Dafa Li

685 citations
43 papers · 494 · h-index 13

Impact in

Papers in

Dafa Li

39 papers receiving 479 citations

Peers

Dafa Li
Comparison fields: 5 of 51
  • Artificial Intelligence 342
  • Atomic and Molecular Physics, and Optics 290
  • Computational Mathematics 4
  • Automotive Engineering 37
  • Computational Theory and Mathematics 43
Replace Debasis Sarkar with:
Debasis Sarkar India
Matthias M. Müller Germany
Sanjoy Mandal India
Neil G. Dickson Canada
Oliver Sander Germany
Ruixing Long United States
Alexander Hentschel Germany
Tianyi Peng China
С. Н. Андрианов Russia
Dafa Li relative to Debasis Sarkar India Debasis Sarkar's profile →
Citations per field
00.5×4.8×
Debasis Sarkar · 1×
Citations per year

Countries citing papers authored by Dafa Li

Since Specialization
Citations

This map shows the geographic impact of Dafa Li's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dafa Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dafa Li more than expected).

Fields of papers citing papers by Dafa Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dafa Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dafa Li. The network helps show where Dafa Li may publish in the future.

Co-authors

The 25 scholars most cited alongside Dafa Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dafa Li Line = papers co-authored together Dafa Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017124
2 201237
3 200729
4 202425
5 200725
6 202023
7 200621
8 200919
9 201218
10 202116
11 200114
12 200313
13 202213
14 200612
15 200912
16 20068
17 20098
18 20257
19 20206
20 20106

About Dafa Li

Dafa Li is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Mechanical Engineering, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 43 papers that have together received 494 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (26 papers), Quantum Computing Algorithms and Architecture (23 papers), Quantum Mechanics and Applications (21 papers), Advanced biosensing and bioanalysis techniques (3 papers), Hydraulic and Pneumatic Systems (3 papers), DNA and Biological Computing (3 papers), Modular Robots and Swarm Intelligence (3 papers) and Optical measurement and interference techniques (3 papers). The work is most often cited by research in Artificial Intelligence (342 citations), Atomic and Molecular Physics, and Optics (290 citations), Computational Mathematics (4 citations), Automotive Engineering (37 citations) and Computational Theory and Mathematics (43 citations). Dafa Li has collaborated with scholars based in China and United States. Frequent co-authors include Xiangrong Li, Hongtao Huang, Xinxin Li, Huanlong Liu, Jiawei Wang, Xiangrong Li, Xiang Qian, Weihua Gui, Xiaohao Wang and Xinghui Li. Their work appears in journals such as Physical Review A, Quantum Information Processing, Quantum Information and Computation, Biosystems and Physics Letters A.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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