Dilip Arumugam

About

Dilip Arumugam is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dilip Arumugam has authored 7 papers receiving a total of 64 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Management Science and Operations Research and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dilip Arumugam’s work include Reinforcement Learning in Robotics (5 papers), Multimodal Machine Learning Applications (2 papers) and Topic Modeling (2 papers). Dilip Arumugam is often cited by papers focused on Reinforcement Learning in Robotics (5 papers), Multimodal Machine Learning Applications (2 papers) and Topic Modeling (2 papers). Dilip Arumugam collaborates with scholars based in United States, Canada and Mexico. Dilip Arumugam's co-authors include Michael L. Littman, David Abel, Lawson L. S. Wong, Siddharth Karamcheti, Stefanie Tellex, Nakul Gopalan, Kavosh Asadi, Doina Precup, Mark K. Ho and Noah D. Goodman and has published in prestigious journals such as Autonomous Robots, PubMed Central and arXiv (Cornell University).

In The Last Decade

Co-authorship network of co-authors of Dilip Arumugam i

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Fields of papers citing papers by Dilip Arumugam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Dilip Arumugam

Since Specialization

Citations

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

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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