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Researcher · Huawei Technologies (China) · China

Shangling Jui

Shangling Jui publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Media Technology, on topics such as Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications and Advanced Neural Network Applications.

ORCID 0000-0002-1047-4264 OpenAlex
World rank
#1,190,594
of 7,710,434 ranked researchers
Rank in China
#172,026
of 978,568
Works
66
Citations
1.1k
Citations per work
17.2
h-index
15
i10-index 19Top 22% in Artificial Intelligence
Shangling Jui

What does Shangling Jui research?

Shangling Jui's work is mostly in Computer Science (69%), Engineering (8%), Decision Sciences (3%).

Physical Sciences: 50 works · 76.5% of their workComputer Science: 46 works · 69.0% of their workArtificial Intelligence: 25 works · 38.5% of their workComputer Vision and Pattern Recognition: 20 works · 30.5% of their workEngineering: 5 works · 7.5% of their workMedia Technology: 3 works · 4.6% of their workElectrical and Electronic Engineering: 2 works · 2.9% of their workEverything else: 12 works · 18.3% of their workSocial Sciences: 2 works · 2.9% of their workDecision Sciences: 2 works · 2.9% of their workManagement Science and Operations Research: 2 works · 2.9% of their workLife Sciences: 2 works · 2.3% of their workBiochemistry, Genetics and Molecular Biology: 2 works · 2.3% of their workCancer Research: 2 works · 2.3% of their workTHEIR WORKShangling Jui66works
Life Sciences2%Physical Sciences76%Social Sciences3%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science4669.0%
    2. Engineering57.5%
  2. Everything else1218.3%
    1. Decision Sciences22.9%
    1. Biochemistry, Genetics and Molecular Biology22.3%
  1. 1Artificial Intelligence38% of their works
  2. 2Computer Vision and Pattern Recognition30% of their works
  3. 3Media Technology5% of their works
  4. 4Management Science and Operations Research3% of their works
  5. 5Electrical and Electronic Engineering3% of their works
  6. 6Cancer Research2% of their works

Research topics

Which keywords describe Shangling Jui's research?

The keywords of their largest research topics: Deep Learning, Representation Learning, Semi-Supervised Learning, Neural Networks, Domain Adaptation, Transfer Learning, Image Captioning and Visual Question Answering.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 20 words, with their numbers
  1. Deep Learning496 topics
  2. Representation Learning302 topics
  3. Semi-Supervised Learning302 topics
  4. Neural Networks273 topics
  5. Domain Adaptation251 topic
  6. Transfer Learning251 topic
  7. Image Captioning151 topic
  8. Visual Question Answering151 topic
  9. Convolutional Neural Networks91 topic
  10. Image Recognition91 topic
  11. Image Quality Assessment81 topic
  12. Perceptual Quality81 topic
  13. Image Fusion71 topic
  14. Multispectral71 topic
  15. Adversarial Examples61 topic
  16. Reinforcement Learning61 topic
  17. Robotics61 topic
  18. Robustness61 topic
  19. Graph Neural Networks51 topic
  20. Knowledge Graph Embedding51 topic

Where does Shangling Jui work?

Shangling Jui's main affiliation in the publication record is Huawei Technologies (China), China.

How many publications and citations does Shangling Jui have?

OpenAlex counts 66 works and 1,138 citations for Shangling Jui, an h-index of 15, ranking #1,190,594 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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