Tin Lai

624 citations
26 papers · 326 · h-index 11

Impact in

Papers in

Tin Lai

24 papers receiving 319 citations

Peers

Tin Lai
Comparison fields: 5 of 64
  • Computer Vision and Pattern Recognition 146
  • Aerospace Engineering 104
  • Health Informatics 5
  • Applied Psychology 17
  • Building and Construction 33
Replace Tareq Alhmiedat with:
Tareq Alhmiedat Saudi Arabia
Lixing Liu China
Josef Pauli Germany
Fei Meng China
Lúcio F. Vismari Brazil
Zhiqiang Jian China
D. Pagac Australia
Ciarán Eising Ireland
Viet Nguyen Vietnam
Rachid Belaroussi France
Tin Lai relative to Tareq Alhmiedat Saudi Arabia Tareq Alhmiedat's profile →
Citations per field
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Citations per year

Countries citing papers authored by Tin Lai

Since Specialization
Citations

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

Fields of papers citing papers by Tin Lai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Tin Lai, 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 Tin Lai Line = papers co-authored together Tin Lai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201957
2 201934
3 202030
4 201828
5 202325
6 202423
7 202220
8 202420
9 202213
10 202113
11 202113
12 20239
13 20218
14 20227
15 20216
16 20225
17 20215
18 20242
19 20222
20 20212

About Tin Lai

Tin Lai is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Artificial Intelligence, Automotive Engineering and Control and Systems Engineering, having authored 26 papers that have together received 326 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (11 papers), Robotics and Sensor-Based Localization (9 papers), Autonomous Vehicle Technology and Safety (4 papers), Robot Manipulation and Learning (3 papers), Machine Learning in Healthcare (2 papers), Anomaly Detection Techniques and Applications (2 papers), UAV Applications and Optimization (2 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (146 citations), Aerospace Engineering (104 citations), Health Informatics (5 citations), Applied Psychology (17 citations) and Building and Construction (33 citations). Tin Lai has collaborated with scholars based in Australia, United States and Switzerland. Frequent co-authors include Fábio Ramos, Farnaz Farid, Kotaro Nakayama, Yutaka Matsuo, Helmut Prendinger, Fariza Sabrina, Ziqi Wang, Sara Shirowzhan, Sayka Jahan and Kakarla Raghava Reddy. Their work appears in journals such as IEEE Robotics and Automation Letters, Sensors, Cybersecurity, IEEE Transactions on Artificial Intelligence and Buildings.

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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