Subramanian Ramamoorthy

96 papers receiving 847 citations

Peers

Subramanian Ramamoorthy
Comparison fields: 5 of 118
  • Artificial Intelligence 359
  • Computer Vision and Pattern Recognition 254
  • Control and Systems Engineering 233
  • Biomedical Engineering 118
  • Management Science and Operations Research 91
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Citations per field
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Citations per year

Countries citing papers authored by Subramanian Ramamoorthy

Since Specialization
Citations

This map shows the geographic impact of Subramanian Ramamoorthy'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 Subramanian Ramamoorthy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Subramanian Ramamoorthy more than expected).

Fields of papers citing papers by Subramanian Ramamoorthy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Subramanian Ramamoorthy. 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 Subramanian Ramamoorthy. The network helps show where Subramanian Ramamoorthy may publish in the future.

Co-authorship network of co-authors of Subramanian Ramamoorthy

This figure shows the co-authorship network connecting the top 25 collaborators of Subramanian Ramamoorthy. A scholar is included among the top collaborators of Subramanian Ramamoorthy 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 Subramanian Ramamoorthy. Subramanian Ramamoorthy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 9
3 6
4 17
5
Residual Learning from Demonstration.
2
6
Vid2Param: Online system identification from video for robotics applications
2
7
Learning from demonstration of trajectory preferences through causal modeling and inference
1
8
Towards robust grasps: Using the environment semantics for robotic object affordances
3
9
The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces
4
10 4
11
Are you doing what i think you are doing? criticising uncertain agent models
4
12 13
13
Adapting Interaction Environments to Diverse Users through Online Action Set Selection
6
14 5
15 1
16
Lifelong Learning of Structure in the Space of Policies
4
17 4
18 1
19
A Multitask Representation Using Reusable Local Policy Templates.
5
20 17

About Subramanian Ramamoorthy

Subramanian Ramamoorthy is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 104 papers that have together received 906 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (36 papers), Robot Manipulation and Learning (17 papers) and Human Pose and Action Recognition (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (254 citations), Artificial Intelligence (359 citations) and Control and Systems Engineering (233 citations). Subramanian Ramamoorthy has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Benjamin Rosman, Stefano V. Albrecht, Benjamin Kuipers, Florian T. Pokorny, Karl Friston, Jacob W. Crandall, Andy Clark, Ronald P. A. Petrick, Katrin S. Lohan and Èric Pairet. Their work appears in journals such as Communications of the ACM, Optics Express and Expert Systems with Applications.

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.

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