Sunil Gupta
- Artificial Intelligence top 5%
- Surgery
- Radiology, Nuclear Medicine and Imaging top 10%
- Epidemiology
- Health Informatics top 1%
- Co-authors
- Svetha VenkateshDinh PhungSantu RanaTruyen TranWei LuoAlistair ShiltonNevenka DimitrovaTu Bao Ho
- Topics
- Domain Adaptation and Few-Shot Learning (10 papers)Machine Learning in Healthcare (10 papers)Bayesian Methods and Mixture Models (9 papers)
- Partner nations
- AustraliaUnited StatesIndia
In The Last Decade
Sunil Gupta
42 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 158
- Artificial Intelligence 428
- Surgery 181
- Radiology, Nuclear Medicine and Imaging 168
- Epidemiology 161
- Health Informatics 137
Countries citing papers authored by Sunil Gupta
This map shows the geographic impact of Sunil 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 Sunil Gupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sunil Gupta more than expected).
Fields of papers citing papers by Sunil Gupta
This network shows the impact of papers produced by Sunil 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 Sunil Gupta. The network helps show where Sunil Gupta may publish in the future.
Co-authorship network of co-authors of Sunil Gupta
This figure shows the co-authorship network connecting the top 25 collaborators of Sunil Gupta. A scholar is included among the top collaborators of Sunil 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 Sunil Gupta. Sunil 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 | 3 | |
| 2 | 0 | |
| 3 | 7 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 9 | |
| 7 | Regret bounds for transfer learning in Bayesian optimisation | 5 |
| 8 | Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary Viewbreakdown → | 695 |
| 9 | 5 | |
| 10 | 20 | |
| 11 | 44 | |
| 12 | 8 | |
| 13 | 83 | |
| 14 | 72 | |
| 15 | 30 | |
| 16 | Factorial Multi-Task Learning : A Bayesian Nonparametric Approach | 15 |
| 17 | A nonparametric Bayesian Poisson Gamma model for count data | 8 |
| 18 | A slice sampler for restricted hierarchical beta process with applications to shared subspace learning | 4 |
| 19 | 10 | |
| 20 | A matrix factorization framework for jointly analyzing multiple nonnegative data source | 2 |
About Sunil Gupta
Sunil Gupta is a scholar working on Artificial Intelligence, Health Information Management and Statistics and Probability, having authored 43 papers that have together received 1.3k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (10 papers), Machine Learning in Healthcare (10 papers) and Bayesian Methods and Mixture Models (9 papers). The work is most often cited by research in Health Informatics (137 citations), Health Information Management (109 citations) and Computational Mathematics (9 citations). Sunil Gupta has collaborated with scholars based in Australia, United States and India. Frequent co-authors include Svetha Venkatesh, Dinh Phung, Santu Rana, Truyen Tran, Wei Luo, Alistair Shilton, Nevenka Dimitrova, Tu Bao Ho, Chandan Karmakar and John Yearwood. Their work appears in journals such as PLoS ONE, BMC Bioinformatics and Journal of Epidemiology & Community Health.
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.