Yeying Zhu
- Statistics and Probability top 2%
- Aerospace Engineering
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Artificial Intelligence
- Co-authors
- Debashis GhoshDonna L. CoffmanPeng HuWei LuoCecilia A. CottonMohammad Mahdi AzariHalim YanıkömeroğluSafieddin Safavi‐Naeini
- Topics
- Advanced Causal Inference Techniques (14 papers)Statistical Methods and Inference (12 papers)Statistical Methods and Bayesian Inference (9 papers)
- Journals
- SHILAP Revista de lepidopterologíaBiometrikaIEEE Access
- Partner nations
- CanadaUnited StatesChina
In The Last Decade
Yeying Zhu
26 papers receiving 355 citations
Peers
Comparison fields: 5 of 90
- Statistics and Probability 146
- Aerospace Engineering 87
- Electrical and Electronic Engineering 53
- Computer Networks and Communications 48
- Artificial Intelligence 41
Countries citing papers authored by Yeying Zhu
This map shows the geographic impact of Yeying Zhu'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 Yeying Zhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yeying Zhu more than expected).
Fields of papers citing papers by Yeying Zhu
This network shows the impact of papers produced by Yeying Zhu. 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 Yeying Zhu. The network helps show where Yeying Zhu may publish in the future.
Co-authorship network of co-authors of Yeying Zhu
This figure shows the co-authorship network connecting the top 25 collaborators of Yeying Zhu. A scholar is included among the top collaborators of Yeying Zhu 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 Yeying Zhu. Yeying Zhu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 18 | |
| 7 | 2 | |
| 8 | 9 | |
| 9 | 23 | |
| 10 | 9 | |
| 11 | 4 | |
| 12 | 8 | |
| 13 | 4 | |
| 14 | 13 | |
| 15 | 28 | |
| 16 | 4 | |
| 17 | 28 | |
| 18 | 94 | |
| 19 | 12 | |
| 20 | Data-adaptive Approaches to Modeling Propensity Scores in Causal Inference Problems | 1 |
About Yeying Zhu
Yeying Zhu is a scholar working on Statistics and Probability, Signal Processing and Aerospace Engineering, having authored 26 papers that have together received 360 indexed citations. Recurring topics across this work include Advanced Causal Inference Techniques (14 papers), Statistical Methods and Inference (12 papers) and Statistical Methods and Bayesian Inference (9 papers). The work is most often cited by research in Statistics and Probability (146 citations), Aerospace Engineering (87 citations) and Computer Networks and Communications (48 citations). Yeying Zhu has collaborated with scholars based in Canada, United States and China. Frequent co-authors include Debashis Ghosh, Donna L. Coffman, Peng Hu, Wei Luo, Cecilia A. Cotton, Mohammad Mahdi Azari, Halim Yanıkömeroğlu, Safieddin Safavi‐Naeini, Jennifer S. Savage and Bhramar Mukherjee. Their work appears in journals such as SHILAP Revista de lepidopterología, Biometrika and IEEE Access.
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.