Shusen Liu
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Materials Chemistry
- Computational Theory and Mathematics top 5%
- Electrical and Electronic Engineering
- Co-authors
- Peer‐Timo BremerValerio PascucciBei WangDan MaljovecBhavya KailkhuraBrian GallagherT. Yong-Jin HanAnna M. Hiszpanski
- Topics
- Data Visualization and Analytics (9 papers)Explainable Artificial Intelligence (XAI) (5 papers)Computer Graphics and Visualization Techniques (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignArtificial Intelligence
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Shusen Liu
35 papers receiving 975 citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Computer Vision and Pattern Recognition 366
- Artificial Intelligence 335
- Materials Chemistry 181
- Computational Theory and Mathematics 106
- Electrical and Electronic Engineering 70
Countries citing papers authored by Shusen Liu
This map shows the geographic impact of Shusen Liu'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 Shusen Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shusen Liu more than expected).
Fields of papers citing papers by Shusen Liu
This network shows the impact of papers produced by Shusen Liu. 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 Shusen Liu. The network helps show where Shusen Liu may publish in the future.
Co-authorship network of co-authors of Shusen Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Shusen Liu. A scholar is included among the top collaborators of Shusen Liu 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 Shusen Liu. Shusen Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 18 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 3 | |
| 9 | Explainable machine learning in materials sciencebreakdown → | 214 |
| 10 | 4 | |
| 11 | 24 | |
| 12 | 11 | |
| 13 | 52 | |
| 14 | 1 | |
| 15 | Q|SI⟩ : A Quantum Programming Environment. | 11 |
| 16 | 67 | |
| 17 | 15 | |
| 18 | 8 | |
| 19 | 45 | |
| 20 | 47 |
About Shusen Liu
Shusen Liu is a scholar working on Computer Graphics and Computer-Aided Design, Structural Biology and Computer Vision and Pattern Recognition, having authored 37 papers that have together received 1.0k indexed citations. Recurring topics across this work include Data Visualization and Analytics (9 papers), Explainable Artificial Intelligence (XAI) (5 papers) and Computer Graphics and Visualization Techniques (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (366 citations), Computer Graphics and Computer-Aided Design (53 citations) and Artificial Intelligence (335 citations). Shusen Liu has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Peer‐Timo Bremer, Valerio Pascucci, Bei Wang, Dan Maljovec, Bhavya Kailkhura, Brian Gallagher, T. Yong-Jin Han, Anna M. Hiszpanski, Xiaoting Zhong and Vivek Srikumar. Their work appears in journals such as Optics Express, Medical Physics and Journal of Chemical Information and Modeling.
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.