Salman Khan
- Computer Vision and Pattern Recognition top 0.5%
- Media Technology top 0.5%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Radiology, Nuclear Medicine and Imaging top 10%
- Co-authors
- Fahad Shahbaz KhanMing–Hsuan YangSyed Waqas ZamirMunawar HayatAditya AroraMuhammad MaazHanoona RasheedMuhammad Uzair Khattak
- Topics
- Domain Adaptation and Few-Shot Learning (4 papers)Advanced Neural Network Applications (4 papers)COVID-19 Clinical Research Studies (4 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsPLoS ONE
- Partner nations
- United StatesUnited Arab EmiratesPakistan
In The Last Decade
Salman Khan
46 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 170
- Computer Vision and Pattern Recognition 2.1k
- Media Technology 864
- Artificial Intelligence 450
- Biomedical Engineering 216
- Radiology, Nuclear Medicine and Imaging 161
Countries citing papers authored by Salman Khan
This map shows the geographic impact of Salman Khan'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 Salman Khan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Salman Khan more than expected).
Fields of papers citing papers by Salman Khan
This network shows the impact of papers produced by Salman Khan. 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 Salman Khan. The network helps show where Salman Khan may publish in the future.
Co-authorship network of co-authors of Salman Khan
This figure shows the co-authorship network connecting the top 25 collaborators of Salman Khan. A scholar is included among the top collaborators of Salman Khan 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 Salman Khan. Salman Khan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 9 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 5 | |
| 9 | 1 | |
| 10 | SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applicationsbreakdown → | 108 |
| 11 | 1 | |
| 12 | 6 | |
| 13 | 25 | |
| 14 | 1 | |
| 15 | 0 | |
| 16 | 17 | |
| 17 | 8 | |
| 18 | 1 | |
| 19 | Universal test and treat (UTT) versus standard of care for access to antiretroviral therapy in HIV clients : The MaxART stepped-wedge randomized controlled health systems trial in Swaziland | 7 |
| 20 | 1 |
About Salman Khan
Salman Khan is a scholar working on Microbiology, Computer Vision and Pattern Recognition and Molecular Medicine, having authored 58 papers that have together received 3.1k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Advanced Neural Network Applications (4 papers) and COVID-19 Clinical Research Studies (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.1k citations), Media Technology (864 citations) and Acoustics and Ultrasonics (14 citations). Salman Khan has collaborated with scholars based in United States, United Arab Emirates and Pakistan. Frequent co-authors include Fahad Shahbaz Khan, Ming–Hsuan Yang, Syed Waqas Zamir, Munawar Hayat, Aditya Arora, Muhammad Maaz, Hanoona Rasheed, Muhammad Uzair Khattak, Mohammed Bennamoun and Hossein Rahmani. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.
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.