Shusen Liu
-
- Data Visualization and Analytics 9
- Generative Adversarial Networks and Image Synthesis 3
-
- Computer Graphics and Visualization Techniques 4
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI) 5
- Quantum Computing Algorithms and Architecture 4
- Topic Modeling 3
- Quantum Information and Cryptography 3
- Biophysics top 10%
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- Machine Learning in Materials Science 4
- Co-authors
- Peer‐Timo BremerValerio PascucciBei WangDan MaljovecBhavya KailkhuraBrian GallagherT. Yong-Jin HanAnna M. Hiszpanski
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignArtificial Intelligence
- Journals
- Optics Express (1 paper)Medical Physics (1 paper)Journal of Chemical Information and Modeling (1 paper)
- 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
- Computer Graphics and Computer-Aided Design 53
- Artificial Intelligence 335
- Biophysics 48
- Health Informatics 10
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
The 25 scholars most cited alongside Shusen Liu, 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 | 2024 | 8 | |
| 2 | 2024 | 18 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 2 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 3 | |
| 8 | 2022 | 3 | |
| 9 | Explainable machine learning in materials sciencebreakdown → | 2022 | 214 |
| 10 | 2022 | 4 | |
| 11 | 2021 | 24 | |
| 12 | 2020 | 11 | |
| 13 | 2020 | 52 | |
| 14 | 2019 | 1 | |
| 15 | Q|SI⟩ : A Quantum Programming Environment. | 2017 | 11 |
| 16 | 2017 | 67 | |
| 17 | 2015 | 15 | |
| 18 | 2012 | 8 | |
| 19 | 2012 | 45 | |
| 20 | 2008 | 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), Computer Graphics and Visualization Techniques (4 papers), Quantum Computing Algorithms and Architecture (4 papers), Machine Learning in Materials Science (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers), Topic Modeling (3 papers) and Quantum Information and Cryptography (3 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.