Ruslan Salakhutdinov

Ruslan Salakhutdinov is a researcher affiliated with UPMC Professor, Machine Learning Department, CMU, specializing in AI & Machine Learning. Their work has been cited 260,422 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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UPMC Professor, Machine Learning Department, CMU•

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Ruslan Salakhutdinov's Citation Metrics

Bibliometric impact based on 1 indexed publication.

H-Index: 1
i10-Index: 1
Total Citations: 260,422
Citing Countries: 1

As of April 2026.

Ruslan Salakhutdinov has an h-index of 1 and 260,422 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)

United States(US)

Top Citing Institutions(Limited)

Google DeepMind

New York University

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 (4 total citations).

Scholar

Ruslan Salakhutdinov

USA

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
Yuhuai Wu
7.0
Google DeepMind
USA· Cited your work 1 time
IndependentTop institution
#2
Quoc V. Le
7.0
Google DeepMind
USA· Cited your work 1 time
IndependentTop institution
#3
Thang Luong
7.0
Google DeepMind
USA· Cited your work 1 time
IndependentTop institution
#4
He He
7.0
New York University
USA· 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.

Cited by 5 papers

5 / 5 independent

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

(title pending — citation is still being processed)
· cites “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”
(title pending — citation is still being processed)
· cites “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”
(title pending — citation is still being processed)
· cites “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”
Solving olympiad geometry without human demonstrations
Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He + 1 more
Nature· cites “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”
(title pending — citation is still being processed)
· cites “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”