Surajit Ray

2.9k citations
44 papers · 1.2k · h-index 17

Impact in

Papers in

Surajit Ray

42 papers receiving 1.2k citations

Peers

Surajit Ray
Comparison fields: 5 of 159
  • Statistics and Probability 229
  • Health Informatics 15
  • Radiology, Nuclear Medicine and Imaging 181
  • Artificial Intelligence 290
  • Biophysics 43
Replace Anna Maria Paganoni with:
Anna Maria Paganoni Italy
Halima Bensmail Qatar
Yongdai Kim South Korea
Marloes H. Maathuis Switzerland
Edward Suh United States
Giovanna Menardi Italy
Jacob Bien United States
Francesca Ieva Italy
Martin Crane Ireland
Narsis A. Kiani Sweden
Surajit Ray relative to Anna Maria Paganoni Italy Anna Maria Paganoni's profile →
Citations per field
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Citations per year

Countries citing papers authored by Surajit Ray

Since Specialization
Citations

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

Fields of papers citing papers by Surajit Ray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Surajit Ray, 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 Surajit Ray Line = papers co-authored together Surajit Ray links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008196
2 2014152
3 2007132
4 2020128
5 2005106
6 201152
7
STRATEGIC SEGMENTATION OF A MARKET
200048
8 199042
9
Quadratic distances on probabilities: A unified foundation
200735
10 200734
11 201634
12 202325
13 201218
14 201318
15 200716
16 201116
17 200916
18 200015
19 201213
20
DISTANCE-BASED MODEL-SELECTION WITH APPLICATION TO THE ANALYSIS OF GENE EXPRESSION DATA
200310

About Surajit Ray

Surajit Ray is a scholar working on Artificial Intelligence, Statistics and Probability, Molecular Biology, Economics and Econometrics and Infectious Diseases, having authored 44 papers that have together received 1.2k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (9 papers), Advanced Statistical Methods and Models (6 papers), Statistical Methods and Inference (6 papers), Statistical Methods and Bayesian Inference (6 papers), Gene expression and cancer classification (4 papers), Monetary Policy and Economic Impact (4 papers), COVID-19 Clinical Research Studies (3 papers) and AI in cancer detection (3 papers). The work is most often cited by research in Statistics and Probability (229 citations), Health Informatics (15 citations), Radiology, Nuclear Medicine and Imaging (181 citations), Artificial Intelligence (290 citations) and Biophysics (43 citations). Surajit Ray has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Bruce G. Lindsay, Jeffrey J. Harden, Kenneth A. Bollen, Jane R. Zavisca, Vladimir Brusić, Honghuang Lin, Ellis L. Reinherz, Songsak Tongchusak, J. Behari and N. E. Savin. Their work appears in journals such as PLoS ONE, Circulation, International Immunopharmacology, Journal of Machine Learning Research and Sociological Methods & 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.

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