World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
45
Citations
7935
World Ranking
1473
National Ranking
646

Engineering and Technology

D-Index
45
Citations
7499
World Ranking
5531
National Ranking
1547

Hrushikesh N. Mhaskar 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 Hrushikesh N. Mhaskar 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: 196 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Hrushikesh N. Mhaskar 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 Hrushikesh N. Mhaskar 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: 45 D-Index — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Overview

Hrushikesh N. Mhaskar is affiliated with Claremont Graduate University in the United States. Their research focuses primarily on computer science, with significant contributions to artificial intelligence, computer vision and pattern recognition, signal processing, statistics and probability, and endocrinology, diabetes and metabolism.

The scientist has published extensively in areas including neural networks and applications, image and signal denoising methods, mathematical approximation and integration, face and expression recognition, numerical methods in inverse problems, statistical methods and inference, and sparse and compressive sensing techniques.

Recent papers authored by Hrushikesh N. Mhaskar include:

  • Function approximation by deep networks (2020) in Communications on Pure & Applied Analysis
  • A Witness Function Based Construction of Discriminative Models Using Hermite Polynomials (2020) in Frontiers in Applied Mathematics and Statistics
  • Local approximation of operators (2023) in Applied and Computational Harmonic Analysis
  • Theory inspired deep network for instantaneous-frequency extraction and signal components recovery from discrete blind-source data (2020) in arXiv (Cornell University)
  • Theory-Inspired Deep Network for Instantaneous-Frequency Extraction and Subsignals Recovery From Discrete Blind-Source Data (2021) in IEEE Transactions on Neural Networks and Learning Systems

Frequent collaborators of Mhaskar include Alexander Cloninger, Tong Mao, Ryan O'Dowd, Sergei V. Pereverzyev, and S. Kitimoon.

Publications are often found in journals and venues such as arXiv (Cornell University), Applied and Computational Harmonic Analysis, Frontiers in Applied Mathematics and Statistics, Neural Networks, and SSRN Electronic Journal.

Best Publications

  • Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review

    Tomaso A. Poggio;Hrushikesh Mhaskar;Hrushikesh Mhaskar;Lorenzo Rosasco;Brando Miranda

  • Neural networks for optimal approximation of smooth and analytic functions

    H. N. Mhaskar

  • Deep vs. shallow networks: An approximation theory perspective

    Hrushikesh N. Mhaskar;Hrushikesh N. Mhaskar;Tomaso Poggio

  • Approximation by superposition of sigmoidal and radial basis functions

    H.N Mhaskar;Charles A Micchelli

  • Where Does the Sup Norm of a Weighted Polynomial Live? (A Generalization of Incomplete Polynomials)

    H. N. Mhaskar;E. B. Saff

  • Spherical Marcinkiewicz-Zygmund inequalities and positive quadrature

    H. N. Mhaskar;F. J. Narcowich;J. D. Ward

  • Extremal problems for polynomials with exponential weights

    H. N. Mhaskar;E. B. Saff

  • Introduction to the Theory of Weighted Polynomial Approximation

    H N Mhaskar

  • When and Why Are Deep Networks Better Than Shallow Ones

    Hrushikesh Mhaskar;Qianli Liao;Tomaso A. Poggio

  • Approximation properties of a multilayered feedforward artificial neural network

    Hrushikesh Narhar Mhaskar

  • Degree of Approximation by Neural and Translation Networks with a Single Hidden Layer

    H. N. Mhaskar;C. A. Micchelli

  • Learning Functions: When Is Deep Better Than Shallow

    Hrushikesh Mhaskar;Qianli Liao;Tomaso Poggio

  • Fundamentals of Approximation Theory

    Hrushikesh N. Mhaskar;Devidas V. Pai

  • Marcinkiewicz-Zygmund measures on manifolds

    F. Filbir;H. N. Mhaskar

  • A Deep Learning Approach to Diabetic Blood Glucose Prediction

    Hrushikesh N. Mhaskar;Sergei V. Pereverzyev;Maria D. van der Walt

  • A proof of Freud's conjecture for exponential weights

    D. S. Lubinsky;H. N. Mhaskar;E. B. Saff

  • Signal decomposition and analysis via extraction of frequencies

    Charles K. Chui;H.N. Mhaskar;H.N. Mhaskar

  • On trigonometric wavelets

    C. K. Chui;H. N. Mhaskar

  • A deep learning approach to diabetic blood glucose prediction

    H. N. Mhaskar;S. V. Pereverzyev;M. D. van der Walt

  • Limitations of the approximation capabilities of neural networks with one hidden layer

    Charles K. Chui;Xin Li;Hrushikesh Narhar Mhaskar

  • Theory of Deep Learning III: explaining the non-overfitting puzzle

    Tomaso A. Poggio;Kenji Kawaguchi;Qianli Liao;Brando Miranda

Frequent Co-Authors

Charles K. Chui
Charles K. Chui Hong Kong Baptist University
Joseph D. Ward
Joseph D. Ward Texas A&M University
Francis J. Narcowich
Francis J. Narcowich Texas A&M University
Edward B. Saff
Edward B. Saff Vanderbilt University
Charles A. Micchelli
Charles A. Micchelli University at Albany, State University of New York
Ding-Xuan Zhou
Ding-Xuan Zhou University of Sydney
Mauro Maggioni
Mauro Maggioni Johns Hopkins University
Ian H. Sloan
Ian H. Sloan University of New South Wales

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