Jingjing Wu

627 citations
50 papers · 436 indexed · h-index 13
Topics
Statistical Methods and Inference (21 papers)Bayesian Methods and Mixture Models (20 papers)Statistical Methods and Bayesian Inference (10 papers)
Journals
SHILAP Revista de lepidopterologíaScientific ReportsIEEE Transactions on Power Systems
Partner nations
CanadaUnited StatesChina

In The Last Decade

Jingjing Wu

44 papers receiving 416 citations

Peers

Jingjing Wu
Comparison fields: 5 of 83
  • Electrical and Electronic Engineering 164
  • Statistics and Probability 138
  • Control and Systems Engineering 116
  • Artificial Intelligence 111
  • Molecular Biology 47
Replace Carlos N. Bouza with:
Carlos N. Bouza Cuba
Raffaele Argiento Italy
Božidar V. Popović Montenegro
Kani Chen Hong Kong
Gaorong Li China
Stephen A. Book United States
Lu Lin China
Tsai‐Hung Fan Taiwan
Isha Dewan India
Peter Purdue United States
Jingjing Wu relative to Carlos N. Bouza Cuba Carlos N. Bouza's profile →
Citations per field
00.5×6.8×
Carlos N. Bouza · 1×
Citations per year

Countries citing papers authored by Jingjing Wu

Since Specialization
Citations

This map shows the geographic impact of Jingjing 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 Jingjing Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingjing Wu more than expected).

Fields of papers citing papers by Jingjing Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jingjing 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 Jingjing Wu. The network helps show where Jingjing Wu may publish in the future.

Co-authorship network of co-authors of Jingjing Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Jingjing Wu. A scholar is included among the top collaborators of Jingjing 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 Jingjing Wu. Jingjing Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 0
3 0
4 0
5 2
6 8
7 2
8 2
9 39
10 2
11 2
12 22
13 4
14 2
15 1
16 15
17 19
18 16
19 1
20 85

About Jingjing Wu

Jingjing Wu is a scholar working on Statistics and Probability, Energy Engineering and Power Technology and Artificial Intelligence, having authored 50 papers that have together received 436 indexed citations. Recurring topics across this work include Statistical Methods and Inference (21 papers), Bayesian Methods and Mixture Models (20 papers) and Statistical Methods and Bayesian Inference (10 papers). The work is most often cited by research in Statistics and Probability (138 citations), Control and Systems Engineering (116 citations) and Safety, Risk, Reliability and Quality (35 citations). Jingjing Wu has collaborated with scholars based in Canada, United States and China. Frequent co-authors include Rohana J. Karunamuni, C.S. Chen, Kevin Tomsovic, C.-S. Chen, Yun Ke, Qing Li, Devin Kwok, Quan Long, Mingyu Kang and Chen Cao. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Power 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.

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