S. Sundararajan
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Computational Mechanics top 10%
- Control and Systems Engineering top 10%
- Computational Theory and Mathematics top 10%
- Co-authors
- S. Sathiya KeerthiChih‐Jen LinCho‐Jui HsiehKai‐Wei ChangShirish ShevadeP. BalamuruganDhruv MahajanYu-Hsiang Lin
- Topics
- Gaussian Processes and Bayesian Inference (4 papers)Face and Expression Recognition (4 papers)Text and Document Classification Technologies (3 papers)
- Partner nations
- IndiaUnited StatesTaiwan
In The Last Decade
S. Sundararajan
13 papers receiving 802 citations
Hit Papers
Peers
Comparison fields: 5 of 104
- Artificial Intelligence 569
- Computer Vision and Pattern Recognition 385
- Computational Mechanics 137
- Control and Systems Engineering 68
- Computational Theory and Mathematics 53
Countries citing papers authored by S. Sundararajan
This map shows the geographic impact of S. Sundararajan'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 S. Sundararajan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Sundararajan more than expected).
Fields of papers citing papers by S. Sundararajan
This network shows the impact of papers produced by S. Sundararajan. 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 S. Sundararajan. The network helps show where S. Sundararajan may publish in the future.
Co-authorship network of co-authors of S. Sundararajan
This figure shows the co-authorship network connecting the top 25 collaborators of S. Sundararajan. A scholar is included among the top collaborators of S. Sundararajan 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 S. Sundararajan. S. Sundararajan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 13 | |
| 6 | 2 | |
| 7 | 2 | |
| 8 | 2 | |
| 9 | Simulation analysis model and equipment selection in continuous surface mining systems | 2 |
| 10 | A Functional Approximation Based Distributed Learning Algorithm. | 4 |
| 11 | 15 | |
| 12 | Semi-supervised classification using sparse Gaussian process regression | 3 |
| 13 | 0 | |
| 14 | A dual coordinate descent method for large-scale linear SVMbreakdown → | 592 |
| 15 | 117 | |
| 16 | 5 | |
| 17 | 87 |
About S. Sundararajan
S. Sundararajan is a scholar working on Artificial Intelligence, Discrete Mathematics and Combinatorics and Health Information Management, having authored 17 papers that have together received 851 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (4 papers), Face and Expression Recognition (4 papers) and Text and Document Classification Technologies (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (385 citations), Artificial Intelligence (569 citations) and Computational Mathematics (7 citations). S. Sundararajan has collaborated with scholars based in India, United States and Taiwan. Frequent co-authors include S. Sathiya Keerthi, Chih‐Jen Lin, Cho‐Jui Hsieh, Kai‐Wei Chang, Shirish Shevade, P. Balamurugan, Dhruv Mahajan, Yu-Hsiang Lin, Chien-Chih Wang and R Harikrishnan. Their work appears in journals such as Neural Computation, SN Computer Science and IAES International Journal of Artificial Intelligence.
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.