Muchao Ye
Impact in
-
- Artificial Intelligence in Healthcare
- Artificial Intelligence top 5%
- Machine Learning in Healthcare
- Topic Modeling
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
Papers in
-
- Artificial Intelligence in Healthcare 6
-
- Topic Modeling 9
- Machine Learning in Healthcare 6
- Adversarial Robustness in Machine Learning 4
- Anomaly Detection Techniques and Applications 2
- Security and Verification in Computing 1
- Co-authors
- Fenglong Ma (12 shared papers)Junyu Luo (6 shared papers)Cao Xiao (5 shared papers)Ting Wang (4 shared papers)Chenglin Miao (3 shared papers)Quanzeng You (2 shared papers)Yaqing Wang (1 shared paper)Xingyi Yang (1 shared paper)
- Journals
- Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1 paper)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Muchao Ye
13 papers receiving 360 citations
Peers
Comparison fields: 5 of 51
- Health Information Management 122
- Artificial Intelligence 309
- Health Informatics 9
- Signal Processing 41
- Computer Vision and Pattern Recognition 42
Countries citing papers authored by Muchao Ye
This map shows the geographic impact of Muchao Ye'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 Muchao Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muchao Ye more than expected).
Fields of papers citing papers by Muchao Ye
This network shows the impact of papers produced by Muchao Ye. 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 Muchao Ye. The network helps show where Muchao Ye may publish in the future.
Co-authors
The 18 scholars most cited alongside Muchao Ye, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 152 | |
| 2 | 2021 | 47 | |
| 3 | 2020 | 32 | |
| 4 | 2021 | 31 | |
| 5 | 2022 | 29 | |
| 6 | 2021 | 15 | |
| 7 | 2025 | 13 | |
| 8 | 2022 | 12 | |
| 9 | 2021 | 11 | |
| 10 | 2023 | 7 | |
| 11 | 2022 | 6 | |
| 12 | 2022 | 6 | |
| 13 | 2024 | 1 | |
| 14 | 2024 | 0 |
About Muchao Ye
Muchao Ye is a scholar working on Health Information Management, Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture and Signal Processing, having authored 14 papers that have together received 362 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Artificial Intelligence in Healthcare (6 papers), Machine Learning in Healthcare (6 papers), Adversarial Robustness in Machine Learning (4 papers), Multimodal Machine Learning Applications (4 papers), Anomaly Detection Techniques and Applications (2 papers), Security and Verification in Computing (1 paper) and Physical Unclonable Functions (PUFs) and Hardware Security (1 paper). The work is most often cited by research in Health Information Management (122 citations), Artificial Intelligence (309 citations), Health Informatics (9 citations), Signal Processing (41 citations) and Computer Vision and Pattern Recognition (42 citations). Muchao Ye has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Fenglong Ma, Junyu Luo, Cao Xiao, Ting Wang, Chenglin Miao, Quanzeng You, Yaqing Wang, Xingyi Yang, He Pan and Weiyang Liu. Their work appears in journals such as Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.