Sudha Rao
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Molecular Biology
- Sociology and Political Science
- Co-authors
- Joel TetreaultHal DauméDaniel MarcuKevin KnightMarine CarpuatXing NiuPhilip ResnikAllyson Ettinger
- Topics
- Topic Modeling (11 papers)Natural Language Processing Techniques (11 papers)Text Readability and Simplification (3 papers)
- Journals
- Edinburgh Research ExplorerMeeting of the Association for Computational LinguisticsInternational Conference on Computational Linguistics
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Sudha Rao
14 papers receiving 371 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 376
- Computer Vision and Pattern Recognition 76
- Information Systems 62
- Molecular Biology 28
- Sociology and Political Science 12
Countries citing papers authored by Sudha Rao
This map shows the geographic impact of Sudha Rao'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 Sudha Rao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sudha Rao more than expected).
Fields of papers citing papers by Sudha Rao
This network shows the impact of papers produced by Sudha Rao. 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 Sudha Rao. The network helps show where Sudha Rao may publish in the future.
Co-authorship network of co-authors of Sudha Rao
This figure shows the co-authorship network connecting the top 25 collaborators of Sudha Rao. A scholar is included among the top collaborators of Sudha Rao 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 Sudha Rao. Sudha Rao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | Controlling the Specificity of Clarification Question Generation. | 2 |
| 5 | 19 | |
| 6 | 5 | |
| 7 | 176 | |
| 8 | Multi-Task Neural Models for Translating Between Styles Within and Across Languages | 25 |
| 9 | 89 | |
| 10 | 4 | |
| 11 | 50 | |
| 12 | 2 | |
| 13 | 13 | |
| 14 | 4 |
About Sudha Rao
Sudha Rao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 14 papers that have together received 403 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Natural Language Processing Techniques (11 papers) and Text Readability and Simplification (3 papers). The work is most often cited by research in Artificial Intelligence (376 citations), Computer Vision and Pattern Recognition (76 citations) and Information Systems (62 citations). Sudha Rao has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Joel Tetreault, Hal Daumé, Daniel Marcu, Kevin Knight, Marine Carpuat, Xing Niu, Philip Resnik, Allyson Ettinger, Paul Smolensky and Yichen Jiang. Their work appears in journals such as Edinburgh Research Explorer, Meeting of the Association for Computational Linguistics and International Conference on Computational Linguistics.
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.