E. C. G. Sudarshan
- Atomic and Molecular Physics, and Optics top 0.1%
- Artificial Intelligence top 0.1%
- Statistical and Nonlinear Physics top 0.1%
- Nuclear and High Energy Physics top 1%
- Astronomy and Astrophysics top 2%
- Co-authors
- Vittorio GoriniAndrzej KossakowskiB. MisraJohn R. KlauderMarvin M. MillerThomas F. JordanCharles B. ChiuIan Duck
- Topics
- Quantum Mechanics and Applications (57 papers)Quantum Information and Cryptography (29 papers)Particle physics theoretical and experimental studies (28 papers)
- Cited by
- Statistical and Nonlinear PhysicsAtomic and Molecular Physics, and OpticsArtificial Intelligence
- Partner nations
- United StatesIndiaItaly
In The Last Decade
E. C. G. Sudarshan
185 papers receiving 9.0k citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Atomic and Molecular Physics, and Optics 6.9k
- Artificial Intelligence 4.3k
- Statistical and Nonlinear Physics 3.0k
- Nuclear and High Energy Physics 1.5k
- Astronomy and Astrophysics 1.1k
Countries citing papers authored by E. C. G. Sudarshan
This map shows the geographic impact of E. C. G. Sudarshan'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 E. C. G. Sudarshan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E. C. G. Sudarshan more than expected).
Fields of papers citing papers by E. C. G. Sudarshan
This network shows the impact of papers produced by E. C. G. Sudarshan. 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 E. C. G. Sudarshan. The network helps show where E. C. G. Sudarshan may publish in the future.
Co-authorship network of co-authors of E. C. G. Sudarshan
This figure shows the co-authorship network connecting the top 25 collaborators of E. C. G. Sudarshan. A scholar is included among the top collaborators of E. C. G. Sudarshan 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 E. C. G. Sudarshan. E. C. G. Sudarshan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 42 | |
| 4 | An Empirical Analysis of Image Compression with Huffman Coding Technique on GPGPU | 0 |
| 5 | 9 | |
| 6 | 33 | |
| 7 | 1 | |
| 8 | 12 | |
| 9 | 18 | |
| 10 | 288 | |
| 11 | 26 | |
| 12 | 17 | |
| 13 | 2 | |
| 14 | 10 | |
| 15 | 1 | |
| 16 | 7 | |
| 17 | 1 | |
| 18 | 86 | |
| 19 | 274 | |
| 20 | 240 |
About E. C. G. Sudarshan
E. C. G. Sudarshan is a scholar working on Nuclear and High Energy Physics, Statistical and Nonlinear Physics and Atomic and Molecular Physics, and Optics, having authored 192 papers that have together received 9.7k indexed citations. Recurring topics across this work include Quantum Mechanics and Applications (57 papers), Quantum Information and Cryptography (29 papers) and Particle physics theoretical and experimental studies (28 papers). The work is most often cited by research in Statistical and Nonlinear Physics (3.0k citations), Atomic and Molecular Physics, and Optics (6.9k citations) and Artificial Intelligence (4.3k citations). E. C. G. Sudarshan has collaborated with scholars based in United States, India and Italy. Frequent co-authors include Vittorio Gorini, Andrzej Kossakowski, B. Misra, John R. Klauder, Marvin M. Miller, Thomas F. Jordan, Charles B. Chiu, Ian Duck, N. Mukunda and K. Johnson. Their work appears in journals such as Nature, Physical Review Letters and Reviews of Modern Physics.
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.