Bei Wang
- Computer Vision and Pattern Recognition top 2%
- Computational Theory and Mathematics top 1%
- Artificial Intelligence top 5%
- Biophysics top 2%
- Molecular Biology
- Co-authors
- Valerio PascucciPeer‐Timo BremerShusen LiuDan MaljovecPaul RosenLin YanIngrid HotzMustafa Hajij
- Topics
- Topological and Geometric Data Analysis (57 papers)Data Visualization and Analytics (35 papers)Cell Image Analysis Techniques (14 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionBiophysics
- Journals
- Nature Cell BiologyJournal of Lightwave TechnologyIEEE Transactions on Visualization and Computer Graphics
- Partner nations
- United StatesChinaSweden
In The Last Decade
Bei Wang
94 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 129
- Computer Vision and Pattern Recognition 515
- Computational Theory and Mathematics 368
- Artificial Intelligence 242
- Biophysics 135
- Molecular Biology 93
Countries citing papers authored by Bei Wang
This map shows the geographic impact of Bei Wang'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 Bei Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bei Wang more than expected).
Fields of papers citing papers by Bei Wang
This network shows the impact of papers produced by Bei Wang. 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 Bei Wang. The network helps show where Bei Wang may publish in the future.
Co-authorship network of co-authors of Bei Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Bei Wang. A scholar is included among the top collaborators of Bei Wang 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 Bei Wang. Bei Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 7 | |
| 11 | 2 | |
| 12 | 9 | |
| 13 | 12 | |
| 14 | TopoAct: Exploring the Shape of Activations in Deep Learning | 2 |
| 15 | MOG: Mapper on Graphs for Relationship Preserving Clustering | 2 |
| 16 | 22 | |
| 17 | Persistent Homology Guided Exploration of Time-Varying Graphs. | 1 |
| 18 | 17 | |
| 19 | Improved Medical Image Registration Algorithm Based on Maximization of Normalized Mutual Information | 0 |
| 20 | 15 |
About Bei Wang
Bei Wang is a scholar working on Computational Theory and Mathematics, Computer Graphics and Computer-Aided Design and Biophysics, having authored 110 papers that have together received 1.1k indexed citations. Recurring topics across this work include Topological and Geometric Data Analysis (57 papers), Data Visualization and Analytics (35 papers) and Cell Image Analysis Techniques (14 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (92 citations), Computer Vision and Pattern Recognition (515 citations) and Biophysics (135 citations). Bei Wang has collaborated with scholars based in United States, China and Sweden. Frequent co-authors include Valerio Pascucci, Peer‐Timo Bremer, Shusen Liu, Dan Maljovec, Paul Rosen, Lin Yan, Ingrid Hotz, Mustafa Hajij, Carlos Scheidegger and Vivek Srikumar. Their work appears in journals such as Nature Cell Biology, Journal of Lightwave Technology and IEEE Transactions on Visualization and Computer Graphics.
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.