Johnathan Chiu
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Neural Network Applications
Papers in
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- Safety Systems Engineering in Autonomy 1
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- Formal Methods in Verification 1
- Co-authors
- Parmida Atighehchian (1 shared paper)Jonathan Granskog (1 shared paper)Daniel J. Fremont (1 shared paper)Dragos D. Margineantu (1 shared paper)Sanjit Arunkumar Seshia (1 shared paper)Priya Sundaresan (1 shared paper)Brijen Thananjeyan (1 shared paper)Ken Y. Goldberg (1 shared paper)
- Journals
- Lecture notes in computer science (1 paper)IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (1 paper)
- Partner nations
- United States
In The Last Decade
Johnathan Chiu
3 papers receiving 310 citations
Johnathan Chiu's Hit Papers
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 231
- Health Informatics 4
- Computer Graphics and Computer-Aided Design 7
- Computational Theory and Mathematics 24
- Safety, Risk, Reliability and Quality 14
Countries citing papers authored by Johnathan Chiu
This map shows the geographic impact of Johnathan Chiu'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 Johnathan Chiu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johnathan Chiu more than expected).
Fields of papers citing papers by Johnathan Chiu
This network shows the impact of papers produced by Johnathan Chiu. 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 Johnathan Chiu. The network helps show where Johnathan Chiu may publish in the future.
Co-authors
The 8 scholars most cited alongside Johnathan Chiu, 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 | Structure and Content-Guided Video Synthesis with Diffusion Models Hit paper breakdown → | 2023 | 262 |
| 2 | 2020 | 42 | |
| 3 | 2019 | 20 |
About Johnathan Chiu
Johnathan Chiu is a scholar working on Safety, Risk, Reliability and Quality, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Control and Systems Engineering and Biomedical Engineering, having authored 3 papers that have together received 324 indexed citations. Recurring topics across this work include Formal Methods in Verification (1 paper), Surgical Simulation and Training (1 paper), Robot Manipulation and Learning (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Soft Robotics and Applications (1 paper) and Safety Systems Engineering in Autonomy (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (231 citations), Health Informatics (4 citations), Computer Graphics and Computer-Aided Design (7 citations), Computational Theory and Mathematics (24 citations) and Safety, Risk, Reliability and Quality (14 citations). Johnathan Chiu has collaborated with scholars based in United States. Frequent co-authors include Parmida Atighehchian, Jonathan Granskog, Daniel J. Fremont, Dragos D. Margineantu, Sanjit Arunkumar Seshia, Priya Sundaresan, Brijen Thananjeyan and Ken Y. Goldberg. Their work appears in journals such as Lecture notes in computer science, IEEE/CVF International Conference on Computer Vision (ICCV) and 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE).
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.