Ranjan Maitra

68 papers receiving 1.2k citations

Peers

Ranjan Maitra
Comparison fields: 5 of 153
  • Computational Mathematics 18
  • Statistics and Probability 194
  • Artificial Intelligence 472
  • Signal Processing 117
  • Cognitive Neuroscience 162
Replace Jörg Polzehl with:
Jörg Polzehl Germany
Carlos Alberola‐López Spain
Giovanni Montana United Kingdom
Ana‐Maria Staicu United States
Laurent Oudre France
Laura M. Sangalli Italy
Fabrice Heitz France
Diana M. Sima Belgium
Mark P. Wachowiak Canada
James Wilson United States
Ranjan Maitra relative to Jörg Polzehl Germany Jörg Polzehl's profile →
Citations per field
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Jörg Polzehl · 1×
Citations per year

Countries citing papers authored by Ranjan Maitra

Since Specialization
Citations

This map shows the geographic impact of Ranjan Maitra'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 Ranjan Maitra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ranjan Maitra more than expected).

Fields of papers citing papers by Ranjan Maitra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ranjan Maitra. 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 Ranjan Maitra. The network helps show where Ranjan Maitra may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Ranjan Maitra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ranjan Maitra Line = papers co-authored together Ranjan Maitra links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20241
3 20242
4 20230
5 20231
6 20225
7 20221
8 20186
9 201819
10
Study of Epiphyseal Union around the Wrist Joint & Extended Hand in Males of Himalayan Area
20131
11 20139
12
MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms
20127
13
CARP: Software for Fishing Out Good Clustering Algorithms
20119
14 201019
15 200814
16 200877
17 20068
18 20057
19 1997127
20 199751

About Ranjan Maitra

Ranjan Maitra is a scholar working on Computational Mathematics, Statistics and Probability, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Signal Processing, having authored 73 papers that have together received 1.3k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (19 papers), Advanced Clustering Algorithms Research (12 papers), Statistical Methods and Inference (11 papers), Advanced MRI Techniques and Applications (10 papers), Medical Imaging Techniques and Applications (9 papers), Statistical Methods and Bayesian Inference (6 papers), Functional Brain Connectivity Studies (6 papers) and Advanced Neuroimaging Techniques and Applications (5 papers). The work is most often cited by research in Computational Mathematics (18 citations), Statistics and Probability (194 citations), Artificial Intelligence (472 citations), Signal Processing (117 citations) and Cognitive Neuroscience (162 citations). Ranjan Maitra has collaborated with scholars based in United States, India and Ireland. Frequent co-authors include Volodymyr Melnykov, Wei‐Chen Chen, Rao P. Gullapalli, Darren L. Johnson, Gregory P. Graziano, Steven A. Goldstein, Douglas C. Moore, Steven Roys, Ivan Ramler and Joel D. Greenspan. Their work appears in journals such as Journal of Computational and Graphical Statistics, Technometrics, Journal of the American Statistical Association, Magnetic Resonance in Medicine and Monthly Notices of the Royal Astronomical Society.

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.

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