David Hong
Impact in
- Computational Mathematics top 2%
- Tensor decomposition and applications
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- Software-Defined Networks and 5G
- IoT and Edge/Fog Computing
- Network Security and Intrusion Detection
- Network Traffic and Congestion Control
Papers in
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- Blind Source Separation Techniques 4
- Advanced Malware Detection Techniques 3
- Co-authors
- Z. Morley Mao (11 shared papers)Tamara G. Kolda (1 shared paper)Laura Balzano (8 shared papers)Jeffrey A. Fessler (6 shared papers)Sujata Banerjee (1 shared paper)Yadi Ma (1 shared paper)Michael F. Clarke (1 shared paper)Scott Mahlke (7 shared papers)
- Journals
- IEEE Transactions on Signal Processing (1 paper)IEEE Transactions on Power Electronics (1 paper)ACM SIGMETRICS Performance Evaluation Review (1 paper)SIAM Review (1 paper)Science Translational Medicine (1 paper)
- Partner nations
- United StatesSpainGambia
In The Last Decade
David Hong
28 papers receiving 484 citations
Peers
Comparison fields: 5 of 92
- Computational Mathematics 75
- Computer Networks and Communications 161
- Signal Processing 51
- Software 14
- Computer Vision and Pattern Recognition 53
Countries citing papers authored by David Hong
This map shows the geographic impact of David Hong'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 David Hong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Hong more than expected).
Fields of papers citing papers by David Hong
This network shows the impact of papers produced by David Hong. 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 David Hong. The network helps show where David Hong may publish in the future.
Co-authors
The 25 scholars most cited alongside David Hong, 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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 91 | |
| 2 | 2020 | 85 | |
| 3 | 2015 | 53 | |
| 4 | 2018 | 51 | |
| 5 | 1998 | 48 | |
| 6 | 2021 | 32 | |
| 7 | 2020 | 20 | |
| 8 | 2013 | 19 | |
| 9 | 2021 | 13 | |
| 10 | 2021 | 11 | |
| 11 | 2021 | 10 | |
| 12 | 2016 | 8 | |
| 13 | 2017 | 7 | |
| 14 | 2020 | 6 | |
| 15 | 2023 | 5 | |
| 16 | 2019 | 5 | |
| 17 | 2022 | 4 | |
| 18 | 2023 | 4 | |
| 19 | 2015 | 4 | |
| 20 | 2019 | 4 |
About David Hong
David Hong is a scholar working on Signal Processing, Electrical and Electronic Engineering, Computer Networks and Communications, Computational Mechanics and Artificial Intelligence, having authored 30 papers that have together received 494 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (5 papers), Software Testing and Debugging Techniques (5 papers), Blind Source Separation Techniques (4 papers), Advanced Malware Detection Techniques (3 papers), Tensor decomposition and applications (3 papers), Security and Verification in Computing (3 papers), Autonomous Vehicle Technology and Safety (2 papers) and Spectroscopy and Chemometric Analyses (2 papers). The work is most often cited by research in Computational Mathematics (75 citations), Computer Networks and Communications (161 citations), Signal Processing (51 citations), Software (14 citations) and Computer Vision and Pattern Recognition (53 citations). David Hong has collaborated with scholars based in United States, Spain and Gambia. Frequent co-authors include Z. Morley Mao, Tamara G. Kolda, Laura Balzano, Jeffrey A. Fessler, Sujata Banerjee, Yadi Ma, Michael F. Clarke, Scott Mahlke, Qi Alfred Chen and Jason Flinn. Their work appears in journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Power Electronics, ACM SIGMETRICS Performance Evaluation Review, SIAM Review and Science Translational Medicine.
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.