Edward Gan
Impact in
- Signal Processing top 5%
- Advanced Malware Detection Techniques
- Data Management and Algorithms
- Artificial Intelligence top 10%
- Security and Verification in Computing
- Data Stream Mining Techniques
- Logic, programming, and type systems
- Anomaly Detection Techniques and Applications
Papers in
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- Time Series Analysis and Forecasting 4
- Data Management and Algorithms 4
-
- Data Stream Mining Techniques 4
- Anomaly Detection Techniques and Applications 4
- Security and Verification in Computing 2
- Domain Adaptation and Few-Shot Learning 2
- Semantic Web and Ontologies 2
- Co-authors
- Peter D. Bailis (11 shared papers)Gang Tan (2 shared papers)Jean-Baptiste Tristan (2 shared papers)Greg Morrisett (2 shared papers)Joseph Tassarotti (2 shared papers)Deepak Narayanan (2 shared papers)Samuel R. Madden (2 shared papers)Jialin Ding (2 shared papers)
- Journals
- Proceedings of the VLDB Endowment (4 papers)ACM SIGPLAN Notices (1 paper)ACM Transactions on Database Systems (1 paper)The VLDB Journal (1 paper)Conference on Innovative Data Systems Research (1 paper)
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Edward Gan
13 papers receiving 306 citations
Peers
Comparison fields: 5 of 41
- Signal Processing 126
- Artificial Intelligence 229
- Hardware and Architecture 47
- Software 21
- Computer Networks and Communications 108
Countries citing papers authored by Edward Gan
This map shows the geographic impact of Edward Gan'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 Edward Gan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward Gan more than expected).
Fields of papers citing papers by Edward Gan
This network shows the impact of papers produced by Edward Gan. 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 Edward Gan. The network helps show where Edward Gan may publish in the future.
Co-authors
The 19 scholars most cited alongside Edward Gan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 109 | |
| 2 | 2017 | 73 | |
| 3 | 2018 | 37 | |
| 4 | 2017 | 20 | |
| 5 | 2012 | 18 | |
| 6 | 2020 | 17 | |
| 7 | 2018 | 13 | |
| 8 | 2020 | 12 | |
| 9 | 2018 | 5 | |
| 10 | 2020 | 5 | |
| 11 | Prioritizing Attention in Analytic Monitoring. | 2017 | 4 |
| 12 | 2017 | 3 | |
| 13 | 2019 | 3 |
About Edward Gan
Edward Gan is a scholar working on Signal Processing, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 13 papers that have together received 319 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (4 papers), Anomaly Detection Techniques and Applications (4 papers), Time Series Analysis and Forecasting (4 papers), Data Management and Algorithms (4 papers), Advanced Database Systems and Queries (4 papers), Security and Verification in Computing (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Semantic Web and Ontologies (2 papers). The work is most often cited by research in Signal Processing (126 citations), Artificial Intelligence (229 citations), Hardware and Architecture (47 citations), Software (21 citations) and Computer Networks and Communications (108 citations). Edward Gan has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Peter D. Bailis, Gang Tan, Jean-Baptiste Tristan, Greg Morrisett, Joseph Tassarotti, Deepak Narayanan, Samuel R. Madden, Jialin Ding, Kai Sheng Tai and Matei Zaharia. Their work appears in journals such as Proceedings of the VLDB Endowment, ACM SIGPLAN Notices, ACM Transactions on Database Systems, The VLDB Journal and Conference on Innovative Data Systems Research.
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.