Ashwinkumar Ganesan

465 citations
9 papers · 270 indexed · h-index 5
Topics
Topic Modeling (3 papers)Natural Language Processing Techniques (3 papers)Speech Recognition and Synthesis (2 papers)
Journals
PLoS Computational BiologyIEEE Transactions on Circuits and Systems I Regular PapersMaryland Shared Open Access Repository (USMAI Consortium)
Partner nations
United States

In The Last Decade

Ashwinkumar Ganesan

8 papers receiving 262 citations

Peers

Ashwinkumar Ganesan
Comparison fields: 5 of 93
  • Molecular Biology 93
  • Artificial Intelligence 67
  • Information Systems 35
  • Computer Networks and Communications 30
  • Computer Vision and Pattern Recognition 30
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Dhana Rao United States
Zan Zhang China
Hongguang Fu China
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Ashwinkumar Ganesan relative to Dhana Rao United States Dhana Rao's profile →
Citations per field
00.5×2.7×
Dhana Rao · 1×
Citations per year

Countries citing papers authored by Ashwinkumar Ganesan

Since Specialization
Citations

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

Fields of papers citing papers by Ashwinkumar Ganesan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashwinkumar Ganesan

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

All Works

9 of 9 papers shown
#WorkIndexed citations
1 0
2 4
3 1
4 4
5 10
6 41
7 76
8 12
9 122

About Ashwinkumar Ganesan

Ashwinkumar Ganesan is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 270 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers) and Speech Recognition and Synthesis (2 papers). The work is most often cited by research in Computational Mathematics (3 citations), Microbiology (15 citations) and Signal Processing (23 citations). Ashwinkumar Ganesan has collaborated with scholars based in United States. Frequent co-authors include Tim Oates, James R. White, Julie C. Dunning Hotopp, Kelly Robinson, David R. Riley, Karsten B. Sieber, Tinoosh Mohsenin, Ali Jafari, Sandeep Nair Narayanan and Karuna Pande Joshi. Their work appears in journals such as PLoS Computational Biology, IEEE Transactions on Circuits and Systems I Regular Papers and Maryland Shared Open Access Repository (USMAI Consortium).

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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