Rahul Gupta
- Artificial Intelligence top 1%
- Information Systems top 1%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 2%
- Software top 2%
- Co-authors
- Sunita SarawagiShirish ShevadeShrikanth NarayananAditya KanadeJames AllanShaohua SunDekang LinKevin Murphy
- Topics
- Topic Modeling (21 papers)Natural Language Processing Techniques (15 papers)Emotion and Mood Recognition (11 papers)
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Rahul Gupta
91 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 120
- Artificial Intelligence 1.1k
- Information Systems 507
- Computer Vision and Pattern Recognition 316
- Signal Processing 316
- Software 207
Countries citing papers authored by Rahul Gupta
This map shows the geographic impact of Rahul Gupta'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 Rahul Gupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rahul Gupta more than expected).
Fields of papers citing papers by Rahul Gupta
This network shows the impact of papers produced by Rahul Gupta. 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 Rahul Gupta. The network helps show where Rahul Gupta may publish in the future.
Co-authorship network of co-authors of Rahul Gupta
This figure shows the co-authorship network connecting the top 25 collaborators of Rahul Gupta. A scholar is included among the top collaborators of Rahul Gupta 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 Rahul Gupta. Rahul Gupta 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 | 2 | |
| 3 | 4 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 22 | |
| 7 | 7 | |
| 8 | 4 | |
| 9 | 3 | |
| 10 | 62 | |
| 11 | Neural Attribution for Semantic Bug-Localization in Student Programs | 13 |
| 12 | SCL-UMD at the Medico Task-MediaEval 2017: Transfer Learning based Classification of Medical Images. | 22 |
| 13 | 5 | |
| 14 | Predicting Affect in Music Using Regression Methods on Low Level Features | 1 |
| 15 | 10 | |
| 16 | Affective Feature Design and Predicting Continuous Affective Dimensions from Music | 12 |
| 17 | Joint training for open-domain extraction on the web: exploiting overlap when supervision is limited | 17 |
| 18 | 2 | |
| 19 | 83 | |
| 20 | Topic Models for Summarizing Novelty | 7 |
About Rahul Gupta
Rahul Gupta is a scholar working on Artificial Intelligence, Signal Processing and Experimental and Cognitive Psychology, having authored 94 papers that have together received 1.9k indexed citations. Recurring topics across this work include Topic Modeling (21 papers), Natural Language Processing Techniques (15 papers) and Emotion and Mood Recognition (11 papers). The work is most often cited by research in Software (207 citations), Artificial Intelligence (1.1k citations) and Signal Processing (316 citations). Rahul Gupta has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Sunita Sarawagi, Shirish Shevade, Shrikanth Narayanan, Aditya Kanade, James Allan, Shaohua Sun, Dekang Lin, Kevin Murphy, Robert West and Evgeniy Gabrilovich. Their work appears in journals such as PLoS ONE, IEEE Transactions on Image Processing and Molecular Psychiatry.
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.