Avanika Narayan
Impact in
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- Data Quality and Management
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Semantic Web and Ontologies
- Advanced Graph Neural Networks
Papers in
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- Topic Modeling 3
- Domain Adaptation and Few-Shot Learning 1
- Speech and dialogue systems 1
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- Multimodal Machine Learning Applications 2
- Co-authors
- Christopher Ré (5 shared papers)Laurel Orr (1 shared paper)Ines Chami (1 shared paper)Brandon Yang (1 shared paper)Immanuel Trummer (1 shared paper)Michael Wornow (2 shared papers)Kush Bhatia (1 shared paper)Christopher De (1 shared paper)
- Journals
- Proceedings of the VLDB Endowment (3 papers)Annual Meeting of the Special Interest Group on Discourse and Dialogue (1 paper)neural information processing systems (2 papers)
- Partner nations
- United States
In The Last Decade
Avanika Narayan
5 papers receiving 159 citations
Peers
Comparison fields: 5 of 25
- Management Science and Operations Research 47
- Artificial Intelligence 78
- Computer Networks and Communications 50
- Information Systems 34
- Software 5
Countries citing papers authored by Avanika Narayan
This map shows the geographic impact of Avanika Narayan'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 Avanika Narayan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avanika Narayan more than expected).
Fields of papers citing papers by Avanika Narayan
This network shows the impact of papers produced by Avanika Narayan. 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 Avanika Narayan. The network helps show where Avanika Narayan may publish in the future.
Co-authors
The 10 scholars most cited alongside Avanika Narayan, 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 | 108 | |
| 2 | 2023 | 40 | |
| 3 | 2024 | 7 | |
| 4 | 2023 | 3 | |
| 5 | 2022 | 2 | |
| 6 | 2024 | 0 |
About Avanika Narayan
Avanika Narayan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Management Information Systems and Management Science and Operations Research, having authored 6 papers that have together received 160 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Multimodal Machine Learning Applications (2 papers), Robotic Process Automation Applications (1 paper), Data Quality and Management (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Advanced Database Systems and Queries (1 paper), Speech and dialogue systems (1 paper) and Web Data Mining and Analysis (1 paper). The work is most often cited by research in Management Science and Operations Research (47 citations), Artificial Intelligence (78 citations), Computer Networks and Communications (50 citations), Information Systems (34 citations) and Software (5 citations). Avanika Narayan has collaborated with scholars based in United States. Frequent co-authors include Christopher Ré, Laurel Orr, Ines Chami, Brandon Yang, Immanuel Trummer, Michael Wornow, Kush Bhatia, Christopher De, Nigam Haresh Shah and S. Shyam Sundar. Their work appears in journals such as Proceedings of the VLDB Endowment, Annual Meeting of the Special Interest Group on Discourse and Dialogue and neural information processing 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.