Qiang Ning
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Management Science and Operations Research
- Information Systems
- Media Technology
- Co-authors
- Dan RothHao WuChuchu FanKan ChenYao LuJiangtao WenTushar KhotBen Zhou
- Topics
- Natural Language Processing Techniques (7 papers)Topic Modeling (6 papers)Advanced Text Analysis Techniques (2 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionManagement Science and Operations Research
- Journals
- IEEE Transactions on Automatic ControlIEEE Signal Processing LettersProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- United StatesGermanyChina
In The Last Decade
Qiang Ning
10 papers receiving 186 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 155
- Computer Vision and Pattern Recognition 55
- Management Science and Operations Research 22
- Information Systems 20
- Media Technology 14
Countries citing papers authored by Qiang Ning
This map shows the geographic impact of Qiang Ning'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 Qiang Ning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qiang Ning more than expected).
Fields of papers citing papers by Qiang Ning
This network shows the impact of papers produced by Qiang Ning. 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 Qiang Ning. The network helps show where Qiang Ning may publish in the future.
Co-authorship network of co-authors of Qiang Ning
This figure shows the co-authorship network connecting the top 25 collaborators of Qiang Ning. A scholar is included among the top collaborators of Qiang Ning 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 Qiang Ning. Qiang Ning is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 11 | |
| 3 | 25 | |
| 4 | 8 | |
| 5 | 19 | |
| 6 | 9 | |
| 7 | 9 | |
| 8 | 88 | |
| 9 | 1 | |
| 10 | 23 |
About Qiang Ning
Qiang Ning is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 10 papers that have together received 194 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (7 papers), Topic Modeling (6 papers) and Advanced Text Analysis Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (155 citations), Computer Vision and Pattern Recognition (55 citations) and Management Science and Operations Research (22 citations). Qiang Ning has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Dan Roth, Hao Wu, Chuchu Fan, Kan Chen, Yao Lu, Jiangtao Wen, Tushar Khot, Ben Zhou, Ashish Sabharwal and Kyle Richardson. Their work appears in journals such as IEEE Transactions on Automatic Control, IEEE Signal Processing Letters and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.