Stephen Tian
Impact in
- Control and Systems Engineering top 10%
- Robot Manipulation and Learning
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- Tactile and Sensory Interactions
Papers in
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- Robot Manipulation and Learning 4
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- Multimodal Machine Learning Applications 1
- Advanced Vision and Imaging 1
- Co-authors
- Dinesh Jayaraman (1 shared paper)Mayur Mudigonda (1 shared paper)Chelsea Finn (2 shared papers)Roberto Calandra (1 shared paper)Frederik Ebert (2 shared papers)Sergey Levine (2 shared papers)Anirudha Majumdar (1 shared paper)Yuke Zhu (1 shared paper)
- Journals
- The International Journal of Robotics Research (1 paper)Science Robotics (1 paper)International Conference on Learning Representations (1 paper)
- Partner nations
- United StatesGermanySingapore
In The Last Decade
Stephen Tian
7 papers receiving 172 citations
Stephen Tian's Hit Papers
Peers
Comparison fields: 5 of 40
- Control and Systems Engineering 81
- Cognitive Neuroscience 49
- Computer Vision and Pattern Recognition 39
- Artificial Intelligence 47
- Biomedical Engineering 51
Countries citing papers authored by Stephen Tian
This map shows the geographic impact of Stephen Tian'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 Stephen Tian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen Tian more than expected).
Fields of papers citing papers by Stephen Tian
This network shows the impact of papers produced by Stephen Tian. 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 Stephen Tian. The network helps show where Stephen Tian may publish in the future.
Co-authors
The 25 scholars most cited alongside Stephen Tian, 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 | Foundation models in robotics: Applications, challenges, and the future Hit paper breakdown → | 2024 | 85 |
| 2 | 2019 | 78 | |
| 3 | 2023 | 4 | |
| 4 | 2024 | 4 | |
| 5 | 2025 | 2 | |
| 6 | Model-Based Visual Planning with Self-Supervised Functional Distances | 2021 | 1 |
| 7 | 2025 | 1 | |
| 8 | 2024 | 0 |
About Stephen Tian
Stephen Tian is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering and Mechanical Engineering, having authored 8 papers that have together received 175 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (4 papers), Reinforcement Learning in Robotics (3 papers), Robotics and Sensor-Based Localization (2 papers), Modular Robots and Swarm Intelligence (2 papers), Multimodal Machine Learning Applications (1 paper), Innovations in Concrete and Construction Materials (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and Advanced Vision and Imaging (1 paper). The work is most often cited by research in Control and Systems Engineering (81 citations), Cognitive Neuroscience (49 citations), Computer Vision and Pattern Recognition (39 citations), Artificial Intelligence (47 citations) and Biomedical Engineering (51 citations). Stephen Tian has collaborated with scholars based in United States, Germany and Singapore. Frequent co-authors include Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Frederik Ebert, Sergey Levine, Anirudha Majumdar, Yuke Zhu, Roya Firoozi and Karol Hausman. Their work appears in journals such as The International Journal of Robotics Research, Science Robotics and International Conference on Learning Representations.
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.