Shouling Ji
- Artificial Intelligence top 0.5%
- Computer Networks and Communications top 1%
- Information Systems top 0.5%
- Signal Processing top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Co-authors
- Raheem BeyahTing WangZhipeng CaiYingshu LiJing HePrateek MittalXinyang ZhangWeiqing Li
- Topics
- Adversarial Robustness in Machine Learning (51 papers)Advanced Malware Detection Techniques (46 papers)Privacy-Preserving Technologies in Data (32 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceScientific ReportsIEEE Transactions on Image Processing
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Shouling Ji
229 papers receiving 4.3k citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Artificial Intelligence 2.1k
- Computer Networks and Communications 1.4k
- Information Systems 1.1k
- Signal Processing 814
- Computer Vision and Pattern Recognition 680
Countries citing papers authored by Shouling Ji
This map shows the geographic impact of Shouling Ji'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 Shouling Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shouling Ji more than expected).
Fields of papers citing papers by Shouling Ji
This network shows the impact of papers produced by Shouling Ji. 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 Shouling Ji. The network helps show where Shouling Ji may publish in the future.
Co-authorship network of co-authors of Shouling Ji
This figure shows the co-authorship network connecting the top 25 collaborators of Shouling Ji. A scholar is included among the top collaborators of Shouling Ji 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 Shouling Ji. Shouling Ji 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 | 3 | |
| 3 | 1 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 4 | |
| 10 | 3 | |
| 11 | 7 | |
| 12 | 8 | |
| 13 | 12 | |
| 14 | 53 | |
| 15 | 3 | |
| 16 | 15 | |
| 17 | 4 | |
| 18 | 37 | |
| 19 | 99 | |
| 20 | 13 |
About Shouling Ji
Shouling Ji is a scholar working on Signal Processing, Software and Artificial Intelligence, having authored 252 papers that have together received 4.4k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (51 papers), Advanced Malware Detection Techniques (46 papers) and Privacy-Preserving Technologies in Data (32 papers). The work is most often cited by research in Signal Processing (814 citations), Artificial Intelligence (2.1k citations) and Computer Networks and Communications (1.4k citations). Shouling Ji has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Raheem Beyah, Ting Wang, Zhipeng Cai, Yingshu Li, Jing He, Prateek Mittal, Xinyang Zhang, Weiqing Li, Zhenguang Liu and Yi Pan. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and IEEE Transactions on Image 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.