Song Lan

972 citations
29 papers · 733 · h-index 11

Impact in

Papers in

Song Lan

26 papers receiving 709 citations

Peers

Song Lan
Comparison fields: 5 of 41
  • Statistical and Nonlinear Physics 333
  • Atomic and Molecular Physics, and Optics 397
  • Artificial Intelligence 338
  • Computational Theory and Mathematics 154
  • Computer Vision and Pattern Recognition 84
Replace Xuelin Yang with:
Xuelin Yang China
Mariusz Jakubowski United States
Frédéric Dupuis Switzerland
Terry Farrelly Germany
Haozhen Situ China
Kazuya Amano Japan
Anke Zhao China
Edward Grant United Kingdom
Hiroyuki Someya Japan
Rahul Jain Singapore
Song Lan relative to Xuelin Yang China Xuelin Yang's profile →
Citations per field
00.5×1.5×2.3×
Xuelin Yang · 1×
Citations per year

Countries citing papers authored by Song Lan

Since Specialization
Citations

This map shows the geographic impact of Song Lan'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 Song Lan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Song Lan more than expected).

Fields of papers citing papers by Song Lan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Song Lan. 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 Song Lan. The network helps show where Song Lan may publish in the future.

Co-authors

The 25 scholars most cited alongside Song Lan, 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 Song Lan Line = papers co-authored together Song Lan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013124
2 2013122
3 1999103
4 201494
5 199976
6 200051
7 199950
8 200236
9 199721
10 200011
11 200211
12 20186
13 20144
14 20144
15 20233
16 20213
17 20122
18
[Screening and structure analysis of the aptamer target to Escherichia coli tolC protein].
20142
19 20242
20
Coronary artery imaging with dual-source CT:initial experience
20011

About Song Lan

Song Lan is a scholar working on Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics, Artificial Intelligence, Electrical and Electronic Engineering and Aerospace Engineering, having authored 29 papers that have together received 733 indexed citations. Recurring topics across this work include Advanced Fiber Laser Technologies (9 papers), Nonlinear Photonic Systems (8 papers), Photorefractive and Nonlinear Optics (6 papers), Quantum Computing Algorithms and Architecture (5 papers), Quantum Information and Cryptography (4 papers), Antenna Design and Analysis (3 papers), Quantum-Dot Cellular Automata (3 papers) and Microwave Engineering and Waveguides (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (333 citations), Atomic and Molecular Physics, and Optics (397 citations), Artificial Intelligence (338 citations), Computational Theory and Mathematics (154 citations) and Computer Vision and Pattern Recognition (84 citations). Song Lan has collaborated with scholars based in China, United States and Israel. Frequent co-authors include Ri‐Gui Zhou, Qingxin Zhu, Hai-Sheng Li, Mordechai Segev, Ming-Feng Shih, J. A. Giordmaine, Charalambos Anastassiou, Zhigang Chen, Chenyi Shen and Hou Ian. Their work appears in journals such as Optics Letters, Quantum Information Processing, Information Sciences, Applied Physics Letters and Electronics.

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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