Haonan Wang
- Artificial Intelligence top 10%
- Sociology and Political Science
- Computer Vision and Pattern Recognition top 10%
- Marketing top 10%
- Information Systems and Management top 5%
- Co-authors
- J. S. MarronDerek L. SondereggerWilliam H. ClementsBarry R. NoonMaria Giovanna RanalliZhicong LuRoger WattenhoferYe Wang
- Topics
- Topic Modeling (4 papers)Blind Source Separation Techniques (4 papers)Soil Geostatistics and Mapping (4 papers)
- Journals
- Journal of the American Statistical AssociationScientific ReportsChemical Engineering Journal
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Haonan Wang
70 papers receiving 786 citations
Peers
Comparison fields: 5 of 139
- Artificial Intelligence 189
- Sociology and Political Science 105
- Computer Vision and Pattern Recognition 70
- Marketing 69
- Information Systems and Management 68
Countries citing papers authored by Haonan Wang
This map shows the geographic impact of Haonan Wang'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 Haonan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haonan Wang more than expected).
Fields of papers citing papers by Haonan Wang
This network shows the impact of papers produced by Haonan Wang. 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 Haonan Wang. The network helps show where Haonan Wang may publish in the future.
Co-authorship network of co-authors of Haonan Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Haonan Wang. A scholar is included among the top collaborators of Haonan Wang 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 Haonan Wang. Haonan Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 3 | |
| 9 | 11 | |
| 10 | 1 | |
| 11 | 16 | |
| 12 | 0 | |
| 13 | 5 | |
| 14 | Exploring Explainable Selection to Control Abstractive Generation | 3 |
| 15 | 7 | |
| 16 | 5 | |
| 17 | 13 | |
| 18 | 50 | |
| 19 | 6 | |
| 20 | 41 |
About Haonan Wang
Haonan Wang is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 79 papers that have together received 824 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Blind Source Separation Techniques (4 papers) and Soil Geostatistics and Mapping (4 papers). The work is most often cited by research in Information Systems and Management (68 citations), Marketing (69 citations) and Statistics and Probability (58 citations). Haonan Wang has collaborated with scholars based in United States, China and Australia. Frequent co-authors include J. S. Marron, Derek L. Sonderegger, William H. Clements, Barry R. Noon, Maria Giovanna Ranalli, Zhicong Lu, Roger Wattenhofer, Ye Wang, Peng Cao and Burcu Aydın. Their work appears in journals such as Journal of the American Statistical Association, Scientific Reports and Chemical Engineering Journal.
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.