Vigneshwaran Subbaraju
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Cognitive Neuroscience top 10%
- Computer Networks and Communications top 10%
- Biomedical Engineering
- Co-authors
- Archan MisraDipanjan ChakrabortyKarl AbererZhixian YanSuresh SundaramN. SundararajanSougata SenYoungki Lee
- Topics
- Multimodal Machine Learning Applications (7 papers)EEG and Brain-Computer Interfaces (5 papers)Functional Brain Connectivity Studies (5 papers)
- Partner nations
- SingaporeUnited StatesIndia
In The Last Decade
Vigneshwaran Subbaraju
32 papers receiving 540 citations
Peers
Comparison fields: 5 of 78
- Computer Vision and Pattern Recognition 226
- Electrical and Electronic Engineering 125
- Cognitive Neuroscience 122
- Computer Networks and Communications 102
- Biomedical Engineering 78
Countries citing papers authored by Vigneshwaran Subbaraju
This map shows the geographic impact of Vigneshwaran Subbaraju'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 Vigneshwaran Subbaraju with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vigneshwaran Subbaraju more than expected).
Fields of papers citing papers by Vigneshwaran Subbaraju
This network shows the impact of papers produced by Vigneshwaran Subbaraju. 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 Vigneshwaran Subbaraju. The network helps show where Vigneshwaran Subbaraju may publish in the future.
Co-authorship network of co-authors of Vigneshwaran Subbaraju
This figure shows the co-authorship network connecting the top 25 collaborators of Vigneshwaran Subbaraju. A scholar is included among the top collaborators of Vigneshwaran Subbaraju 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 Vigneshwaran Subbaraju. Vigneshwaran Subbaraju is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 2 | |
| 5 | Predicting Event Memorability from Contextual Visual Semantics | 0 |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 11 | |
| 9 | 5 | |
| 10 | 2 | |
| 11 | VC-I2R@ImageCLEF2017: Ensemble of Deep Learned Features for Lifelog Video Summarization | 7 |
| 12 | 18 | |
| 13 | 1 | |
| 14 | 7 | |
| 15 | 61 | |
| 16 | 1 | |
| 17 | 66 | |
| 18 | 26 | |
| 19 | 15 | |
| 20 | 206 |
About Vigneshwaran Subbaraju
Vigneshwaran Subbaraju is a scholar working on Computer Vision and Pattern Recognition, Computer Science Applications and Human-Computer Interaction, having authored 33 papers that have together received 558 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (7 papers), EEG and Brain-Computer Interfaces (5 papers) and Functional Brain Connectivity Studies (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (226 citations), Human-Computer Interaction (51 citations) and Transportation (62 citations). Vigneshwaran Subbaraju has collaborated with scholars based in Singapore, United States and India. Frequent co-authors include Archan Misra, Dipanjan Chakraborty, Karl Aberer, Zhixian Yan, Suresh Sundaram, N. Sundararajan, Sougata Sen, Youngki Lee, Rajesh Krishna Balan and Srinivasan Seshan. Their work appears in journals such as Scientific Reports, Expert Systems with Applications and European Journal of Neuroscience.
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.