Nvidia (United States)
In research, Nvidia (United States) stands highest in Computer Science (#211 of 4,417 worldwide), Physical Sciences (#757 of 10,670 worldwide) and Engineering (#613 of 5,901 worldwide), 2022-now. Relative to its size it is most specialised in Hardware and Architecture, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition (Hardware and Architecture is 56.5× its share of world research).
- World rank, 2022-now
- #1,196 of 22,385 · #1,125 all time
- Rank in United States
- #262 of 3,332
- Research works
- 2.6k ▲ 83% vs 2018-21
- Citations
- 92k 34.8 per fractional work
- Top-10% rate
- 19.6% all institutions 10.0%
- vs its own fields
- 1.60× 1.00× is exactly expected
- Open access
- 40% world 40%
- International
- 44% world 18%
What is Nvidia (United States) known for in research?
The fields where it stands highest, 2022-now, ranked among every institution above the floor in each field.
| Field | World rank | Where that sits | Top-10% rate | Works | All time |
|---|---|---|---|---|---|
| Computer ScienceField | #211 of 4,417 | 22.0% | 472 | #226 | |
| Physical SciencesDomain | #757 of 10,670 | 21.7% | 841 | #698 | |
| EngineeringField | #613 of 5,901 | 22.1% | 274 | #782 | |
| Electrical and Electronic EngineeringSubfield | #363 of 2,176 | 22.6% | 110 | #498 | |
| Artificial IntelligenceSubfield | #321 of 1,862 | 19.8% | 150 | #389 | |
| Computer Vision and Pattern RecognitionSubfield | #167 of 963 | 25.4% | 103 | #160 | |
| Hardware and ArchitectureSubfield | #13 of 41 | 26.0% | 64 | #43 | |
| Social SciencesDomain | #4,310 of 9,585 | 12.4% | 88 | #4254 |
Each strip is that field's whole ranked pool, with the notch where Nvidia (United States) sits in it, in the colour of the band that rank falls in. The track under the rate is the rate itself: it carries no average mark, because the average differs by field (from about 6% in Dentistry to 21% in Economics).
Which of those standings moved
The same fields on both windows: where it stood over the whole record, and where it stands in 2022-now. The distance is the story, and it is the one thing the two rank columns above cannot show.
- Computer Science#226 #211
- Physical Sciences#698 #757
- Engineering#782 #613
- Electrical and Electronic Engineering#498 #363
- Artificial Intelligence#389 #321
- Computer Vision and Pattern Recognition#160 #167
- Hardware and Architecture#43 #13
- Social Sciences#4,254 #4,310
A dot further right is a better standing. Fields it was not ranked in over the whole record are left out.
Which fields of science does Nvidia (United States) work in?
In 2022-now, 83% of research at Nvidia (United States) is in Physical Sciences, 9% in Social Sciences. Its largest fields are Computer Science (47%), Engineering (26%), Medicine (4%). It does 2.1× the world's share of Physical Sciences.
Each ring splits the one inside it: the four domains of science, their fields, their subfields. Size is fractional works in 2022-now. Every slice is a page; the interactive view zooms down to single topics and colours each by how much more of it this institution does than the world.
Its 4 domains and 26 fields, with their numbers
- Physical Sciences96382.5%
- Computer Science54646.8%
- Engineering30125.8%
- Physics and Astronomy312.6%
- Environmental Science282.4%
- Earth and Planetary Sciences262.2%
- Materials Science171.5%
- Mathematics100.8%
- Chemistry30.3%
- Chemical Engineering10.1%
- Energy00.0%
- Social Sciences1018.7%
- Social Sciences363.1%
- Decision Sciences201.7%
- Psychology131.1%
- Business, Management and Accounting111.0%
- Arts and Humanities110.9%
- Economics, Econometrics and Finance100.8%
- Health Sciences574.9%
- Medicine504.3%
- Health Professions70.6%
- Nursing00.0%
- Veterinary00.0%
- Dentistry00.0%
- Life Sciences453.9%
What does Nvidia (United States) specialise in?
Where its research is concentrated relative to its size: Hardware and Architecture takes 56.5× the share of its output that it takes of world research.
- Hardware and ArchitectureSubfield · 74.1 works56×
- Computer Graphics and Computer-Aided DesignSubfield · 22.0 works23×
- Computer Vision and Pattern RecognitionSubfield · 116.8 works7.9×
- Computer Networks and CommunicationsSubfield · 70.4 works6.8×
- Artificial IntelligenceSubfield · 175.0 works6.2×
- Computer ScienceField · 545.7 works6.0×
Location quotient, a volume reading rather than an impact one. It surfaces small, lopsided specialities.
Field profile
Location quotient across every field it publishes in: outside the ring is more than an institution of this size would be expected to publish, inside it is less.
Rings at 0.5×, 1× and 2×. Widest outward: Computer Science, 6.0×. Every wedge is a field page.
All 26 fields, with their numbers
- Computer Science6.01×546 works
- Decision Sciences1.78×20 works
- Engineering1.72×301 works
- Earth and Planetary Sciences1.40×26 works
- Physics and Astronomy1.18×31 works
- Mathematics0.72×10 works
- Neuroscience0.63×12 works
- Environmental Science0.49×28 works
- Biochemistry, Genetics and Molecular Biology0.45×28 works
- Materials Science0.42×17 works
- Economics, Econometrics and Finance0.38×10 works
- Psychology0.38×13 works
- Arts and Humanities0.32×11 works
- Business, Management and Accounting0.30×11 works
- Chemical Engineering0.28×1 works
- Social Sciences0.25×36 works
- Health Professions0.22×7 works
- Medicine0.22×50 works
- Chemistry0.18×3 works
- Veterinary0.11×0 works
- Agricultural and Biological Sciences0.08×4 works
- Pharmacology, Toxicology and Pharmaceutics0.08×0 works
- Nursing0.05×0 works
- Immunology and Microbiology0.04×0 works
- Energy0.04×0 works
- Dentistry0.02×0 works
- Computer Science 6.0×
- Decision Sciences 1.8×
- Engineering 1.7×
- Earth and Planetary Sciences 1.4×
- Physics and Astronomy 1.2×
- Mathematics 0.7×
- Neuroscience 0.6×
- Environmental Science 0.5×
- Biochemistry, Genetics and Molecular Biology 0.5×
- Materials Science 0.4×
- Economics, Econometrics and Finance 0.4×
- Psychology 0.4×
- Arts and Humanities 0.3×
- Business, Management and Accounting 0.3×
- Chemical Engineering 0.3×
- Social Sciences 0.2×
- Health Professions 0.2×
- Medicine 0.2×
- Chemistry 0.2×
- Veterinary 0.1×
- Agricultural and Biological Sciences 0.1×
- Pharmacology, Toxicology and Pharmaceutics 0.1×
- Nursing 0.1×
- Immunology and Microbiology 0.0×
- Energy 0.0×
- Dentistry 0.0×
Who are the top researchers at Nvidia (United States)?
Ranked on the quality score, Daguang Xu, Brucek Khailany and Pavlo A. Molchanov lead among researchers whose main affiliation is Nvidia (United States).
- 1
Daguang Xu
United States · #39,040 worldwide 18k citations · 248 works
- 2
Brucek Khailany
United States · #47,251 worldwide 7.9k citations · 124 works
- 3
Pavlo A. Molchanov
United States · #95,318 worldwide 7.6k citations · 191 works
- 4
Holger R. Roth
United States · #98,573 worldwide 24k citations · 269 works
- 5
Ziyue Xu
United States · #98,921 worldwide 12k citations · 184 works
- 6
Dong Yang
United States · #136,105 worldwide 10k citations · 196 works
- 7
Andriy Myronenko
United States · #161,063 worldwide 10k citations · 102 works
- 8
Niladrish Chatterjee
United States · #167,795 worldwide 2.1k citations · 40 works
- 9
Haoxing Mark Ren
United States · #223,541 worldwide 3.4k citations · 188 works
- 10
Yashraj Narang
United States · #234,753 worldwide 1.4k citations · 58 works
Which keywords describe research at Nvidia (United States)?
By fractional works in 2022-now, weighted toward what it does more of than the world: Deep Learning, Machine Learning, Neural Networks, Parallel Computing, Convolutional Neural Networks, High-Performance Computing, GPU Computing and Performance Optimization.
- Depth Estimation
- Distributed Storage
- Texture Analysis
- Speaker Verification
- Integrated Circuits
- Hidden Markov Models
- Cloud Computing
- Representation Learning
- Computer Graphics
- Image Recognition
- FPGA
- Resource Management
- Image Classification
- Data Mining
- Distributed Systems
- Big Data
- Performance Optimization
- High-Performance Computing
- Convolutional Neural Networks
- Machine Learning
- Deep Learning
- Neural Networks
- Parallel Computing
- GPU Computing
- Energy Efficiency
- Unsupervised Learning
- Deep Neural Networks
- Convolutional Networks
- Visualization
- Simulation
- Computer Vision
- Object Detection
- Rendering
- Fault Tolerance
- Power Optimization
- CMOS Technology
- Acoustic Modeling
- Network Coding
- Workflow Management
- Flash Memory
Size is fractional works in 2022-now in the topics tagged with each word; colour is the word's share of this institution's work against its share of the world's. The 40 words are chosen for being large and distinctive. Each links to the topic it comes from most.
All 40 words, with their numbers
- Deep Learning210▲ 5.2×72 topics
- Machine Learning121▲ 3.1×71 topics
- Neural Networks89▲ 4.9×29 topics
- Convolutional Neural Networks60▲ 7.0×14 topics
- Parallel Computing58▲ 42×4 topics
- High-Performance Computing51▲ 54×3 topics
- GPU Computing40▲ 88×1 topic
- Performance Optimization40▲ 54×1 topic
- Energy Efficiency38▲ 3.5×22 topics
- Big Data34▲ 3.1×19 topics
- Unsupervised Learning32▲ 18×3 topics
- Distributed Systems31▲ 15×6 topics
- Deep Neural Networks27▲ 19×3 topics
- Data Mining26▲ 3.0×8 topics
- Convolutional Networks25▲ 16×3 topics
- Image Classification25▲ 11×3 topics
- Visualization24▲ 36×3 topics
- Resource Management24▲ 12×2 topics
- Simulation24▲ 7.9×9 topics
- FPGA22▲ 50×3 topics
- Computer Vision21▲ 6.6×3 topics
- Image Recognition20▲ 12×1 topic
- Object Detection20▲ 13×1 topic
- Computer Graphics20▲ 67×1 topic
- Rendering20▲ 67×1 topic
- Representation Learning20▲ 11×3 topics
- Fault Tolerance19▲ 11×7 topics
- Cloud Computing19▲ 5.7×7 topics
- Power Optimization19▲ 61×2 topics
- Hidden Markov Models19▲ 28×3 topics
- CMOS Technology19▲ 20×5 topics
- Integrated Circuits18▲ 11×3 topics
- Acoustic Modeling18▲ 41×1 topic
- Speaker Verification18▲ 41×1 topic
- Network Coding18▲ 34×2 topics
- Texture Analysis17▲ 6.7×3 topics
- Workflow Management17▲ 20×2 topics
- Distributed Storage17▲ 55×1 topic
- Flash Memory17▲ 55×1 topic
- Depth Estimation17▲ 18×2 topics
Which research topics does Nvidia (United States) publish most on?
By volume in 2022-now: Parallel Computing and Optimization Techniques, Advanced Neural Network Applications, Computer Graphics and Visualization Techniques and Speech Recognition and Synthesis.
Area is fractional works; colour is the subfield each topic belongs to.
- 1 Parallel Computing and Optimization Techniques Hardware and Architecture 40 works
- 2 Advanced Neural Network Applications Computer Vision and Pattern Recognition 20 works
- 3 Computer Graphics and Visualization Techniques Computer Graphics and Computer-Aided Design 20 works
- 4 Speech Recognition and Synthesis Artificial Intelligence 18 works
- 5 Advanced Data Storage Technologies Computer Networks and Communications 17 works
- 6 Advanced Vision and Imaging Computer Vision and Pattern Recognition 15 works
- 7 Natural Language Processing Techniques Artificial Intelligence 15 works
- 8 VLSI and Analog Circuit Testing Hardware and Architecture 14 works
- 9 Topic Modeling Artificial Intelligence 14 works
- 10 3D Shape Modeling and Analysis Computational Mechanics 13 works
How open and international is its research?
Against the world's own shares: the tick on each track. Both are shares of its output, so they sit on one scale and can be read against each other as well as against the world.
World: 40% of research is openly available.
World: 18% is written across borders.
How has Nvidia (United States)'s research output changed?
Output in 2022-2025 was 83% higher than in 2018-2021.
The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.
Its share of all research
Growing output can still be a shrinking share: all of science grows. This is Nvidia (United States) as a percentage of every work published that year, 0.0043% in 2024.
Fractional works of Nvidia (United States) over all fractional works that year. How the balance of science has shifted.
How much of it reaches the world top 10%
Year by year, against all institutions in the same year.
Of each year's output, the share that reached the world's 10% most cited for its field and year. Peak 36.0% in 2010.
Other research institutions in Santa Clara
- Intel (United States)
- Santa Clara University
- Agilent Technologies (United States)
- Applied Materials (United States)
- GlobalFoundries (United States)
- Coherent (United States)
- Renesas Electronics (United States)
- Evans Analytical Group (United States)
Research measures only: rankings here say nothing about teaching, admissions or student experience. Comparable institutions and collaboration partners have tabs of their own, above.
Questions people ask
What does Nvidia Research publish research on?
In research, Nvidia (United States) stands highest in Computer Science (#211 of 4,417 worldwide), Physical Sciences (#757 of 10,670 worldwide) and Engineering (#613 of 5,901 worldwide), 2022-now. Relative to its size it is most specialised in Hardware and Architecture, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition (Hardware and Architecture is 56.5× its share of world research).
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