Jason Lin

1.2k citations
55 papers · 932 · h-index 17

Impact in

Papers in

Jason Lin

50 papers receiving 909 citations

Peers

Jason Lin
Comparison fields: 5 of 73
  • Artificial Intelligence 679
  • Atomic and Molecular Physics, and Optics 579
  • Statistics, Probability and Uncertainty 69
  • Statistics and Probability 44
  • Computer Vision and Pattern Recognition 97
Replace A.M. Colla with:
A.M. Colla Italy
Efstathia Bura United States
Zichang He United States
Nan Zhao China
Annalisa Riccardi United Kingdom
Pier Luigi Novi Inverardi Italy
Rajan Srinivasan Netherlands
Ye Liu China
Shyh-Rong Lay United States
Jason Lin relative to A.M. Colla Italy A.M. Colla's profile →
Citations per field
00.5×10×20×34.5×
A.M. Colla · 1×
Citations per year

Countries citing papers authored by Jason Lin

Since Specialization
Citations

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

Fields of papers citing papers by Jason Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011195
2 201175
3 201257
4 201252
5 201048
6 201241
7 200341
8 200341
9 202134
10 201334
11 202133
12 202227
13 201226
14 202121
15 202218
16 201416
17 200316
18 202213
19 202212
20 202410

About Jason Lin

Jason Lin is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Control and Systems Engineering, having authored 55 papers that have together received 932 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (28 papers), Quantum Mechanics and Applications (27 papers), Quantum Computing Algorithms and Architecture (27 papers), Natural Language Processing Techniques (5 papers), Advanced Steganography and Watermarking Techniques (4 papers), Mathematics, Computing, and Information Processing (4 papers), Digital Media Forensic Detection (4 papers) and Advanced Statistical Process Monitoring (3 papers). The work is most often cited by research in Artificial Intelligence (679 citations), Atomic and Molecular Physics, and Optics (579 citations), Statistics, Probability and Uncertainty (69 citations), Statistics and Probability (44 citations) and Computer Vision and Pattern Recognition (97 citations). Jason Lin has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Tzonelih Hwang, Chun‐Wei Yang, Chia‐Wei Tsai, Kuen‐Suan Chen, Scott Satkin, Martial Hebert, Daniel Miller Runfola, Yao‐Chung Chang, Peter Kemper and Ruay-Shiung Chang. Their work appears in journals such as Quantum Information Processing, Optics Communications, The International Journal of Advanced Manufacturing Technology, EPJ Quantum Technology and Information Sciences.

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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