Sharan Narang
Impact in
- Artificial Intelligence top 10%
- Natural Language Processing Techniques
- Topic Modeling
- Speech Recognition and Synthesis
- Text Readability and Simplification
- Advanced Text Analysis Techniques
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- Multimodal Machine Learning Applications
- Handwritten Text Recognition Techniques
- Video Analysis and Summarization
Papers in
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- Natural Language Processing Techniques 5
- Topic Modeling 4
- Text Readability and Simplification 1
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- Multimodal Machine Learning Applications 3
- Co-authors
- Colin Raffel (2 shared papers)Adam Paul Roberts (2 shared papers)Noah Constant (2 shared papers)Rami Al‐Rfou (1 shared paper)Linting Xue (1 shared paper)Mihir Kale (1 shared paper)Aditya Barua (1 shared paper)Noah Fiedel (2 shared papers)
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- United States
In The Last Decade
Sharan Narang
7 papers receiving 271 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 191
- Computer Vision and Pattern Recognition 81
- Signal Processing 16
- Information Systems 29
- Software 4
Countries citing papers authored by Sharan Narang
This map shows the geographic impact of Sharan Narang'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 Sharan Narang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sharan Narang more than expected).
Fields of papers citing papers by Sharan Narang
This network shows the impact of papers produced by Sharan Narang. 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 Sharan Narang. The network helps show where Sharan Narang may publish in the future.
Co-authors
The 25 scholars most cited alongside Sharan Narang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 152 | |
| 2 | 2021 | 46 | |
| 3 | 2023 | 24 | |
| 4 | 2023 | 22 | |
| 5 | 2023 | 20 | |
| 6 | 2024 | 14 | |
| 7 | End to end speech recognition in English and Mandarin | 2016 | 10 |
About Sharan Narang
Sharan Narang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Geophysics, Ocean Engineering and Mechanical Engineering, having authored 7 papers that have together received 288 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Topic Modeling (4 papers), Multimodal Machine Learning Applications (3 papers), Hydraulic Fracturing and Reservoir Analysis (1 paper), Seismic Imaging and Inversion Techniques (1 paper), Drilling and Well Engineering (1 paper) and Text Readability and Simplification (1 paper). The work is most often cited by research in Artificial Intelligence (191 citations), Computer Vision and Pattern Recognition (81 citations), Signal Processing (16 citations), Information Systems (29 citations) and Software (4 citations). Sharan Narang has collaborated with scholars based in United States. Frequent co-authors include Colin Raffel, Adam Paul Roberts, Noah Constant, Rami Al‐Rfou, Linting Xue, Mihir Kale, Aditya Barua, Noah Fiedel, Yi Hong Tay and Hyung Won Chung. Their work appears in journals such as Transactions of the Association for Computational Linguistics and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.