Surajit Ray

2.9k total citations
41 papers, 1.1k citations indexed

About

Surajit Ray is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Surajit Ray has authored 41 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Statistics and Probability, 10 papers in Artificial Intelligence and 8 papers in Molecular Biology. Recurrent topics in Surajit Ray's work include Bayesian Methods and Mixture Models (8 papers), Advanced Statistical Methods and Models (6 papers) and Statistical Methods and Inference (6 papers). Surajit Ray is often cited by papers focused on Bayesian Methods and Mixture Models (8 papers), Advanced Statistical Methods and Models (6 papers) and Statistical Methods and Inference (6 papers). Surajit Ray collaborates with scholars based in United States, United Kingdom and Netherlands. Surajit Ray's co-authors include Bruce G. Lindsay, Vladimir Brusić, Jane R. Zavisca, Jeffrey J. Harden, Kenneth A. Bollen, Ellis L. Reinherz, Honghuang Lin, Songsak Tongchusak, N. E. Savin and J. Behari and has published in prestigious journals such as Circulation, SHILAP Revista de lepidopterología and Journal of the American Statistical Association.

In The Last Decade

Surajit Ray

39 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Surajit Ray United States 16 270 259 208 199 119 41 1.1k
Wenjiang Fu United States 14 196 0.7× 199 0.8× 557 2.7× 34 0.2× 386 3.2× 29 2.2k
Alison Callahan United States 22 448 1.7× 361 1.4× 74 0.4× 113 0.6× 17 0.1× 51 1.4k
Zhenzhen Lu China 12 179 0.7× 60 0.2× 416 2.0× 65 0.3× 19 0.2× 29 1.1k
Edward Suh United States 18 299 1.1× 649 2.5× 52 0.3× 87 0.4× 86 0.7× 29 1.6k
Marloes H. Maathuis Switzerland 20 832 3.1× 412 1.6× 453 2.2× 57 0.3× 18 0.2× 48 2.0k
Vincent Ferretti Canada 21 231 0.9× 1.5k 5.8× 57 0.3× 142 0.7× 262 2.2× 42 2.8k
David S. Leslie United Kingdom 25 320 1.2× 159 0.6× 71 0.3× 75 0.4× 891 7.5× 74 2.3k
Halima Bensmail Qatar 26 468 1.7× 702 2.7× 101 0.5× 77 0.4× 29 0.2× 85 2.0k
Anna Maria Paganoni Italy 22 228 0.8× 99 0.4× 259 1.2× 118 0.6× 203 1.7× 108 1.4k
James Thompson United States 18 124 0.5× 88 0.3× 204 1.0× 63 0.3× 13 0.1× 55 1.3k

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-authorship network of co-authors of Surajit Ray

This figure shows the co-authorship network connecting the top 25 collaborators of Surajit Ray. A scholar is included among the top collaborators of Surajit Ray 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 Surajit Ray. Surajit Ray is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mamalakis, Michail, Abhirup Banerjee, Surajit Ray, et al.. (2024). Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning. Neural Computing and Applications. 36(30). 18841–18862.
2.
Cantoni, Diego, Craig Wilkie, Emma Bentley, et al.. (2023). Correlation between pseudotyped virus and authentic virus neutralisation assays, a systematic review and meta-analysis of the literature. Frontiers in Immunology. 14. 1184362–1184362. 19 indexed citations
3.
Banerjee, Abhirup, Michail Mamalakis, Surajit Ray, et al.. (2022). Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers. International Journal of Molecular Sciences. 23(13). 7260–7260. 4 indexed citations
4.
Ray, Surajit, Abhirup Banerjee, Andrew J. Swift, et al.. (2022). A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest X-rays. Scientific Reports. 12(1). 18220–18220. 4 indexed citations
5.
Small, A.D., et al.. (2020). Radionuclide ventriculography phase analysis for risk stratification of patients undergoing cardiotoxic cancer therapy. Journal of Nuclear Cardiology. 29(2). 581–589. 6 indexed citations
6.
Bayarri, M. J., James O. Berger, Woncheol Jang, et al.. (2019). Prior-based Bayesian information criterion. 3(1). 2–13. 2 indexed citations
7.
Das, Sonali, et al.. (2019). Functional regression models for South African economic indicators: a growth curve perspective. OPEC Energy Review. 43(2). 217–237.
8.
Young, Dylan M., Lauren Parry, Duncan Lee, & Surajit Ray. (2018). Spatial models with covariates improve estimates of peat depth in blanket peatlands. PLoS ONE. 13(9). e0202691–e0202691. 7 indexed citations
9.
Bollen, Kenneth A., Jeffrey J. Harden, Surajit Ray, & Jane R. Zavisca. (2014). BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models. Structural Equation Modeling A Multidisciplinary Journal. 21(1). 1–19. 141 indexed citations
10.
Ray, Surajit, et al.. (2014). Statistical Monitoring of Clinical Trials With Multiple Co-Primary Endpoints Using Multivariate B-value. Statistics in Biopharmaceutical Research. 6(3). 241–250. 7 indexed citations
11.
Lindsay, Bruce G., Marianthi Markatou, & Surajit Ray. (2013). Kernels, Degrees of Freedom, and Power Properties of Quadratic Distance Goodness-of-Fit Tests. Journal of the American Statistical Association. 109(505). 395–410. 14 indexed citations
12.
Ray, Surajit, et al.. (2012). On the upper bound of the number of modes of a multivariate normal mixture. Journal of Multivariate Analysis. 108. 41–52. 8 indexed citations
13.
Ray, Surajit & Saumyadipta Pyne. (2012). A Computational Framework to Emulate the Human Perspective in Flow Cytometric Data Analysis. PLoS ONE. 7(5). e35693–e35693. 12 indexed citations
14.
Shi, Ping, et al.. (2011). Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction. BMC Bioinformatics. 12(1). 375–375. 50 indexed citations
15.
Lin, Honghuang, Surajit Ray, Songsak Tongchusak, Ellis L. Reinherz, & Vladimir Brusić. (2008). Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research. BMC Immunology. 9(1). 8–8. 188 indexed citations
16.
Ray, Surajit, et al.. (2007). A Nonparametric Statistical Approach to Clustering via Mode Identification. Journal of Machine Learning Research. 8(59). 1687–1723. 123 indexed citations
17.
Lindsay, Bruce G., Marianthi Markatou, Surajit Ray, Ke Yang, & Shu‐Chuan Chen. (2007). Quadratic distances on probabilities: A unified foundation. 30 indexed citations
18.
Ray, Surajit & Thomas B. Kepler. (2007). Amino acid biophysical properties in the statistical prediction of peptide-MHC class I binding. PubMed. 3(1). 9–9. 6 indexed citations
19.
Ray, Surajit & Bruce G. Lindsay. (2005). The topography of multivariate normal mixtures. The Annals of Statistics. 33(5). 95 indexed citations
20.
Ray, Surajit & J. Behari. (1990). Physiological Changes in Rats after Exposure to Low Levels of Microwaves. Radiation Research. 123(2). 199–199. 38 indexed citations

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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