Science, Princeton University•

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Sanjeev Arora's Citation Metrics

Bibliometric impact based on 1 indexed publication.

H-Index: 1
i10-Index: 1
Total Citations: 46,843
Citing Countries: 10

As of April 2026.

Sanjeev Arora has an h-index of 1 and 46,843 total citations across 1 publication, with research cited by institutions in 10 countries.

Global Impact Map

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

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Top Citing Countries(Limited)

United States(US)

Canada(CA)

Singapore(SG)

United States(US)

Switzerland(CH)

Germany(DE)

United Kingdom(GB)

Netherlands(NL)

Latvia(LV)

Czech Republic(CZ)

Top Citing Institutions(Limited)

IBM Research

University of Toronto

National University of Singapore

E.ON Digital Technology

Science and Technology Facilities Council

Los Alamos National Laboratory

Wells Fargo

Harvard University

University of Southern California

Airbus

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 10 of 10 countries (39 total citations).

Scholar

Sanjeev Arora

3910

USA

Canada

Singapore

Switzerland

United Kingdom

United States

Germany

Latvia

Netherlands

Czech Republic

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
Jakob S. Kottmann
7.0
University of Toronto
Canada· Cited your work 1 time
IndependentTop institution
#2
Thi Ha Kyaw
7.0
University of Toronto
Canada· Cited your work 1 time
IndependentTop institution
#3
Patrick Rebentrost
7.0
National University of Singapore
Singapore· Cited your work 1 time
IndependentTop institution
#4
Hermanni Heimonen
7.0
National University of Singapore
Singapore· Cited your work 1 time
IndependentTop institution
#5
Christa Zoufal
7.0
IBM Research
Switzerland· Cited your work 1 time
IndependentTop institution
#6
Stefan Woerner
7.0
IBM Research
Switzerland· Cited your work 1 time
IndependentTop institution
#7
Bruce G. Elmegreen
8.5
IBM Research
USA· Cited your work 1 time
IndependentTop institutionDiversifies geography
#8
Stefano Mensa
7.0
Science and Technology Facilities Council
United Kingdom· Cited your work 1 time
IndependentTop institution
#9
Benjamin C. B. Symons
7.0
Science and Technology Facilities Council
United Kingdom· Cited your work 1 time
IndependentTop institution
#10
Carleton Coffrin
8.5
Los Alamos National Laboratory
United States· Cited your work 1 time
IndependentTop institutionDiversifies geography

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.

Cited by 5 papers

5 / 5 independent

Citations flagged as non-independent share the scholar's home institution (Princeton University). EB-1A & O-1A petitions typically quote the independent-citation count as the stronger evidence of outside recognition.

Challenges and opportunities in quantum optimization
Amira Abbas, Andris Ambainis, Brandon Augustino, Andreas Bärtschi + 42 more
Nature Reviews Physics· cites “Computational Complexity: A Modern Approach”
Noisy intermediate-scale quantum algorithms
Kishor Bharti, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea + 10 more
Reviews of Modern Physics· cites “Computational Complexity: A Modern Approach”
(title pending — citation is still being processed)
· cites “Computational Complexity: A Modern Approach”
(title pending — citation is still being processed)
· cites “Computational Complexity: A Modern Approach”
Ising machines as hardware solvers of combinatorial optimization problems
Naeimeh Mohseni, Peter L. McMahon, Tim Byrnes
Nature Reviews Physics· cites “Computational Complexity: A Modern Approach”