Stephen S.‐T. Yau
- Molecular Biology top 10%
- Endocrinology, Diabetes and Metabolism top 1%
- Geometry and Topology top 0.5%
- Surgery top 5%
- Artificial Intelligence top 2%
- Co-authors
- Stephen C.K. LawSin‐Ming ChowSiu‐Kie AuRong HeWai‐Hon LauChangchuan YinJohn K. ChanShing‐Tung Yau
- Topics
- Algebraic Geometry and Number Theory (52 papers)Target Tracking and Data Fusion in Sensor Networks (51 papers)Machine Learning in Bioinformatics (44 papers)
- Journals
- Proceedings of the National Academy of SciencesNucleic Acids ResearchJournal of Clinical Oncology
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Stephen S.‐T. Yau
293 papers receiving 4.3k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Molecular Biology 981
- Endocrinology, Diabetes and Metabolism 896
- Geometry and Topology 889
- Surgery 828
- Artificial Intelligence 636
Countries citing papers authored by Stephen S.‐T. Yau
This map shows the geographic impact of Stephen S.‐T. Yau'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 Stephen S.‐T. Yau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen S.‐T. Yau more than expected).
Fields of papers citing papers by Stephen S.‐T. Yau
This network shows the impact of papers produced by Stephen S.‐T. Yau. 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 Stephen S.‐T. Yau. The network helps show where Stephen S.‐T. Yau may publish in the future.
Co-authorship network of co-authors of Stephen S.‐T. Yau
This figure shows the co-authorship network connecting the top 25 collaborators of Stephen S.‐T. Yau. A scholar is included among the top collaborators of Stephen S.‐T. Yau 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 Stephen S.‐T. Yau. Stephen S.‐T. Yau 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 | 1 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 5 | |
| 9 | 0 | |
| 10 | 3 | |
| 11 | 48 | |
| 12 | 0 | |
| 13 | 3 | |
| 14 | Classification of Affine Varieties Being Cones over Nonsingular Projective Varieties: Hypersurface Case | 9 |
| 15 | 4 | |
| 16 | 2 | |
| 17 | 10 | |
| 18 | On the design of modulo arithmetic units based on cyclic groups | 0 |
| 19 | An estimate of the gap of the first two eigenvalues in the Schrödinger operator | 89 |
| 20 | 5 |
About Stephen S.‐T. Yau
Stephen S.‐T. Yau is a scholar working on Geometry and Topology, Algebra and Number Theory and Mathematical Physics, having authored 343 papers that have together received 4.7k indexed citations. Recurring topics across this work include Algebraic Geometry and Number Theory (52 papers), Target Tracking and Data Fusion in Sensor Networks (51 papers) and Machine Learning in Bioinformatics (44 papers). The work is most often cited by research in Algebra and Number Theory (523 citations), Geometry and Topology (889 citations) and Otorhinolaryngology (312 citations). Stephen S.‐T. Yau has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Stephen C.K. Law, Sin‐Ming Chow, Siu‐Kie Au, Rong He, Wai‐Hon Lau, Changchuan Yin, John K. Chan, Shing‐Tung Yau, Chenglong Yu and Huaiqing Zuo. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Clinical Oncology.
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.