Ya Zhang
- Artificial Intelligence top 0.2%
- Computer Vision and Pattern Recognition top 0.2%
- Molecular Biology top 10%
- Information Systems top 0.5%
- Biomedical Engineering top 5%
- Topics
- Domain Adaptation and Few-Shot Learning (27 papers)Anomaly Detection Techniques and Applications (23 papers)Advanced Image and Video Retrieval Techniques (20 papers)
- Journals
- CellNature CommunicationsSHILAP Revista de lepidopterología
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Ya Zhang
436 papers receiving 9.6k citations
Hit Papers
Peers
Comparison fields: 5 of 216
- Artificial Intelligence 2.9k
- Computer Vision and Pattern Recognition 2.6k
- Molecular Biology 1.1k
- Information Systems 1.0k
- Biomedical Engineering 810
Countries citing papers authored by Ya Zhang
This map shows the geographic impact of Ya 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 Ya Zhang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ya Zhang more than expected).
Fields of papers citing papers by Ya Zhang
This network shows the impact of papers produced by Ya 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 Ya Zhang. The network helps show where Ya Zhang may publish in the future.
Co-authorship network of co-authors of Ya Zhang
This figure shows the co-authorship network connecting the top 25 collaborators of Ya Zhang. A scholar is included among the top collaborators of Ya 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 Ya Zhang. Ya 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 | Machine learning-enhanced flavoromics: Identifying key aroma compounds and predicting sensory quality in sauce-flavor baijiubreakdown → | 19 |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 6 | |
| 5 | 1 | |
| 6 | 6 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 1 | |
| 10 | 7 | |
| 11 | 8 | |
| 12 | 13 | |
| 13 | 23 | |
| 14 | 18 | |
| 15 | 19 | |
| 16 | 21 | |
| 17 | 58 | |
| 18 | 9 | |
| 19 | 4 | |
| 20 | Procreation Health of 3196 Unmarried Female University Students | 1 |
About Ya Zhang
Ya Zhang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Health Informatics, having authored 478 papers that have together received 9.8k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (27 papers), Anomaly Detection Techniques and Applications (23 papers) and Advanced Image and Video Retrieval Techniques (20 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.6k citations), Artificial Intelligence (2.9k citations) and Health Informatics (110 citations). Ya Zhang has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Olivier Chapelle, Yanfeng Wang, Siheng Chen, Zhe Xu, Shaoli Huang, Maosen Li, Qi Tian, Dacheng Tao, Qinwei Xu and Ruipeng Zhang. Their work appears in journals such as Cell, Nature Communications and SHILAP Revista de lepidopterología.
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.