Suraj Nair
- Artificial Intelligence top 10%
- Control and Systems Engineering top 10%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Automotive Engineering
- Co-authors
- Chelsea FinnLi Fei-FeiMichael LuoKrishnan SrinivasanJoseph E. GonzalezJulian IbarzAshwin BalakrishnaBrijen Thananjeyan
- Topics
- Multimodal Machine Learning Applications (4 papers)Generative Adversarial Networks and Image Synthesis (2 papers)Advanced Vision and Imaging (2 papers)
- Cited by
- Control and Systems EngineeringComputer Vision and Pattern RecognitionArtificial Intelligence
- Journals
- IEEE Robotics and Automation LettersmediaTUM (Technical University of Munich)International Conference on Learning Representations
- Partner nations
- United StatesGermanySingapore
In The Last Decade
Suraj Nair
8 papers receiving 256 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 146
- Control and Systems Engineering 118
- Computer Vision and Pattern Recognition 101
- Biomedical Engineering 38
- Automotive Engineering 28
Countries citing papers authored by Suraj Nair
This map shows the geographic impact of Suraj Nair'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 Suraj Nair with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suraj Nair more than expected).
Fields of papers citing papers by Suraj Nair
This network shows the impact of papers produced by Suraj Nair. 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 Suraj Nair. The network helps show where Suraj Nair may publish in the future.
Co-authorship network of co-authors of Suraj Nair
This figure shows the co-authorship network connecting the top 25 collaborators of Suraj Nair. A scholar is included among the top collaborators of Suraj Nair 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 Suraj Nair. Suraj Nair is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 14 | |
| 5 | Model-Based Visual Planning with Self-Supervised Functional Distances | 1 |
| 6 | 108 | |
| 7 | 44 | |
| 8 | 75 | |
| 9 | 5 | |
| 10 | 11 | |
| 11 | 0 |
About Suraj Nair
Suraj Nair is a scholar working on Computer Vision and Pattern Recognition, General Social Sciences and Computer Science Applications, having authored 11 papers that have together received 264 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Control and Systems Engineering (118 citations), Computer Vision and Pattern Recognition (101 citations) and Artificial Intelligence (146 citations). Suraj Nair has collaborated with scholars based in United States, Germany and Singapore. Frequent co-authors include Chelsea Finn, Li Fei-Fei, Michael Luo, Krishnan Srinivasan, Joseph E. Gonzalez, Julian Ibarz, Ashwin Balakrishna, Brijen Thananjeyan, Ken Goldberg and Minho Hwang. Their work appears in journals such as IEEE Robotics and Automation Letters, mediaTUM (Technical University of Munich) 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.