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Researcher · Macao Polytechnic University · China

Si Shi

Si Shi publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, on topics such as Advanced Graph Neural Networks, Multimodal Machine Learning Applications and Recommender Systems and Techniques.

ORCID 0000-0002-6952-9848 OpenAlex
World rank
#2,275,312
of 7,710,434 ranked researchers
Rank in China
#353,639
of 978,568
Works
19
Citations
376
Citations per work
19.8
h-index
5
i10-index 3Top 82% in Artificial Intelligence
Si Shi

What does Si Shi research?

Si Shi's work is mostly in Computer Science (66%), Physics and Astronomy (6%), Engineering (4%).

Physical Sciences: 14 works · 74.6% of their workComputer Science: 12 works · 65.5% of their workArtificial Intelligence: 8 works · 40.0% of their workComputer Vision and Pattern Recognition: 3 works · 18.2% of their workInformation Systems: 1 works · 7.3% of their workPhysics and Astronomy: 1 works · 5.5% of their workStatistical and Nonlinear Physics: 1 works · 5.5% of their workEngineering: 1 works · 3.6% of their workControl and Systems Engineering: 1 works · 3.6% of their workEverything else: 4 works · 21.8% of their workSocial Sciences: 1 works · 3.6% of their workBusiness, Management and Accounting: 1 works · 3.6% of their workAccounting: 1 works · 3.6% of their workTHEIR WORKSi Shi19works
Physical Sciences75%Social Sciences4%

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 Science1265.5%
    2. Physics and Astronomy15.5%
    3. Engineering13.6%
  2. Everything else421.8%
    1. Business, Management and Accounting13.6%
  1. 1Artificial Intelligence40% of their works
  2. 2Computer Vision and Pattern Recognition18% of their works
  3. 3Information Systems7% of their works
  4. 4Statistical and Nonlinear Physics6% of their works
  5. 5Control and Systems Engineering4% of their works
  6. 6Accounting4% of their works

Research topics

Which keywords describe Si Shi's research?

The keywords of their largest research topics: Deep Learning, Neural Networks, Graph Neural Networks, Knowledge Graph Embedding, Image Captioning, Visual Question Answering, Machine Learning and Collaborative Filtering.

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 19 words, with their numbers
  1. Deep Learning266 topics
  2. Neural Networks123 topics
  3. Graph Neural Networks81 topic
  4. Knowledge Graph Embedding81 topic
  5. Image Captioning61 topic
  6. Visual Question Answering61 topic
  7. Machine Learning52 topics
  8. Collaborative Filtering41 topic
  9. Matrix Factorization41 topic
  10. Electronic Health Records31 topic
  11. Healthcare31 topic
  12. Information Visualization31 topic
  13. Model Reduction31 topic
  14. Partial Differential Equations31 topic
  15. Visual Analytics31 topic
  16. Machine Translation21 topic
  17. Opinion Mining21 topic
  18. Sentiment Analysis21 topic
  19. Word Representation21 topic

Where does Si Shi work?

Si Shi's main affiliation in the publication record is Macao Polytechnic University, China.

How many publications and citations does Si Shi have?

OpenAlex counts 19 works and 376 citations for Si Shi, an h-index of 5, ranking #2,275,312 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.

Other researchers named Si Shi

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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