Shuming Shi
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 1%
- Automotive Engineering top 2%
- Computer Networks and Communications top 5%
- Topics
- Topic Modeling (106 papers)Natural Language Processing Techniques (101 papers)Multimodal Machine Learning Applications (37 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Shuming Shi
176 papers receiving 3.2k citations
Peers
Comparison fields: 5 of 123
- Artificial Intelligence 2.3k
- Computer Vision and Pattern Recognition 644
- Information Systems 547
- Automotive Engineering 358
- Computer Networks and Communications 243
Countries citing papers authored by Shuming Shi
This map shows the geographic impact of Shuming Shi'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 Shuming Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuming Shi more than expected).
Fields of papers citing papers by Shuming Shi
This network shows the impact of papers produced by Shuming Shi. 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 Shuming Shi. The network helps show where Shuming Shi may publish in the future.
Co-authorship network of co-authors of Shuming Shi
This figure shows the co-authorship network connecting the top 25 collaborators of Shuming Shi. A scholar is included among the top collaborators of Shuming Shi 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 Shuming Shi. Shuming Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 48 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 4 | |
| 8 | 5 | |
| 9 | 30 | |
| 10 | 1 | |
| 11 | 12 | |
| 12 | 42 | |
| 13 | 83 | |
| 14 | Parameter Identification Method for the Tire Cornering Stiffness of Model Vehicle | 2 |
| 15 | 0 | |
| 16 | Nonlinear Evidence Fusion and Propagation for Hyponymy Relation Mining | 13 |
| 17 | Microsoft Research Asia at the Web Track of TREC 2009 | 13 |
| 18 | Microsoft Research Asia at Web Track and Terabyte Track of TREC 2004. | 40 |
| 19 | 1 | |
| 20 | METHODS OF CALCULATION OF COLLISION VELOCITY OF MOTOR VEHICLES USING THE LAW OF MOMENTUM CONSERVATION | 0 |
About Shuming Shi
Shuming Shi is a scholar working on Artificial Intelligence, Automotive Engineering and Computer Vision and Pattern Recognition, having authored 184 papers that have together received 3.4k indexed citations. Recurring topics across this work include Topic Modeling (106 papers), Natural Language Processing Techniques (101 papers) and Multimodal Machine Learning Applications (37 papers). The work is most often cited by research in Artificial Intelligence (2.3k citations), Computer Vision and Pattern Recognition (644 citations) and Automotive Engineering (358 citations). Shuming Shi has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Zhaopeng Tu, Xiaojiang Liu, Chin-Yew Lin, Haisong Zhang, Yan Wang, Longyue Wang, Jing Li, Jon Turner, Ji-Rong Wen and Yan Song. Their work appears in journals such as IEEE Transactions on Automatic Control, Optics Express and IEEE Access.
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.