Shuyang Dai
- Materials Chemistry top 10%
- Atomic and Molecular Physics, and Optics top 10%
- Electrical and Electronic Engineering
- Molecular Biology
- Cancer Research top 10%
- Topics
- Microstructure and mechanical properties (7 papers)Pediatric Hepatobiliary Diseases and Treatments (5 papers)Generative Adversarial Networks and Image Synthesis (5 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Shuyang Dai
61 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 137
- Materials Chemistry 704
- Atomic and Molecular Physics, and Optics 237
- Electrical and Electronic Engineering 197
- Molecular Biology 178
- Cancer Research 174
Countries citing papers authored by Shuyang Dai
This map shows the geographic impact of Shuyang Dai'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 Shuyang Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuyang Dai more than expected).
Fields of papers citing papers by Shuyang Dai
This network shows the impact of papers produced by Shuyang Dai. 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 Shuyang Dai. The network helps show where Shuyang Dai may publish in the future.
Co-authorship network of co-authors of Shuyang Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Shuyang Dai. A scholar is included among the top collaborators of Shuyang Dai 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 Shuyang Dai. Shuyang Dai 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 | 7 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 36 | |
| 9 | 2 | |
| 10 | 7 | |
| 11 | 6 | |
| 12 | 1 | |
| 13 | 94 | |
| 14 | Variational annealing of GANs: A Langevin perspective | 1 |
| 15 | On Fenchel Mini-Max Learning | 1 |
| 16 | Symmetric Variational Autoencoder and Connections to Adversarial Learning | 12 |
| 17 | JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets | 3 |
| 18 | 4 | |
| 19 | 18 | |
| 20 | 288 |
About Shuyang Dai
Shuyang Dai is a scholar working on Radiation, Statistics, Probability and Uncertainty and Pulmonary and Respiratory Medicine, having authored 66 papers that have together received 1.4k indexed citations. Recurring topics across this work include Microstructure and mechanical properties (7 papers), Pediatric Hepatobiliary Diseases and Treatments (5 papers) and Generative Adversarial Networks and Image Synthesis (5 papers). The work is most often cited by research in Materials Chemistry (704 citations), Cancer Research (174 citations) and Hepatology (74 citations). Shuyang Dai has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include David J. Srolovitz, Yang Xiang, Jiarui Yang, Xide Li, Wen Wang, Quanshui Zheng, Yijie Xia, Jian Han, Songsong Zhou and Jianwei Sun. Their work appears in journals such as Nature Communications, Nano Letters and Bioinformatics.
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.