Ramakrishnan Kannan

863 total citations
51 papers, 484 citations indexed

About

Ramakrishnan Kannan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ramakrishnan Kannan has authored 51 papers receiving a total of 484 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 16 papers in Computer Vision and Pattern Recognition and 9 papers in Information Systems. Recurrent topics in Ramakrishnan Kannan's work include Parallel Computing and Optimization Techniques (8 papers), Tensor decomposition and applications (6 papers) and Face and Expression Recognition (6 papers). Ramakrishnan Kannan is often cited by papers focused on Parallel Computing and Optimization Techniques (8 papers), Tensor decomposition and applications (6 papers) and Face and Expression Recognition (6 papers). Ramakrishnan Kannan collaborates with scholars based in United States, South Korea and India. Ramakrishnan Kannan's co-authors include Haesun Park, Grey Ballard, Vipin Kumar, Anuj Karpatne, Mariya Ishteva, Jacob Hinkle, Arvind Ramanathan, Edwin Pednault, M. Todd Young and Amol Ghoting and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Parallel and Distributed Systems and ACM Transactions on Mathematical Software.

In The Last Decade

Ramakrishnan Kannan

46 papers receiving 471 citations

Peers

Ramakrishnan Kannan
Comparison fields: 5 of 90
  • Artificial Intelligence 155
  • Computer Vision and Pattern Recognition 108
  • Information Systems 91
  • Materials Chemistry 71
  • Computer Networks and Communications 68
Replace Minjie Wang with:
Minjie Wang China
Cheng Cheng United States
Stephan Zheng United States
Hao Lu United States
Maryam Mehri Dehnavi Canada
Jian Gong China
Jiarui Fang China
Mohamed Wahib Japan
Minjie Wang China View profile →
Citations per field, relative to Ramakrishnan Kannan
Ramakrishnan Kannan · 1×
Citations per year, relative to Ramakrishnan Kannan
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Countries citing papers authored by Ramakrishnan Kannan

Since Specialization
Citations

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

Fields of papers citing papers by Ramakrishnan Kannan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramakrishnan Kannan

This figure shows the co-authorship network connecting the top 25 collaborators of Ramakrishnan Kannan. A scholar is included among the top collaborators of Ramakrishnan Kannan 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 Ramakrishnan Kannan. Ramakrishnan Kannan 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
# Work Indexed citations
1 0
2 2
3 1
4 1
5 0
6 2
7 7
8 0
9 2
10 2
11 1
12 43
13 19
14 27
15 4
16 3
17 11
18 43
19 36
20
CRM Analytics Framework
0

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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