Lan Wu

39 total papers · 479 total citations
25 papers, 390 citations indexed

About

Lan Wu is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Lan Wu has authored 25 papers receiving a total of 390 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 9 papers in Control and Systems Engineering and 8 papers in Artificial Intelligence. Recurrent topics in Lan Wu's work include Robotics and Sensor-Based Localization (6 papers), Gaussian Processes and Bayesian Inference (4 papers) and Robot Manipulation and Learning (4 papers). Lan Wu is often cited by papers focused on Robotics and Sensor-Based Localization (6 papers), Gaussian Processes and Bayesian Inference (4 papers) and Robot Manipulation and Learning (4 papers). Lan Wu collaborates with scholars based in Australia, China and Germany. Lan Wu's co-authors include Song Wang, Jiangjian Shi, Xueping Liu, Tianshi Qin, Peng Zhou, Fuzhi Huang, Jie Zhong, Saisai Li, Qingbo Meng and Yong Peng and has published in prestigious journals such as Advanced Energy Materials, Transportation Research Part C Emerging Technologies and IEEE Transactions on Robotics.

In The Last Decade

Lan Wu

22 papers receiving 385 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Lan Wu 272 186 137 40 39 25 390
Duo Xiao 154 0.6× 129 0.7× 21 0.2× 51 1.3× 32 0.8× 30 397
Ziyu Liu 102 0.4× 146 0.8× 62 0.5× 26 0.7× 21 0.5× 35 417
Sungmoon Joo 205 0.8× 85 0.5× 59 0.4× 46 1.1× 34 0.9× 24 357
Wenyan Guo 69 0.3× 90 0.5× 69 0.5× 12 0.3× 33 0.8× 19 417
Yanfang Chen 148 0.5× 105 0.6× 38 0.3× 9 0.2× 32 0.8× 45 340
Xia Cai 187 0.7× 114 0.6× 107 0.8× 13 0.3× 9 0.2× 18 416
Mingqiang Zhu 203 0.7× 111 0.6× 38 0.3× 9 0.2× 15 0.4× 24 375
Zehua Li 266 1.0× 69 0.4× 30 0.2× 29 0.7× 22 0.6× 20 374
Jianhao Liu 91 0.3× 50 0.3× 47 0.3× 27 0.7× 19 0.5× 26 358
Tao Shen 158 0.6× 83 0.4× 34 0.2× 55 1.4× 13 0.3× 25 437

Countries citing papers authored by Lan Wu

Since Specialization
Citations

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

Fields of papers citing papers by Lan Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lan Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Lan Wu. A scholar is included among the top collaborators of Lan Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lan Wu. Lan Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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