Zonghan Yang
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Speech Recognition and Synthesis
- Domain Adaptation and Few-Shot Learning
- Machine Learning in Healthcare
Papers in
-
- Natural Language Processing Techniques 7
- Topic Modeling 7
-
- Multimodal Machine Learning Applications 7
- Co-authors
- Maosong Sun (6 shared papers)Yang Liu (5 shared papers)Hai-Tao Zheng (1 shared paper)Jianfei Chen (1 shared paper)Weilin Zhao (1 shared paper)Guang Yang (1 shared paper)Ning Ding (1 shared paper)Yulin Chen (1 shared paper)
- Journals
- Sustainability (1 paper)Nature Machine Intelligence (1 paper)SHILAP Revista de lepidopterología (2 papers)arXiv (Cornell University) (1 paper)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Zonghan Yang
7 papers receiving 511 citations
Zonghan Yang's Hit Papers
Peers
Comparison fields: 5 of 98
- Health Informatics 25
- Artificial Intelligence 307
- Computer Vision and Pattern Recognition 100
- Software 10
- Information Systems 52
Countries citing papers authored by Zonghan Yang
This map shows the geographic impact of Zonghan Yang'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 Zonghan Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zonghan Yang more than expected).
Fields of papers citing papers by Zonghan Yang
This network shows the impact of papers produced by Zonghan Yang. 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 Zonghan Yang. The network helps show where Zonghan Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Zonghan Yang, 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 | Parameter-efficient fine-tuning of large-scale pre-trained language models Hit paper breakdown → | 2023 | 420 |
| 2 | 2020 | 62 | |
| 3 | 2019 | 48 | |
| 4 | 2021 | 5 | |
| 5 | 2021 | 2 | |
| 6 | 2025 | 2 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 0 | |
| 9 | 2023 | 0 | |
| 10 | 2019 | 0 | |
| 11 | 2020 | 0 |
About Zonghan Yang
Zonghan Yang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Civil and Structural Engineering and Control and Systems Engineering, having authored 11 papers that have together received 540 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (7 papers), Multimodal Machine Learning Applications (7 papers), Topic Modeling (7 papers), Thermal Analysis in Power Transmission (1 paper), Indoor and Outdoor Localization Technologies (1 paper), Innovative concrete reinforcement materials (1 paper), Recycled Aggregate Concrete Performance (1 paper) and Concrete and Cement Materials Research (1 paper). The work is most often cited by research in Health Informatics (25 citations), Artificial Intelligence (307 citations), Computer Vision and Pattern Recognition (100 citations), Software (10 citations) and Information Systems (52 citations). Zonghan Yang has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Maosong Sun, Yang Liu, Hai-Tao Zheng, Jianfei Chen, Weilin Zhao, Guang Yang, Ning Ding, Yulin Chen, Yusheng Su and Chi-Min Chan. Their work appears in journals such as Sustainability, Nature Machine Intelligence, SHILAP Revista de lepidopterología and arXiv (Cornell University).
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.