World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
35
Citations
7133
World Ranking
2727
National Ranking
1117

Engineering and Technology

D-Index
31
Citations
5989
World Ranking
9649
National Ranking
2748

Subhashis Ghosal publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Subhashis Ghosal sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 159 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Subhashis Ghosal D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Subhashis Ghosal sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 35 D-Index — 25th percentile

25% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Subhashis Ghosal is affiliated with North Carolina State University in the United States. Their research primarily spans the field of Mathematics, with a concentrated focus on Statistics and Probability.

The scientist's work includes significant contributions to the following subfields:

  • Statistics and Probability
  • Artificial Intelligence
  • Genetics
  • Molecular Biology
  • Statistics, Probability and Uncertainty

Ghosal's research topics encompass a range of statistical and probabilistic methodologies, notably:

  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Advanced Statistical Methods and Models
  • Bayesian Methods and Mixture Models
  • Genetic and phenotypic traits in livestock
  • Spectroscopy and Chemometric Analyses
  • Probabilistic and Robust Engineering Design

The scientist has published extensively in various academic venues. The most frequent publication venues include:

  • arXiv (Cornell University)
  • Electronic Journal of Statistics
  • Bernoulli
  • The Annals of Statistics
  • Journal of Statistical Planning and Inference

Among recent papers, notable works are:

  • "Bayesian linear regression for multivariate responses under group sparsity," 2020, Bernoulli
  • "Empirical Bayes oracle uncertainty quantification for regression," 2020, The Annals of Statistics
  • "Contraction properties of shrinkage priors in logistic regression," 2020, Journal of Statistical Planning and Inference
  • "Posterior contraction in sparse generalized linear models," 2020, Biometrika
  • "Unified Bayesian theory of sparse linear regression with nuisance parameters," 2021, Electronic Journal of Statistics

Frequent collaborators in their research include:

  • Seonghyun Jeong
  • Moumita Chakraborty
  • Kang Wang
  • Eduard Belitser

Best Publications

  • Convergence rates of posterior distributions

    Subhashis Ghosal;Jayanta K. Ghosh;Aad W. van der Vaart

  • Fundamentals of Nonparametric Bayesian Inference

    Subhashis Ghosal;Aad van der Vaart

  • POSTERIOR CONSISTENCY OF DIRICHLET MIXTURES IN DENSITY ESTIMATION

    S. Ghosal;J. K. Ghosh;R. V. Ramamoorthi

  • Convergence rates of posterior distributions for non-i.i.d. observations

    Subhashis Ghosal;Aad van der Vaart

  • Rates of convergence for Bayes and maximum likelihood estimation for mixture of normal densities

    Subhashis Ghosal;Aad W. van der Vaart

  • Posterior convergence rates of Dirichlet mixtures at smooth densities

    Subhashis Ghosal;Aad van der Vaart

  • Posterior consistency of Gaussian process prior for nonparametric binary regression

    Subhashis Ghosal;Anindya Roy

  • Convergence rates of posterior distributions for noniid observations

    Subhashis Ghosal;Aad van der Vaart

  • Convergence rates for density estimation with Bernstein polynomials

    Subhashis Ghosal

  • Adaptive Bayesian multivariate density estimation with Dirichlet mixtures

    Weining Shen;Surya T. Tokdar;Subhashis Ghosal

  • Bayesian Estimation of the Spectral Density of a Time Series

    Nidhan Choudhuri;Subhashis Ghosal;Anindya Roy

  • Extensions of the strong law of large numbers of Marcinkiewicz and Zygmund for dependent variables

    T. K. Chandra;S. Ghosal

  • Kullback Leibler property of kernel mixture priors in Bayesian density estimation

    Yuefeng Wu;Subhashis Ghosal

  • Adaptive Bayesian inference on the mean of an infinite-dimensional normal distribution

    Eduard Belitser;Subhashis Ghosal

  • Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression

    William Weimin Yoo;Subhashis Ghosal

  • Bayesian structure learning in graphical models

    Sayantan Banerjee;Subhashis Ghosal

  • On convergence of posterior distributions

    Subhashis Ghosal;Jayanta K. Ghosh;Tapas Samanta

  • Nonparametric Bayesian model selection and averaging

    Subhashis Ghosal;Jüri Lember;Aad van der Vaart

  • Testing monotonicity of a regression function.

    S. Ghosal;A.W. van der Vaart

  • Asymptotic normality of posterior distributions in high-dimensional linear models

    Subhashis Ghosal

  • Bayesian Nonparametrics: The Dirichlet process, related priors and posterior asymptotics

    Unknown

  • Nonparametric Bayesian model selection and averaging

    S. Ghosal;J. Lember;A.W. van der Vaart

Frequent Co-Authors

Aad van der Vaart
Aad van der Vaart Delft University of Technology
Jayanta K. Ghosh
Jayanta K. Ghosh Purdue University West Lafayette
Hao Helen Zhang
Hao Helen Zhang University of Arizona
David E. Kleiner
David E. Kleiner National Institutes of Health
William F. Rosenberger
William F. Rosenberger George Mason University

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