Hui Guan
Impact in
- Ocean Engineering top 5%
- Enhanced Oil Recovery Techniques
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques
Papers in
-
- Domain Adaptation and Few-Shot Learning 6
- Adversarial Robustness in Machine Learning 5
-
- Advanced Neural Network Applications 9
- Advanced Image and Video Retrieval Techniques 6
- Co-authors
- Baowei Wang (2 shared papers)Naixue Xiong (1 shared paper)Xipeng Shen (16 shared papers)K. S. Sorbie (4 shared papers)Dermot F. Brougham (1 shared paper)Marco Serafini (3 shared papers)K. J. Packer (1 shared paper)Yang Shen (4 shared papers)
- Journals
- IEEE Access (3 papers)Petroleum Exploration and Development (2 papers)Proceedings of the VLDB Endowment (2 papers)SPE Production & Operations (1 paper)Communications of the ACM (1 paper)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Hui Guan
69 papers receiving 524 citations
Peers
Comparison fields: 5 of 89
- Ocean Engineering 103
- Hardware and Architecture 40
- Mechanics of Materials 142
- Nuclear and High Energy Physics 62
- Computer Vision and Pattern Recognition 89
Countries citing papers authored by Hui Guan
This map shows the geographic impact of Hui Guan'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 Hui Guan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hui Guan more than expected).
Fields of papers citing papers by Hui Guan
This network shows the impact of papers produced by Hui Guan. 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 Hui Guan. The network helps show where Hui Guan may publish in the future.
Co-authors
The 25 scholars most cited alongside Hui Guan, 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 75 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 95 | |
| 2 | 2002 | 67 | |
| 3 | 2015 | 37 | |
| 4 | 2021 | 34 | |
| 5 | 2018 | 33 | |
| 6 | 2019 | 21 | |
| 7 | 2014 | 18 | |
| 8 | 2016 | 16 | |
| 9 | 2021 | 15 | |
| 10 | 2021 | 14 | |
| 11 | 2023 | 13 | |
| 12 | 2024 | 10 | |
| 13 | 2014 | 9 | |
| 14 | 2020 | 9 | |
| 15 | 2018 | 8 | |
| 16 | 2019 | 7 | |
| 17 | 2019 | 7 | |
| 18 | 2023 | 7 | |
| 19 | 2021 | 7 | |
| 20 | 2003 | 6 |
About Hui Guan
Hui Guan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Computer Networks and Communications and Mechanics of Materials, having authored 75 papers that have together received 548 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (9 papers), Software Engineering Research (9 papers), Hydrocarbon exploration and reservoir analysis (8 papers), Domain Adaptation and Few-Shot Learning (6 papers), Advanced Image and Video Retrieval Techniques (6 papers), Information and Cyber Security (5 papers), Calcium Carbonate Crystallization and Inhibition (5 papers) and Adversarial Robustness in Machine Learning (5 papers). The work is most often cited by research in Ocean Engineering (103 citations), Hardware and Architecture (40 citations), Mechanics of Materials (142 citations), Nuclear and High Energy Physics (62 citations) and Computer Vision and Pattern Recognition (89 citations). Hui Guan has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Baowei Wang, Naixue Xiong, Xipeng Shen, K. S. Sorbie, Dermot F. Brougham, Marco Serafini, K. J. Packer, Yang Shen, Fudong Zhang and Hongji Yang. Their work appears in journals such as IEEE Access, Petroleum Exploration and Development, Proceedings of the VLDB Endowment, SPE Production & Operations and Communications of the ACM.
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.