Anirudh Vemula
- Automotive Engineering top 2%
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Safety, Risk, Reliability and Quality top 5%
- Building and Construction top 10%
- Co-authors
- Jean OhKatharina MuellingWen SunMaxim LikhachevJ. Andrew BagnellNikhil S. PatilNaveen AggarwalK. K. Ramakrishnan
- Topics
- Robotic Path Planning Algorithms (4 papers)Robotics and Sensor-Based Localization (3 papers)Reinforcement Learning in Robotics (2 papers)
- Cited by
- Automotive EngineeringSafety, Risk, Reliability and QualityComputer Vision and Pattern Recognition
- Journals
- SHILAP Revista de lepidopterologíaArXiv.orgarXiv (Cornell University)
- Partner nations
- United StatesIndiaMorocco
In The Last Decade
Anirudh Vemula
9 papers receiving 449 citations
Hit Papers
Peers
Comparison fields: 5 of 40
- Automotive Engineering 341
- Computer Vision and Pattern Recognition 295
- Artificial Intelligence 176
- Safety, Risk, Reliability and Quality 136
- Building and Construction 80
Countries citing papers authored by Anirudh Vemula
This map shows the geographic impact of Anirudh Vemula'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 Anirudh Vemula with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anirudh Vemula more than expected).
Fields of papers citing papers by Anirudh Vemula
This network shows the impact of papers produced by Anirudh Vemula. 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 Anirudh Vemula. The network helps show where Anirudh Vemula may publish in the future.
Co-authorship network of co-authors of Anirudh Vemula
This figure shows the co-authorship network connecting the top 25 collaborators of Anirudh Vemula. A scholar is included among the top collaborators of Anirudh Vemula 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 Anirudh Vemula. Anirudh Vemula is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 7 | |
| 3 | 3 | |
| 4 | Provably Efficient Imitation Learning from Observation Alone. | 5 |
| 5 | 4 | |
| 6 | 1 | |
| 7 | Social Attention: Modeling Attention in Human Crowdsbreakdown → | 440 |
| 8 | 3 | |
| 9 | 9 |
About Anirudh Vemula
Anirudh Vemula is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Computer Science Applications, having authored 9 papers that have together received 475 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (4 papers), Robotics and Sensor-Based Localization (3 papers) and Reinforcement Learning in Robotics (2 papers). The work is most often cited by research in Automotive Engineering (341 citations), Safety, Risk, Reliability and Quality (136 citations) and Computer Vision and Pattern Recognition (295 citations). Anirudh Vemula has collaborated with scholars based in United States, India and Morocco. Frequent co-authors include Jean Oh, Katharina Muelling, Wen Sun, Maxim Likhachev, J. Andrew Bagnell, Nikhil S. Patil, Naveen Aggarwal, K. K. Ramakrishnan, Drew Bagnell and Byron Boots. Their work appears in journals such as SHILAP Revista de lepidopterología, ArXiv.org and arXiv (Cornell University).
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.