Shanghai University of Engineering Science
In research, Shanghai University of Engineering Science stands highest in Physical Sciences (#344 of 12,888 worldwide), Engineering (#203 of 6,636 worldwide) and Materials Science (#172 of 2,468 worldwide), 2022–2025. Relative to its size it is most specialised in Computer Vision and Pattern Recognition, Automotive Engineering and Polymers and Plastics — Computer Vision and Pattern Recognition is 5.6× its share of world research.
- World rank, 2022–2025
- #598 of 28,054 · #1,908 all time
- Rank in China
- #191 of 3,058
- Research works
- 16k ▲ 59% vs 2013–17
- Citations
- 138k 8.7 per fractional work
- Top-10% rate
- 18.2% record average 16.5%
- Open access
- 29% world 28%
What is Shanghai University of Engineering Science 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 |
|---|---|---|---|---|---|
| Physical SciencesDomain | #344 of 12,888 | 17.4% | 4,228 | #1334 | |
| EngineeringField | #203 of 6,636 | 16.7% | 2,059 | #830 | |
| Materials ScienceField | #172 of 2,468 | 16.2% | 529 | #846 | |
| Computer ScienceField | #340 of 4,667 | 18.0% | 998 | #660 | |
| Mechanical EngineeringSubfield | #100 of 1,351 | 14.7% | 386 | #433 | |
| Computer Vision and Pattern RecognitionSubfield | #78 of 941 | 16.8% | 354 | #348 | |
| Electrical and Electronic EngineeringSubfield | #239 of 2,318 | 18.0% | 369 | #836 | |
| Materials ChemistrySubfield | #176 of 1,556 | 17.0% | 266 | #758 | |
| Biomedical EngineeringSubfield | #232 of 1,568 | 16.8% | 239 | #881 | |
| Social SciencesDomain | #1,557 of 10,521 | 23.1% | 413 | #3274 |
Each strip is that field’s whole ranked pool, with the notch where Shanghai University of Engineering Science 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).
Which of those standings moved
The same fields on both windows: where it stood over the whole record, and where it stands in 2022–2025. The distance is the story, and it is the one thing the two rank columns above cannot show.
- Physical Sciences#1,334 #344
- Engineering#830 #203
- Materials Science#846 #172
- Computer Science#660 #340
- Mechanical Engineering#433 #100
- Computer Vision and Pattern Recognition#348 #78
- Electrical and Electronic Engineering#836 #239
- Materials Chemistry#758 #176
- Biomedical Engineering#881 #232
- Social Sciences#3,274 #1,557
A dot further right is a better standing. Fields it was not ranked in over the whole record are left out.
What does Shanghai University of Engineering Science specialise in?
Where its research is concentrated relative to its size: Computer Vision and Pattern Recognition takes 5.6× the share of its output that it takes of world research.
- Computer Vision and Pattern RecognitionSubfield · 354.2 works5.6×
- Automotive EngineeringSubfield · 137.3 works5.1×
- Polymers and PlasticsSubfield · 94.4 works4.7×
- Surfaces, Coatings and FilmsSubfield · 22.8 works4.6×
- Mechanical EngineeringSubfield · 385.8 works4.2×
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: Materials Science, 3.0×. Every wedge is a field page.
- Materials Science 3.0×
- Engineering 2.7×
- Computer Science 2.6×
- Chemical Engineering 2.5×
- Chemistry 1.8×
- Energy 1.7×
- Decision Sciences 1.4×
- Physics and Astronomy 1.2×
- Environmental Science 0.7×
- Mathematics 0.6×
- Business, Management and Accounting 0.6×
- Neuroscience 0.5×
- Earth and Planetary Sciences 0.5×
- Pharmacology, Toxicology and Pharmaceutics 0.5×
- Economics, Econometrics and Finance 0.4×
- Biochemistry, Genetics and Molecular Biology 0.4×
- Psychology 0.3×
- Health Professions 0.2×
- Social Sciences 0.2×
- Medicine 0.2×
- Arts and Humanities 0.1×
- Agricultural and Biological Sciences 0.1×
- Dentistry 0.1×
- Nursing 0.1×
- Immunology and Microbiology 0.1×
- Veterinary 0.0×
Who are the top researchers at Shanghai University of Engineering Science?
Ranked on the composite score, Honghao Gao, Hao Wang and Hengyun Zhang lead among researchers whose main affiliation is Shanghai University of Engineering Science.
- 1 Honghao Gao China · #118,094 worldwide 429 citations · 86 works
- 2 Hao Wang China · #184,595 worldwide 232 citations · 34 works
- 3 Hengyun Zhang China · #209,263 worldwide 307 citations · 48 works
- 4 Wei An China · #209,735 worldwide 353 citations · 66 works
- 5 Xijian Liu China · #213,802 worldwide 255 citations · 27 works
- 6 Jian Mao China · #300,912 worldwide 90 citations · 32 works
- 7 Li Zhao China · #309,663 worldwide 70 citations · 31 works
- 8 Jinguo Wang China · #322,419 worldwide 368 citations · 46 works
- 9 Jing Zheng China · #384,385 worldwide 117 citations · 26 works
- 10 Zhishui Yu China · #400,407 worldwide 229 citations · 37 works
Which keywords describe research at Shanghai University of Engineering Science?
By fractional works in 2022–2025, weighted toward what it does more of than the world: Deep Learning, Neural Networks, Mechanical Properties, Machine Learning, Microstructure, Convolutional Neural Networks, Energy Storage and Nanocomposites.
- Graph Convolutional Networks
- Material Characterization
- Semantic Segmentation
- Flexible Electronics
- Unsupervised Learning
- Semi-Supervised Learning
- Image Classification
- Machine Vision
- Representation Learning
- Collision Avoidance
- Image Processing
- Additive Manufacturing
- Autonomous Vehicles
- Heat Transfer
- Cathode Materials
- Nanomaterials
- Microstructure
- Convolutional Neural Networks
- Mechanical Properties
- Neural Networks
- Deep Learning
- Machine Learning
- Biomedical Applications
- Nanocomposites
- Energy Storage
- Graphene
- Lithium-ion Batteries
- Metal-Organic Frameworks
- Rechargeable Batteries
- Computer Vision
- Polymer Composites
- Multi-Agent Systems
- Electric Vehicles
- Information Retrieval
- Text Classification
- Energy Harvesting
- Image Recognition
- Object Detection
- Surface Roughness
- Path Planning
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 Learning684▲ 4.0×83 topics
- Neural Networks359▲ 4.7×37 topics
- Machine Learning290▲ 1.77×83 topics
- Mechanical Properties222▲ 4.6×38 topics
- Biomedical Applications176▲ 3.0×35 topics
- Convolutional Neural Networks157▲ 4.3×14 topics
- Nanocomposites154▲ 3.8×22 topics
- Microstructure153▲ 5.9×19 topics
- Energy Storage148▲ 4.5×12 topics
- Nanomaterials144▲ 2.7×19 topics
- Graphene135▲ 4.0×12 topics
- Cathode Materials109▲ 5.5×3 topics
- Lithium-ion Batteries96▲ 4.5×4 topics
- Heat Transfer82▲ 3.3×20 topics
- Metal-Organic Frameworks82▲ 3.9×6 topics
- Autonomous Vehicles79▲ 7.5×4 topics
- Rechargeable Batteries75▲ 5.6×2 topics
- Additive Manufacturing75▲ 4.8×8 topics
- Computer Vision74▲ 5.4×4 topics
- Image Processing73▲ 3.3×12 topics
- Polymer Composites72▲ 5.4×7 topics
- Collision Avoidance70▲ 7.7×4 topics
- Multi-Agent Systems69▲ 7.3×6 topics
- Representation Learning66▲ 9.5×3 topics
- Electric Vehicles64▲ 4.3×4 topics
- Machine Vision62▲ 5.5×6 topics
- Information Retrieval62▲ 5.7×4 topics
- Image Classification60▲ 7.0×3 topics
- Text Classification58▲ 7.4×3 topics
- Semi-Supervised Learning58▲ 10×3 topics
- Energy Harvesting54▲ 6.0×3 topics
- Unsupervised Learning54▲ 7.6×3 topics
- Image Recognition54▲ 8.1×2 topics
- Flexible Electronics53▲ 6.2×3 topics
- Object Detection53▲ 8.3×1 topic
- Semantic Segmentation53▲ 8.3×1 topic
- Surface Roughness50▲ 7.4×4 topics
- Material Characterization50▲ 8.3×3 topics
- Path Planning49▲ 7.6×3 topics
- Graph Convolutional Networks48▲ 8.7×2 topics
Which research topics does Shanghai University of Engineering Science publish most on?
By volume in 2022–2025: Advanced Neural Network Applications, Advancements in Battery Materials, Advanced Sensor and Energy Harvesting Materials and Topic Modeling.
Area is fractional works; colour is the subfield each topic belongs to.
- 1 Advanced Neural Network Applications Computer Vision and Pattern Recognition 53 works
- 2 Advancements in Battery Materials Electrical and Electronic Engineering 51 works
- 3 Advanced Sensor and Energy Harvesting Materials Biomedical Engineering 49 works
- 4 Topic Modeling Artificial Intelligence 48 works
- 5 Advanced Battery Technologies Research Automotive Engineering 38 works
- 6 Industrial Vision Systems and Defect Detection Industrial and Manufacturing Engineering 37 works
- 7 Additive Manufacturing Materials and Processes Mechanical Engineering 36 works
- 8 Robotic Path Planning Algorithms Computer Vision and Pattern Recognition 35 works
- 9 Supercapacitor Materials and Fabrication Electronic, Optical and Magnetic Materials 34 works
- 10 Advanced Battery Materials and Technologies Electrical and Electronic Engineering 34 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 Shanghai University of Engineering Science's research output changed?
Output in 2018–2022 was 59% 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.
Other research institutions in Shanghai
- Shanghai Jiao Tong University
- Tongji University
- Fudan University
- Shanghai University
- East China Normal University
- East China University of Science and Technology
- Donghua University
- University of Shanghai for Science and Technology
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.