Binh Tang
Impact in
- Structural Biology top 10%
- Advanced Electron Microscopy Techniques and Applications
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- Electron and X-Ray Spectroscopy Techniques
Papers in
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- Impact of Light on Environment and Health 2
- Land Use and Ecosystem Services 2
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- Machine Learning in Materials Science 2
- Co-authors
- David S. Matteson (5 shared papers)Yanyan Liu (2 shared papers)Joshua Vincent (2 shared papers)Peter A. Crozier (2 shared papers)Carlos Fernandez‐Granda (2 shared papers)Eero P. Simoncelli (2 shared papers)Ramón Manzorro (2 shared papers)Ying Sun (1 shared paper)
- Journals
- IEEE Transactions on Computational Imaging (1 paper)Applied Economic Perspectives and Policy (1 paper)Microscopy and Microanalysis (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesIndia
In The Last Decade
Binh Tang
5 papers receiving 63 citations
Peers
Comparison fields: 5 of 49
- Structural Biology 13
- Surfaces, Coatings and Films 12
- Media Technology 7
- Biophysics 4
- Modeling and Simulation 3
Countries citing papers authored by Binh Tang
This map shows the geographic impact of Binh Tang'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 Binh Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Binh Tang more than expected).
Fields of papers citing papers by Binh Tang
This network shows the impact of papers produced by Binh Tang. 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 Binh Tang. The network helps show where Binh Tang may publish in the future.
Co-authors
The 9 scholars most cited alongside Binh Tang, 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 | 2022 | 26 | |
| 2 | Probabilistic Transformer For Time Series Analysis | 2021 | 20 |
| 3 | 2021 | 13 | |
| 4 | Dynamic Poverty Prediction with Vegetation Index | 2018 | 5 |
| 5 | 2021 | 1 |
About Binh Tang
Binh Tang is a scholar working on Global and Planetary Change, Materials Chemistry, Structural Biology, Computer Vision and Pattern Recognition and Signal Processing, having authored 5 papers that have together received 65 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (2 papers), Impact of Light on Environment and Health (2 papers), Land Use and Ecosystem Services (2 papers), Human Mobility and Location-Based Analysis (1 paper), Electron and X-Ray Spectroscopy Techniques (1 paper), Cell Image Analysis Techniques (1 paper), Time Series Analysis and Forecasting (1 paper) and Image and Signal Denoising Methods (1 paper). The work is most often cited by research in Structural Biology (13 citations), Surfaces, Coatings and Films (12 citations), Media Technology (7 citations), Biophysics (4 citations) and Modeling and Simulation (3 citations). Binh Tang has collaborated with scholars based in United States and India. Frequent co-authors include David S. Matteson, Yanyan Liu, Joshua Vincent, Peter A. Crozier, Carlos Fernandez‐Granda, Eero P. Simoncelli, Ramón Manzorro, Ying Sun and Mitesh M. Khapra. Their work appears in journals such as IEEE Transactions on Computational Imaging, Applied Economic Perspectives and Policy, Microscopy and Microanalysis and Neural Information Processing Systems.
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.