Mingxi Wu
Impact in
- Signal Processing top 2%
- Data Management and Algorithms
- Time Series Analysis and Forecasting
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- Advanced Database Systems and Queries
- Network Security and Intrusion Detection
Papers in
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- Anomaly Detection Techniques and Applications 7
- Bayesian Methods and Mixture Models 2
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- Advanced Database Systems and Queries 7
- Co-authors
- Christopher Jermaine (5 shared papers)Sanjay Ranka (4 shared papers)Luis L. Perez (3 shared papers)Fei Xu (3 shared papers)Peter J. Haas (2 shared papers)Chris Jermaine (7 shared papers)Alin Deutsch (2 shared papers)Yu Xu (1 shared paper)
- Journals
- Proceedings of the VLDB Endowment (2 papers)ACM Transactions on Database Systems (2 papers)The VLDB Journal (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Mingxi Wu
19 papers receiving 682 citations
Peers
Comparison fields: 5 of 62
- Signal Processing 312
- Computer Networks and Communications 372
- Artificial Intelligence 497
- Management Science and Operations Research 78
- Computer Vision and Pattern Recognition 93
Countries citing papers authored by Mingxi Wu
This map shows the geographic impact of Mingxi Wu'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 Mingxi Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingxi Wu more than expected).
Fields of papers citing papers by Mingxi Wu
This network shows the impact of papers produced by Mingxi Wu. 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 Mingxi Wu. The network helps show where Mingxi Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Mingxi Wu, 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 | 2007 | 216 | |
| 2 | 2008 | 175 | |
| 3 | 2007 | 83 | |
| 4 | 2006 | 48 | |
| 5 | 2022 | 44 | |
| 6 | 2011 | 27 | |
| 7 | 2022 | 24 | |
| 8 | 2009 | 23 | |
| 9 | 2020 | 20 | |
| 10 | 2013 | 16 | |
| 11 | A Bayesian method for guessing the extreme values in a data set | 2007 | 16 |
| 12 | 2008 | 11 | |
| 13 | 1992 | 9 | |
| 14 | 2009 | 5 | |
| 15 | 2022 | 4 | |
| 16 | 2025 | 3 | |
| 17 | 2010 | 2 | |
| 18 | 2015 | 2 | |
| 19 | Statistical methods for fast anomaly detection | 2008 | 1 |
About Mingxi Wu
Mingxi Wu is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Computer Vision and Pattern Recognition and Epidemiology, having authored 19 papers that have together received 729 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (7 papers), Anomaly Detection Techniques and Applications (7 papers), Data Management and Algorithms (7 papers), Data-Driven Disease Surveillance (4 papers), Graph Theory and Algorithms (4 papers), Time Series Analysis and Forecasting (4 papers), Advanced Statistical Methods and Models (3 papers) and Bayesian Methods and Mixture Models (2 papers). The work is most often cited by research in Signal Processing (312 citations), Computer Networks and Communications (372 citations), Artificial Intelligence (497 citations), Management Science and Operations Research (78 citations) and Computer Vision and Pattern Recognition (93 citations). Mingxi Wu has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Christopher Jermaine, Sanjay Ranka, Luis L. Perez, Fei Xu, Peter J. Haas, Chris Jermaine, Alin Deutsch, Yu Xu, John G. Gums and Peter Boncz. Their work appears in journals such as Proceedings of the VLDB Endowment, ACM Transactions on Database Systems, The VLDB Journal, ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering.
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.