Wei Emma Zhang
- Artificial Intelligence top 2%
- Computer Networks and Communications top 2%
- Information Systems top 2%
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Co-authors
- Quan Z. ShengAdnan MahmoodAhoud AlhazmiChenliang LiZawar HussainSurya NepalLina YaoSubhash Sagar
- Topics
- Topic Modeling (28 papers)IoT and Edge/Fog Computing (15 papers)Natural Language Processing Techniques (10 papers)
- Journals
- SHILAP Revista de lepidopterologíaJournal of the American Statistical AssociationPLoS ONE
- Partner nations
- AustraliaChinaUnited States
In The Last Decade
Wei Emma Zhang
148 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Artificial Intelligence 817
- Computer Networks and Communications 475
- Information Systems 424
- Computer Vision and Pattern Recognition 307
- Electrical and Electronic Engineering 247
Countries citing papers authored by Wei Emma Zhang
This map shows the geographic impact of Wei Emma Zhang'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 Wei Emma Zhang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei Emma Zhang more than expected).
Fields of papers citing papers by Wei Emma Zhang
This network shows the impact of papers produced by Wei Emma Zhang. 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 Wei Emma Zhang. The network helps show where Wei Emma Zhang may publish in the future.
Co-authorship network of co-authors of Wei Emma Zhang
This figure shows the co-authorship network connecting the top 25 collaborators of Wei Emma Zhang. A scholar is included among the top collaborators of Wei Emma Zhang 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 Wei Emma Zhang. Wei Emma Zhang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 12 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 17 | |
| 6 | 3 | |
| 7 | 3 | |
| 8 | 9 | |
| 9 | 18 | |
| 10 | 58 | |
| 11 | 11 | |
| 12 | 8 | |
| 13 | 8 | |
| 14 | 4 | |
| 15 | 3 | |
| 16 | Generating Textual Adversarial Examples for Deep Learning Models: A Survey. | 15 |
| 17 | 7 | |
| 18 | 43 | |
| 19 | 3 | |
| 20 | 0 |
About Wei Emma Zhang
Wei Emma Zhang is a scholar working on Computational Mathematics, Artificial Intelligence and Computer Networks and Communications, having authored 165 papers that have together received 2.1k indexed citations. Recurring topics across this work include Topic Modeling (28 papers), IoT and Edge/Fog Computing (15 papers) and Natural Language Processing Techniques (10 papers). The work is most often cited by research in Artificial Intelligence (817 citations), Computer Networks and Communications (475 citations) and Information Systems (424 citations). Wei Emma Zhang has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Quan Z. Sheng, Adnan Mahmood, Ahoud Alhazmi, Chenliang Li, Zawar Hussain, Surya Nepal, Lina Yao, Subhash Sagar, Yongrui Qin and Xianzhi Wang. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and PLoS ONE.
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.