Weifu Li
Impact in
- Structural Biology top 5%
- Metals and Alloys top 10%
Papers in
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- Face and Expression Recognition 5
-
- High Temperature Alloys and Creep 8
- Metallurgical Processes and Thermodynamics 8
- Microstructure and Mechanical Properties of Steels 6
- Co-authors
- Hua Han (17 shared papers)Ying Ren (6 shared papers)Lifeng Zhang (5 shared papers)Qiwei Xie (11 shared papers)Xi Chen (2 shared papers)Lin Li (4 shared papers)Lina Zhang (5 shared papers)Zijun Qin (5 shared papers)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (3 papers)Metals (2 papers)Molecular Plant (2 papers)steel research international (2 papers)Scripta Materialia (2 papers)
- Partner nations
- ChinaUnited StatesMacao
In The Last Decade
Weifu Li
61 papers receiving 800 citations
Weifu Li's Hit Papers
Peers
Comparison fields: 5 of 103
- Structural Biology 38
- Metals and Alloys 34
- Biophysics 57
- Mechanical Engineering 343
- Endocrine and Autonomic Systems 28
Countries citing papers authored by Weifu Li
This map shows the geographic impact of Weifu Li'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 Weifu Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weifu Li more than expected).
Fields of papers citing papers by Weifu Li
This network shows the impact of papers produced by Weifu Li. 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 Weifu Li. The network helps show where Weifu Li may publish in the future.
Co-authors
The 25 scholars most cited alongside Weifu Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 91 | |
| 2 | 2020 | 80 | |
| 3 | 2018 | 58 | |
| 4 | 2019 | 55 | |
| 5 | 2019 | 47 | |
| 6 | 2022 | 32 | |
| 7 | 2019 | 30 | |
| 8 | 2024 | 25 | |
| 9 | 2022 | 23 | |
| 10 | 2019 | 23 | |
| 11 | 2023 | 21 | |
| 12 | AutoGP: An intelligent breeding platform for enhancing maize genomic selection Hit paper breakdown → | 2025 | 19 |
| 13 | 2020 | 18 | |
| 14 | 2022 | 18 | |
| 15 | 2018 | 18 | |
| 16 | 2020 | 18 | |
| 17 | 2019 | 18 | |
| 18 | 2018 | 17 | |
| 19 | 2022 | 15 | |
| 20 | 2022 | 14 |
About Weifu Li
Weifu Li is a scholar working on Computer Vision and Pattern Recognition, Mechanical Engineering, Artificial Intelligence, Biophysics and Plant Science, having authored 66 papers that have together received 811 indexed citations. Recurring topics across this work include High Temperature Alloys and Creep (8 papers), Cell Image Analysis Techniques (8 papers), Metallurgical Processes and Thermodynamics (8 papers), Microstructure and Mechanical Properties of Steels (6 papers), Neural Networks and Applications (6 papers), Face and Expression Recognition (5 papers), Statistical Methods and Inference (4 papers) and Genetic Mapping and Diversity in Plants and Animals (4 papers). The work is most often cited by research in Structural Biology (38 citations), Metals and Alloys (34 citations), Biophysics (57 citations), Mechanical Engineering (343 citations) and Endocrine and Autonomic Systems (28 citations). Weifu Li has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Hua Han, Ying Ren, Lifeng Zhang, Qiwei Xie, Xi Chen, Lin Li, Lina Zhang, Zijun Qin, Zi Wang and Liming Tan. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Metals, Molecular Plant, steel research international and Scripta Materialia.
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.