Mingxi Wu
- Artificial Intelligence top 2%
- Computer Networks and Communications top 5%
- Signal Processing top 2%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Co-authors
- Christopher JermaineSanjay RankaFei XuLuis L. PerezPeter J. HaasChris JermaineAlin DeutschJohn G. Gums
- Topics
- Advanced Database Systems and Queries (7 papers)Data Management and Algorithms (7 papers)Anomaly Detection Techniques and Applications (7 papers)
- Journals
- IEEE Transactions on Knowledge and Data EngineeringProceedings of the VLDB EndowmentACM Transactions on Database Systems
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Mingxi Wu
19 papers receiving 668 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 490
- Computer Networks and Communications 366
- Signal Processing 306
- Computer Vision and Pattern Recognition 91
- Information Systems 84
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-authorship network of co-authors of Mingxi Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Mingxi Wu. A scholar is included among the top collaborators of Mingxi Wu 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 Mingxi Wu. Mingxi Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 21 | |
| 3 | 40 | |
| 4 | 4 | |
| 5 | 20 | |
| 6 | 2 | |
| 7 | 16 | |
| 8 | 27 | |
| 9 | 2 | |
| 10 | 5 | |
| 11 | 23 | |
| 12 | 11 | |
| 13 | Statistical methods for fast anomaly detection | 1 |
| 14 | 175 | |
| 15 | A Bayesian method for guessing the extreme values in a data set | 16 |
| 16 | 82 | |
| 17 | 212 | |
| 18 | 48 | |
| 19 | 9 |
About Mingxi Wu
Mingxi Wu is a scholar working on Signal Processing, Statistics and Probability and Computer Networks and Communications, having authored 19 papers that have together received 715 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (7 papers), Data Management and Algorithms (7 papers) and Anomaly Detection Techniques and Applications (7 papers). The work is most often cited by research in Signal Processing (306 citations), Computer Networks and Communications (366 citations) and Artificial Intelligence (490 citations). Mingxi Wu has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Christopher Jermaine, Sanjay Ranka, Fei Xu, Luis L. Perez, Peter J. Haas, Chris Jermaine, Alin Deutsch, John G. Gums, Yu Xu and Yuchen Zhang. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Proceedings of the VLDB Endowment and ACM Transactions on Database Systems.
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.