Samsung (United Kingdom)
In research, Samsung (United Kingdom) stands highest in Computer Science (#3,379 of 4,667 worldwide) and Physical Sciences (#11,609 of 12,888 worldwide), 2022–2025. Relative to its size it is most specialised in Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Science — Computer Vision and Pattern Recognition is 20.6× its share of world research.
- World rank, 2022–2025
- #23,058 of 28,054 · #19,694 all time
- Rank in United Kingdom
- #859 of 1,094
- Research works
- 417 ▲ 31% vs 2013–17
- Citations
- 5.9k 14.1 per fractional work
- Top-10% rate
- 18.1% record average 16.5%
- Open access
- 32% world 28%
What is Samsung (United Kingdom) known for in research?
The fields where it stands highest, 2022–2025, ranked among every institution above the floor in each field.
| Field | World rank | Where that sits | Top-10% rate | Works | All time |
|---|---|---|---|---|---|
| Computer ScienceField | #3,379 of 4,667 | 20.6% | 61 | #3661 | |
| Physical SciencesDomain | #11,609 of 12,888 | 17.1% | 79 | #11803 |
Each strip is that field’s whole ranked pool, with the notch where Samsung (United Kingdom) 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 world mark, because the world rate differs by field (from about 6% to 21% in this record).
What does Samsung (United Kingdom) specialise in?
Where its research is concentrated relative to its size: Computer Vision and Pattern Recognition takes 20.6× the share of its output that it takes of world research.
- Computer Vision and Pattern RecognitionSubfield · 23.3 works21×
- Artificial IntelligenceSubfield · 24.7 works11×
- Computer ScienceField · 60.6 works8.7×
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, 8.7×. Every wedge is a field page.
- Computer Science 8.7×
- Engineering 1.2×
- Neuroscience 0.9×
- Decision Sciences 0.8×
- Energy 0.6×
- Arts and Humanities 0.5×
- Biochemistry, Genetics and Molecular Biology 0.3×
- Pharmacology, Toxicology and Pharmaceutics 0.3×
- Psychology 0.3×
- Physics and Astronomy 0.2×
- Business, Management and Accounting 0.2×
- Economics, Econometrics and Finance 0.2×
- Mathematics 0.2×
- Materials Science 0.2×
- Immunology and Microbiology 0.2×
- Medicine 0.2×
- Health Professions 0.1×
- Social Sciences 0.1×
- Environmental Science 0.0×
- Agricultural and Biological Sciences 0.0×
Who are the top researchers at Samsung (United Kingdom)?
Ranked on the composite score, Adrian Bulat, Shangbin Wu and Stylianos I. Venieris lead among researchers whose main affiliation is Samsung (United Kingdom).
- 1 Adrian Bulat United Kingdom · #114,558 worldwide 446 citations · 31 works
- 2 Shangbin Wu United Kingdom · #442,318 worldwide 161 citations · 17 works
- 3 Stylianos I. Venieris United Kingdom · #762,518 worldwide 77 citations · 19 works
- 4 Mythri Hunukumbure United Kingdom · #803,745 worldwide 46 citations · 18 works
- 5 Yinan Qi United Kingdom · #1,411,125 worldwide 47 citations · 23 works
Which keywords describe research at Samsung (United Kingdom)?
By fractional works in 2022–2025, weighted toward what it does more of than the world: Deep Learning, Neural Networks, Convolutional Neural Networks, Unsupervised Learning, Representation Learning, Meta-Learning, Transfer Learning and Semi-Supervised Learning.
- Time Series
- Text Classification
- Feature Matching
- Cellular Networks
- Spectral Efficiency
- Depth Estimation
- Pose Estimation
- Generative Adversarial Networks
- Speech Enhancement
- Audio-Visual Speech Recognition
- Convolutional Networks
- Speaker Verification
- Object Detection
- Image Classification
- Deep Neural Networks
- Domain Adaptation
- Transfer Learning
- Representation Learning
- Unsupervised Learning
- Neural Networks
- Deep Learning
- Convolutional Neural Networks
- Machine Learning
- Meta-Learning
- Semi-Supervised Learning
- Few-Shot Learning
- Energy Efficiency
- Image Recognition
- Acoustic Modeling
- Classification
- Beamforming
- Image Captioning
- Visual Question Answering
- 5G Networks
- Feature Extraction
- Heterogeneous Networks
- Anomaly Detection
- Massive MIMO
- Local Descriptors
- Image Segmentation
Size is fractional works in 2022–2025 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 Learning32▲ 11×44 topics
- Neural Networks18▲ 13×19 topics
- Convolutional Neural Networks13▲ 20×10 topics
- Unsupervised Learning10▲ 75×3 topics
- Machine Learning9▲ 2.9×23 topics
- Representation Learning8▲ 66×3 topics
- Meta-Learning8▲ 105×2 topics
- Transfer Learning7▲ 43×3 topics
- Semi-Supervised Learning7▲ 66×3 topics
- Domain Adaptation6▲ 141×1 topic
- Few-Shot Learning6▲ 141×1 topic
- Deep Neural Networks5▲ 56×3 topics
- Energy Efficiency5▲ 5.6×10 topics
- Image Classification4▲ 27×2 topics
- Image Recognition4▲ 34×1 topic
- Object Detection4▲ 36×1 topic
- Acoustic Modeling4▲ 117×1 topic
- Speaker Verification4▲ 117×1 topic
- Classification4▲ 9.5×6 topics
- Convolutional Networks4▲ 28×3 topics
- Beamforming3▲ 34×3 topics
- Audio-Visual Speech Recognition3▲ 57×1 topic
- Image Captioning3▲ 64×1 topic
- Speech Enhancement3▲ 57×1 topic
- Visual Question Answering3▲ 39×1 topic
- Generative Adversarial Networks3▲ 35×2 topics
- 5G Networks2▲ 14×5 topics
- Pose Estimation2▲ 24×2 topics
- Feature Extraction2▲ 5.8×4 topics
- Depth Estimation2▲ 30×2 topics
- Heterogeneous Networks2▲ 26×2 topics
- Spectral Efficiency2▲ 40×2 topics
- Anomaly Detection2▲ 8.4×3 topics
- Cellular Networks2▲ 49×1 topic
- Massive MIMO2▲ 29×1 topic
- Feature Matching2▲ 39×1 topic
- Local Descriptors2▲ 39×1 topic
- Text Classification2▲ 15×3 topics
- Image Segmentation2▲ 17×2 topics
- Time Series2▲ 12×2 topics
Which research topics does Samsung (United Kingdom) publish most on?
By volume in 2022–2025: Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, Speech Recognition and Synthesis and Speech and Audio Processing.
Area is fractional works; colour is the subfield each topic belongs to.
- 1 Domain Adaptation and Few-Shot Learning Artificial Intelligence 6 works
- 2 Advanced Neural Network Applications Computer Vision and Pattern Recognition 4 works
- 3 Speech Recognition and Synthesis Artificial Intelligence 4 works
- 4 Speech and Audio Processing Signal Processing 3 works
- 5 Multimodal Machine Learning Applications Computer Vision and Pattern Recognition 3 works
- 6 Advanced MIMO Systems Optimization Electrical and Electronic Engineering 2 works
- 7 Advanced Image and Video Retrieval Techniques Computer Vision and Pattern Recognition 2 works
- 8 Generative Adversarial Networks and Image Synthesis Computer Vision and Pattern Recognition 2 works
- 9 Topic Modeling Artificial Intelligence 2 works
- 10 Advanced Vision and Imaging Computer Vision and Pattern Recognition 1 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: 28% of research is openly available.
World: 19% is written across borders.
How has Samsung (United Kingdom)'s research output changed?
Output in 2018–2022 was 31% higher than in 2013–2017.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Research measures only: rankings here say nothing about teaching, admissions or student experience. Comparable institutions and collaboration partners are in the interactive view on the map.