World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
46
Citations
16751
World Ranking
1314
National Ranking
588

Michael L. Stein 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 Michael L. Stein 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: 161 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

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

Michael L. Stein 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 Michael L. Stein 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: 46 D-Index — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 1999 - Fellow of the American Statistical Association (ASA)

Overview

Michael L. Stein is affiliated with Rutgers, The State University of New Jersey in the United States. Their research primarily focuses on environmental science, with notable contributions to global and planetary change, environmental engineering, and atmospheric science. They have also worked in finance and artificial intelligence as applied to environmental contexts.

The scientist has explored major topics including climate variability and models, hydrology and drought analysis, soil geostatistics and mapping, Gaussian processes and Bayesian inference, financial risk and volatility modeling, meteorological phenomena and simulations, and atmospheric and environmental gas dynamics.

Recent papers by Michael L. Stein and collaborators provide insight into their research scope. Titles include:

  • Neural networks for parameter estimation in intractable models (2023, Computational Statistics & Data Analysis)
  • A parametric model for distributions with flexible behavior in both tails (2020, Environmetrics)
  • The Evolving Distribution of Relative Humidity Conditional Upon Daily Maximum Temperature in a Warming Climate (2020, Journal of Geophysical Research Atmospheres)
  • Parametric models for distributions when interest is in extremes with an application to daily temperature (2020, Extremes)
  • Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails (2022, Weather and Climate Extremes)

Frequent co-authors of Michael L. Stein include:

  • Christopher J. Geoga
  • Julie Bessac
  • Mitchell Krock
  • Adam H. Monahan
  • Mihai Anitescu

Publishing venues where Michael L. Stein regularly contributes include:

  • arXiv (Cornell University)
  • Environmetrics
  • Extremes
  • Journal of the American Statistical Association
  • Statistics and Computing

Michael L. Stein was recognized as a Fellow of the American Statistical Association in 1999. This distinction is a formal acknowledgment within the statistical community.

Best Publications

  • Interpolation of Spatial Data: Some Theory for Kriging

    Michael Leonard Stein

  • Interpolation of Spatial Data

    Michael L. Stein

  • Large sample properties of simulations using latin hypercube sampling

    Michael Stein

  • A Bayesian analysis of kriging

    Mark S. Handcock;Michael L. Stein

  • Space–Time Covariance Functions

    Michael L Stein

  • Approximating likelihoods for large spatial data sets

    Michael L. Stein;Zhiyi Chi;Leah J. Welty

  • Spatial sampling design for prediction with estimated parameters

    Zhengyuan Zhu;Michael L. Stein

  • Limitations on low rank approximations for covariance matrices of spatial data

    Michael L. Stein

  • Asymptotically Efficient Prediction of a Random Field with a Misspecified Covariance Function

    Michael L. Stein

  • A general method for validating statistical downscaling methods under future climate change

    M. Vrac;M. L. Stein;K. Hayhoe;K. Hayhoe;X.-Z. Liang

  • Nonstationary covariance models for global data

    Mikyoung Jun;Michael L. Stein

  • Estimating and choosing

    Michael Stein

  • Statistical emulation of climate model projections based on precomputed GCM runs

    Stefano Castruccio;David J. McInerney;Michael L. Stein;Feifei Liu Crouch

  • Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing

    M. Vrac;M. Stein;K. Hayhoe

  • An Approach to Producing Space–Time Covariance Functions on Spheres

    Mikyoung Jun;Michael L Stein

  • Fast and Exact Simulation of Fractional Brownian Surfaces

    Michael L Stein

  • A modeling approach for large spatial datasets

    Michael L. Stein

  • Spatial variation of total column ozone on a global scale

    Michael L. Stein

  • Spatial sampling design for parameter estimation of the covariance function

    Zhengyuan Zhu;Michael L. Stein

  • Uniform Asymptotic Optimality of Linear Predictions of a Random Field Using an Incorrect Second-Order Structure

    Michael Stein

  • The screening effect in Kriging

    Michael L. Stein

  • The Analysis of Directional Time Series: Applications to Wind Speed and Direction

    Michael Stein

Frequent Co-Authors

Mihai Anitescu
Mihai Anitescu Argonne National Laboratory
Katharine Hayhoe
Katharine Hayhoe Texas Tech University
Dmitry Beletsky
Dmitry Beletsky University of Michigan–Ann Arbor
David J. Schwab
David J. Schwab Michigan Technological University
Donald J. Wuebbles
Donald J. Wuebbles University of Illinois at Urbana-Champaign
Ying Sun
Ying Sun King Abdullah University of Science and Technology
Mark S. Handcock
Mark S. Handcock University of California, Los Angeles
Donald G. York
Donald G. York University of Chicago
Edward B. Jenkins
Edward B. Jenkins Princeton University
Brian Yanny
Brian Yanny Fermilab

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Related Online Degrees & Career Pathways

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Exploring these related online degrees can enhance employability and open diverse career pathways for Mathematics graduates in the USA.

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