Vijaykumar Gullapalli
- Artificial Intelligence top 5%
- Control and Systems Engineering top 5%
- Cognitive Neuroscience top 10%
- Biomedical Engineering
- Computational Theory and Mathematics top 10%
- Co-authors
- Judy A. FranklinAndrew G. BartoNeil E. BerthierRachel K. CliftonDaniel D. McCallDaniel J. RobinRoderic A. GrupenJack Gelfand
- Topics
- Reinforcement Learning in Robotics (10 papers)Robot Manipulation and Learning (7 papers)Evolutionary Algorithms and Applications (5 papers)
- Partner nations
- United States
In The Last Decade
Vijaykumar Gullapalli
16 papers receiving 655 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 348
- Control and Systems Engineering 290
- Cognitive Neuroscience 184
- Biomedical Engineering 165
- Computational Theory and Mathematics 71
Countries citing papers authored by Vijaykumar Gullapalli
This map shows the geographic impact of Vijaykumar Gullapalli'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 Vijaykumar Gullapalli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vijaykumar Gullapalli more than expected).
Fields of papers citing papers by Vijaykumar Gullapalli
This network shows the impact of papers produced by Vijaykumar Gullapalli. 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 Vijaykumar Gullapalli. The network helps show where Vijaykumar Gullapalli may publish in the future.
Co-authorship network of co-authors of Vijaykumar Gullapalli
This figure shows the co-authorship network connecting the top 25 collaborators of Vijaykumar Gullapalli. A scholar is included among the top collaborators of Vijaykumar Gullapalli 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 Vijaykumar Gullapalli. Vijaykumar Gullapalli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 25 | |
| 2 | 26 | |
| 3 | 4 | |
| 4 | 16 | |
| 5 | 19 | |
| 6 | 6 | |
| 7 | 137 | |
| 8 | 2 | |
| 9 | 16 | |
| 10 | 15 | |
| 11 | 141 | |
| 12 | Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms | 23 |
| 13 | Learning Control Under Extreme Uncertainty | 21 |
| 14 | Reinforcement learning and its application to control | 46 |
| 15 | 214 | |
| 16 | A Stochastic Algorithm for Learning Real-valued Functions via Reinforcement | 1 |
About Vijaykumar Gullapalli
Vijaykumar Gullapalli is a scholar working on Artificial Intelligence, Control and Systems Engineering and Software, having authored 16 papers that have together received 712 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Robot Manipulation and Learning (7 papers) and Evolutionary Algorithms and Applications (5 papers). The work is most often cited by research in Control and Systems Engineering (290 citations), Artificial Intelligence (348 citations) and Cognitive Neuroscience (184 citations). Vijaykumar Gullapalli has collaborated with scholars based in United States. Frequent co-authors include Judy A. Franklin, Andrew G. Barto, Neil E. Berthier, Rachel K. Clifton, Daniel D. McCall, Daniel J. Robin, Roderic A. Grupen, Jack Gelfand, William W. Wilson and Stephen H. Lane. Their work appears in journals such as Annals of the New York Academy of Sciences, Neurocomputing and Neural Networks.
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.