Miu-Ling Lam

822 citations
39 papers · 559 · h-index 14

Impact in

Papers in

Miu-Ling Lam

39 papers receiving 536 citations

Peers

Miu-Ling Lam
Comparison fields: 5 of 83
  • Media Technology 97
  • Computer Graphics and Computer-Aided Design 29
  • Human-Computer Interaction 43
  • Computer Vision and Pattern Recognition 157
  • Control and Systems Engineering 123
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Jeremy Maitin-Shepard United States
Seokhee Jeon South Korea
S. Inokuchi Japan
Hao Yan China
Mingsi Tong China
Yizhe Chang United States
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Hideaki Kimata Japan
Sung‐Il Chien South Korea
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Citations per field
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Citations per year

Countries citing papers authored by Miu-Ling Lam

Since Specialization
Citations

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

Fields of papers citing papers by Miu-Ling Lam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Miu-Ling Lam. 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 Miu-Ling Lam. The network helps show where Miu-Ling Lam may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004105
2 202257
3 202131
4 201630
5 201429
6 201427
7 201025
8 201725
9 199823
10 201321
11 202017
12 200616
13 201914
14 202013
15 202012
16 200810
17 200710
18 20159
19 20128
20 20148

About Miu-Ling Lam

Miu-Ling Lam is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering, Computer Graphics and Computer-Aided Design, Media Technology and Cell Biology, having authored 39 papers that have together received 559 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (7 papers), Cellular Mechanics and Interactions (6 papers), Computer Graphics and Visualization Techniques (6 papers), Modular Robots and Swarm Intelligence (5 papers), Advanced Optical Imaging Technologies (4 papers), 3D Printing in Biomedical Research (4 papers), Advanced Image Processing Techniques (4 papers) and Virtual Reality Applications and Impacts (3 papers). The work is most often cited by research in Media Technology (97 citations), Computer Graphics and Computer-Aided Design (29 citations), Human-Computer Interaction (43 citations), Computer Vision and Pattern Recognition (157 citations) and Control and Systems Engineering (123 citations). Miu-Ling Lam has collaborated with scholars based in Hong Kong, United States and China. Frequent co-authors include Bin Chen, Derui Ding, Yi‐Hsin Liu, Ting‐Hsuan Chen, Jizhou Li, Yunhui Liu, Kit Yung Lam, Yuanye Bao, Ting Xu and Minghui Wu. Their work appears in journals such as Nature Communications, Optics Express, Scientific Reports, Biotechnology and Bioengineering and ACS Biomaterials Science & Engineering.

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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