Bibhas Chakraborty
- Statistics and Probability top 0.5%
- General Health Professions top 2%
- Applied Psychology top 1%
- Artificial Intelligence top 5%
- Economics and Econometrics top 5%
- Co-authors
- Susan A. MurphyErica E. M. MoodieVictor J. StrecherMarcus Eng Hock OngNan LiuFeng XieLinda M. CollinsVijay Nair
- Topics
- Statistical Methods in Clinical Trials (22 papers)Advanced Causal Inference Techniques (22 papers)Machine Learning in Healthcare (14 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsAmerican Journal of Public Health
- Partner nations
- United StatesSingaporeUnited Kingdom
In The Last Decade
Bibhas Chakraborty
105 papers receiving 2.6k citations
Peers
Comparison fields: 5 of 158
- Statistics and Probability 644
- General Health Professions 484
- Applied Psychology 433
- Artificial Intelligence 353
- Economics and Econometrics 278
Countries citing papers authored by Bibhas Chakraborty
This map shows the geographic impact of Bibhas Chakraborty'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 Bibhas Chakraborty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bibhas Chakraborty more than expected).
Fields of papers citing papers by Bibhas Chakraborty
This network shows the impact of papers produced by Bibhas Chakraborty. 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 Bibhas Chakraborty. The network helps show where Bibhas Chakraborty may publish in the future.
Co-authorship network of co-authors of Bibhas Chakraborty
This figure shows the co-authorship network connecting the top 25 collaborators of Bibhas Chakraborty. A scholar is included among the top collaborators of Bibhas Chakraborty 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 Bibhas Chakraborty. Bibhas Chakraborty is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 4 | |
| 6 | 5 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 6 | |
| 10 | 2 | |
| 11 | 22 | |
| 12 | 14 | |
| 13 | 16 | |
| 14 | 19 | |
| 15 | 3 | |
| 16 | 73 | |
| 17 | 82 | |
| 18 | 12 | |
| 19 | 19 | |
| 20 | 4 |
About Bibhas Chakraborty
Bibhas Chakraborty is a scholar working on Statistics and Probability, Applied Psychology and Health Informatics, having authored 114 papers that have together received 2.7k indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (22 papers), Advanced Causal Inference Techniques (22 papers) and Machine Learning in Healthcare (14 papers). The work is most often cited by research in Applied Psychology (433 citations), Statistics and Probability (644 citations) and Health Informatics (92 citations). Bibhas Chakraborty has collaborated with scholars based in United States, Singapore and United Kingdom. Frequent co-authors include Susan A. Murphy, Erica E. M. Moodie, Victor J. Strecher, Marcus Eng Hock Ong, Nan Liu, Feng Xie, Linda M. Collins, Vijay Nair, Roderick J. A. Little and Yilin Ning. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and American Journal of Public 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.