Chandra Bhagavatula
- Artificial Intelligence top 1%
- Topic Modeling 32
- Natural Language Processing Techniques 31
- Advanced Text Analysis Techniques 8
- Semantic Web and Ontologies 5
- Health Informatics top 10%
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- Multimodal Machine Learning Applications 8
- Information Systems top 5%
- General Social Sciences top 5%
- Computational and Text Analysis Methods 2
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- Biomedical Text Mining and Ontologies 4
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- Ethics and Social Impacts of AI 3
Chandra Bhagavatula
37 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 75
- Artificial Intelligence 1.0k
- Health Informatics 19
- Computer Vision and Pattern Recognition 263
- Information Systems 150
- General Social Sciences 14
Countries citing papers authored by Chandra Bhagavatula
This map shows the geographic impact of Chandra Bhagavatula'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 Chandra Bhagavatula with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chandra Bhagavatula more than expected).
Fields of papers citing papers by Chandra Bhagavatula
This network shows the impact of papers produced by Chandra Bhagavatula. 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 Chandra Bhagavatula. The network helps show where Chandra Bhagavatula may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Chandra Bhagavatula, 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 | 2025 | 5 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2023 | 8 | |
| 5 | 2023 | 5 | |
| 6 | 2023 | 11 | |
| 7 | 2023 | 2 | |
| 8 | 2022 | 98 | |
| 9 | 2022 | 3 | |
| 10 | 2022 | 52 | |
| 11 | On-the-Fly Controlled Text Generation with Experts and Anti-Experts. | 2021 | 5 |
| 12 | 2021 | 94 | |
| 13 | 2021 | 39 | |
| 14 | 2021 | 5 | |
| 15 | 2021 | 10 | |
| 16 | Visual Commonsense Graphs: Reasoning about the Dynamic Context of a Still Image. | 2020 | 2 |
| 17 | G-DAUG: Generative Data Augmentation for Commonsense Reasoning | 2020 | 5 |
| 18 | 2019 | 144 | |
| 19 | 2019 | 56 | |
| 20 | 2018 | 98 |
About Chandra Bhagavatula
Chandra Bhagavatula is a scholar working on Artificial Intelligence, General Social Sciences, Computer Vision and Pattern Recognition, Safety Research and Communication, having authored 38 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (32 papers), Natural Language Processing Techniques (31 papers), Advanced Text Analysis Techniques (8 papers), Multimodal Machine Learning Applications (8 papers), Semantic Web and Ontologies (5 papers), Biomedical Text Mining and Ontologies (4 papers), Ethics and Social Impacts of AI (3 papers) and Computational and Text Analysis Methods (2 papers). The work is most often cited by research in Artificial Intelligence (1.0k citations), Health Informatics (19 citations), Computer Vision and Pattern Recognition (263 citations), Information Systems (150 citations) and General Social Sciences (14 citations). Chandra Bhagavatula has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Yejin Choi, Ronan Le Bras, Keisuke Sakaguchi, Lifu Huang, Ximing Lu, Waleed Ammar, Russell Power, Doug Downey, Sergey Feldman and Jena D. Hwang. Their work appears in journals such as Computational Linguistics, Communications of the ACM, Nature Machine Intelligence, Empirical Methods in Natural Language Processing and arXiv (Cornell University).
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.