Kiranmoy Das
- Statistics and Probability top 5%
- Statistical Methods and Bayesian Inference 13
- Statistical Methods and Inference 13
- Advanced Statistical Methods and Models 4
- Genetics top 10%
- Genetic Mapping and Diversity in Plants and Animals 15
- Genetic and phenotypic traits in livestock 10
- Genetic Associations and Epidemiology 8
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- Genetics and Plant Breeding 5
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- Bayesian Methods and Mixture Models 11
- Co-authors
- Jiahan LiGuifang FuRunze LiRongling WuNaomi AltmanMartin KrzywinskiZhong WangMichael J. Daniels
- Journals
- Journal of the American Statistical Association (2 papers)Bioinformatics (1 paper)Water Research (1 paper)
- Partner nations
- United StatesIndiaChina
In The Last Decade
Kiranmoy Das
41 papers receiving 605 citations
Peers
Comparison fields: 5 of 120
- Statistics and Probability 133
- Genetics 284
- Plant Science 114
- Artificial Intelligence 80
- Renewable Energy, Sustainability and the Environment 28
Countries citing papers authored by Kiranmoy Das
This map shows the geographic impact of Kiranmoy Das'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 Kiranmoy Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kiranmoy Das more than expected).
Fields of papers citing papers by Kiranmoy Das
This network shows the impact of papers produced by Kiranmoy Das. 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 Kiranmoy Das. The network helps show where Kiranmoy Das may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kiranmoy Das, 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 | 2024 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 1 | |
| 4 | 2020 | 2 | |
| 5 | 2018 | 9 | |
| 6 | 2017 | 0 | |
| 7 | 2017 | 32 | |
| 8 | 2015 | 2 | |
| 9 | 2014 | 16 | |
| 10 | 2013 | 1 | |
| 11 | 2012 | 6 | |
| 12 | 2012 | 14 | |
| 13 | 2011 | 11 | |
| 14 | 2011 | 74 | |
| 15 | 2011 | 10 | |
| 16 | 2010 | 16 | |
| 17 | 2010 | 10 | |
| 18 | 2009 | 12 | |
| 19 | 2009 | 3 | |
| 20 | 2008 | 6 |
About Kiranmoy Das
Kiranmoy Das is a scholar working on Statistics and Probability, Genetics and Artificial Intelligence, having authored 42 papers that have together received 619 indexed citations. Recurring topics across this work include Genetic Mapping and Diversity in Plants and Animals (15 papers), Statistical Methods and Bayesian Inference (13 papers), Statistical Methods and Inference (13 papers), Bayesian Methods and Mixture Models (11 papers), Genetic and phenotypic traits in livestock (10 papers), Genetic Associations and Epidemiology (8 papers), Genetics and Plant Breeding (5 papers) and Advanced Statistical Methods and Models (4 papers). The work is most often cited by research in Statistics and Probability (133 citations), Genetics (284 citations) and Plant Science (114 citations). Kiranmoy Das has collaborated with scholars based in United States, India and China. Frequent co-authors include Jiahan Li, Guifang Fu, Runze Li, Rongling Wu, Naomi Altman, Martin Krzywinski, Rongling Wu, Zhong Wang, Michael J. Daniels and Parsaoran Hutapea. Their work appears in journals such as Journal of the American Statistical Association, Bioinformatics and Water Research.
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.