Jun Kong
Impact in
- Artificial Intelligence top 0.5%
- AI in cancer detection
- Genetics top 2%
- Glioma Diagnosis and Treatment
Papers in
-
- AI in cancer detection 60
- Software 23
- Model-Driven Software Engineering Techniques 23
- Co-authors
- Joel Haskin Saltz (38 shared papers)Daniel Jay Brat (33 shared papers)Metin N. Gürcan (17 shared papers)Lee Alex Donald Cooper (29 shared papers)Fusheng Wang (59 shared papers)Olcay Sertel (13 shared papers)Kang Zhang (33 shared papers)Tahsin Kurç (36 shared papers)
- Journals
- Journal of Visual Languages & Computing (7 papers)Bioinformatics (5 papers)Computers in Biology and Medicine (4 papers)Journal of Pathology Informatics (3 papers)Distributed and Parallel Databases (2 papers)
- Partner nations
- United StatesChinaBrazil
In The Last Decade
Jun Kong
217 papers receiving 4.1k citations
Peers
Comparison fields: 5 of 155
- Artificial Intelligence 1.5k
- Genetics 358
- Software 184
- Health Informatics 84
- Biophysics 183
Countries citing papers authored by Jun Kong
This map shows the geographic impact of Jun Kong'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 Jun Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Kong more than expected).
Fields of papers citing papers by Jun Kong
This network shows the impact of papers produced by Jun Kong. 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 Jun Kong. The network helps show where Jun Kong may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Kong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 225 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 192 | |
| 2 | 2008 | 184 | |
| 3 | 2013 | 174 | |
| 4 | 2008 | 169 | |
| 5 | 2013 | 156 | |
| 6 | 2008 | 133 | |
| 7 | 2013 | 90 | |
| 8 | 2015 | 80 | |
| 9 | 2014 | 78 | |
| 10 | 2012 | 76 | |
| 11 | 2006 | 61 | |
| 12 | 2005 | 59 | |
| 13 | 2017 | 59 | |
| 14 | 2009 | 58 | |
| 15 | 2006 | 54 | |
| 16 | 2008 | 53 | |
| 17 | 2014 | 53 | |
| 18 | 2013 | 52 | |
| 19 | 2010 | 51 | |
| 20 | 2011 | 51 |
About Jun Kong
Jun Kong is a scholar working on Artificial Intelligence, Software, Computer Vision and Pattern Recognition, Human-Computer Interaction and Biophysics, having authored 225 papers that have together received 4.2k indexed citations. Recurring topics across this work include AI in cancer detection (60 papers), Model-Driven Software Engineering Techniques (23 papers), Medical Image Segmentation Techniques (20 papers), Cell Image Analysis Techniques (18 papers), Parallel Computing and Optimization Techniques (15 papers), Usability and User Interface Design (13 papers), Glioma Diagnosis and Treatment (11 papers) and Interactive and Immersive Displays (10 papers). The work is most often cited by research in Artificial Intelligence (1.5k citations), Genetics (358 citations), Software (184 citations), Health Informatics (84 citations) and Biophysics (183 citations). Jun Kong has collaborated with scholars based in United States, China and Brazil. Frequent co-authors include Joel Haskin Saltz, Daniel Jay Brat, Metin N. Gürcan, Lee Alex Donald Cooper, Fusheng Wang, Olcay Sertel, Kang Zhang, Tahsin Kurç, Ümit V. Çatalyürek and George Teodoro. Their work appears in journals such as Journal of Visual Languages & Computing, Bioinformatics, Computers in Biology and Medicine, Journal of Pathology Informatics and Distributed and Parallel Databases.
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.