Tejaswini Pedapati
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Signal Processing
- Computer Networks and Communications
- Sociology and Political Science
- Co-authors
- Peter J. HaasSrinivasan ParthasarathyÇağatay DemiralpPin‐Yu ChenYizhou SunPrasanna SattigeriMartin WistubaHorst Samulowitz
- Topics
- Machine Learning and Data Classification (4 papers)Time Series Analysis and Forecasting (3 papers)Data Stream Mining Techniques (3 papers)
- Journals
- Nature CommunicationsProceedings of the VLDB EndowmentIEEE Journal on Emerging and Selected Topics in Circuits and Systems
- Partner nations
- United StatesIrelandMexico
In The Last Decade
Tejaswini Pedapati
11 papers receiving 111 citations
Peers
Comparison fields: 5 of 39
- Computer Vision and Pattern Recognition 78
- Artificial Intelligence 50
- Signal Processing 22
- Computer Networks and Communications 10
- Sociology and Political Science 9
Countries citing papers authored by Tejaswini Pedapati
This map shows the geographic impact of Tejaswini Pedapati'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 Tejaswini Pedapati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tejaswini Pedapati more than expected).
Fields of papers citing papers by Tejaswini Pedapati
This network shows the impact of papers produced by Tejaswini Pedapati. 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 Tejaswini Pedapati. The network helps show where Tejaswini Pedapati may publish in the future.
Co-authorship network of co-authors of Tejaswini Pedapati
This figure shows the co-authorship network connecting the top 25 collaborators of Tejaswini Pedapati. A scholar is included among the top collaborators of Tejaswini Pedapati 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 Tejaswini Pedapati. Tejaswini Pedapati 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 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 7 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 3 | |
| 10 | 2 | |
| 11 | 6 | |
| 12 | 14 | |
| 13 | 1 | |
| 14 | 74 |
About Tejaswini Pedapati
Tejaswini Pedapati is a scholar working on Artificial Intelligence, Signal Processing and Computer Science Applications, having authored 14 papers that have together received 112 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (4 papers), Time Series Analysis and Forecasting (3 papers) and Data Stream Mining Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (78 citations), Signal Processing (22 citations) and Artificial Intelligence (50 citations). Tejaswini Pedapati has collaborated with scholars based in United States, Ireland and Mexico. Frequent co-authors include Peter J. Haas, Srinivasan Parthasarathy, Çağatay Demiralp, Pin‐Yu Chen, Yizhou Sun, Prasanna Sattigeri, Martin Wistuba, Horst Samulowitz, Nalini Ratha and Samuel Thomas. Their work appears in journals such as Nature Communications, Proceedings of the VLDB Endowment and IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
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.