Corey Lynch
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 5%
- Biomedical Engineering
- Aerospace Engineering
- Co-authors
- Pierre SermanetSergey LevineJasmine HsuStefan SchaalYevgen ChebotarEric JangJonathan TompsonJosh Attenberg
- Topics
- Multimodal Machine Learning Applications (6 papers)Human Pose and Action Recognition (5 papers)Advanced Vision and Imaging (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceControl and Systems Engineering
- Journals
- IEEE Robotics and Automation Letters2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Partner nations
- United States
In The Last Decade
Corey Lynch
12 papers receiving 527 citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Artificial Intelligence 315
- Computer Vision and Pattern Recognition 303
- Control and Systems Engineering 180
- Biomedical Engineering 35
- Aerospace Engineering 29
Countries citing papers authored by Corey Lynch
This map shows the geographic impact of Corey Lynch'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 Corey Lynch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Corey Lynch more than expected).
Fields of papers citing papers by Corey Lynch
This network shows the impact of papers produced by Corey Lynch. 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 Corey Lynch. The network helps show where Corey Lynch may publish in the future.
Co-authorship network of co-authors of Corey Lynch
This figure shows the co-authorship network connecting the top 25 collaborators of Corey Lynch. A scholar is included among the top collaborators of Corey Lynch 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 Corey Lynch. Corey Lynch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Interactive Language: Talking to Robots in Real Timebreakdown → | 46 |
| 2 | 3 | |
| 3 | 4 | |
| 4 | 9 | |
| 5 | 59 | |
| 6 | 7 | |
| 7 | Time-Contrastive Networks: Self-Supervised Learning from Videobreakdown → | 305 |
| 8 | 27 | |
| 9 | 63 | |
| 10 | Time-Contrastive Networks: Self-Supervised Learning from Multi-View Observation | 6 |
| 11 | 22 | |
| 12 | 1 |
About Corey Lynch
Corey Lynch is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Biophysics, having authored 12 papers that have together received 552 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (6 papers), Human Pose and Action Recognition (5 papers) and Advanced Vision and Imaging (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (303 citations), Artificial Intelligence (315 citations) and Control and Systems Engineering (180 citations). Corey Lynch has collaborated with scholars based in United States. Frequent co-authors include Pierre Sermanet, Sergey Levine, Jasmine Hsu, Stefan Schaal, Yevgen Chebotar, Eric Jang, Jonathan Tompson, Josh Attenberg, Tianli Ding and Debidatta Dwibedi. Their work appears in journals such as IEEE Robotics and Automation Letters and 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.