World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
9334
World Ranking
6814
National Ranking
2996

Amaury Lendasse publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Amaury Lendasse sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 318 publications — 77th percentile

77% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Amaury Lendasse D-index placement in Computer Science in 2026

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

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Amaury Lendasse is affiliated with the University of Houston in the United States. Their research spans multiple areas within computer science and engineering, with a focus on artificial intelligence and related subfields.

The main fields of study in which they have contributed include:

  • Computer Science
  • Engineering

Within these fields, their subfields of specialization include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Electrical and Electronic Engineering
  • Neurology

The topics central to their research work are:

  • Machine Learning and ELM (Extreme Learning Machines)
  • Domain Adaptation and Few-Shot Learning
  • Neural Networks and Applications
  • Face and Expression Recognition
  • Brain Tumor Detection and Classification
  • Machine Learning and Data Classification
  • Advanced Memory and Neural Computing

Among their recent publications, the following papers stand out:

  • "A modified Lanczos Algorithm for fast regularization of extreme learning machines" (2020), published in Neurocomputing
  • "A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans" (2024), published in Aging
  • "Embedded spectral descriptors: learning the point-wise correspondence metric via Siamese neural networks" (2020), published in Journal of Computational Design and Engineering
  • "Quantifying Time-Frequency Co-movement Impact of COVID-19 on U.S. and China Stock Market Toward Investor Sentiment Index" (2021), published in Frontiers in Public Health
  • "Ensemble Learning with Highly Variable Class-Based Performance" (2024), published in Machine Learning and Knowledge Extraction

Lendasse's frequent coauthors include:

  • Kaj-Mikael Björk
  • Edward Ratner
  • Anton Akusok
  • Leonardo Espinosa-Leal
  • Brandon Warner

The venues where Lendasse has commonly published are:

  • ESANN 2021 proceedings
  • Aging
  • Neurocomputing
  • Frontiers in Public Health
  • Machine Learning and Knowledge Extraction

Best Publications

  • OP-ELM: Optimally Pruned Extreme Learning Machine

    Yoan Miche;A. Sorjamaa;P. Bas;O. Simula

  • Extreme Learning Machine

    Erik Cambria;Guang-Bin Huang;Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou

  • Methodology for long-term prediction of time series

    Antti Sorjamaa;Jin Hao;Nima Reyhani;Yongnan Ji

  • High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

    Anton Akusok;Kaj-Mikael Bjork;Yoan Miche;Amaury Lendasse

  • Mutual information for the selection of relevant variables in spectrometric nonlinear modelling

    Fabrice Rossi;Amaury Lendasse;Damien François;Vincent Wertz

  • TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization

    Yoan Miche;Mark van Heeswijk;Patrick Bas;Olli Simula

  • Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis

    John Aldo Lee;Amaury Lendasse;Michel Verleysen

  • GPU-accelerated and parallelized ELM ensembles for large-scale regression

    Mark van Heeswijk;Yoan Miche;Erkki Oja;Amaury Lendasse

  • Non-linear financial time series forecasting application to the Bel 20 stock market index

    Amaury Lendasse;Eric de Bodt;Vincent Wertz;Michel Verleysen

  • Bankruptcy prediction using Extreme Learning Machine and financial expertise

    Qi Yu;Yoan Miche;Eric Séverin;Amaury Lendasse;Amaury Lendasse;Amaury Lendasse

  • A robust nonlinear projection method

    John Aldo Lee;Amaury Lendasse;Nicolas Donckers;Michel Verleysen

  • Regularized extreme learning machine for regression with missing data

    Qi Yu;Yoan Miche;Emil Eirola;Mark Van Heeswijk

  • A robust non-linear projection method.

    John Aldo Lee;Amaury Lendasse;Nicolas Donckers;Michel Verleysen

  • Curvilinear Distance Analysis versus Isomap

    John Aldo Lee;Amaury Lendasse;Michel Verleysen

  • Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

    Mark Heeswijk;Yoan Miche;Tiina Lindh-Knuutila;Peter A. Hilbers

  • Extreme learning machine for missing data using multiple imputations

    Dušan Sovilj;Emil Eirola;Yoan Miche;Kaj-Mikael Björk

  • OP-ELM: Theory, Experiments and a Toolbox

    Yoan Miche;Antti Sorjamaa;Amaury Lendasse

  • Width optimization of the Gaussian kernels in Radial Basis Function Networks

    Nabil Benoudjit;Cédric Archambeau;Amaury Lendasse;John Aldo Lee

  • Long-term time series prediction using OP-ELM

    Alexander Grigorievskiy;Yoan Miche;Anne-Mari Ventelä;Eric Séverin

  • Direct and recursive prediction of time series using mutual information selection

    Yongnan Ji;Jin Hao;Nima Reyhani;Amaury Lendasse

  • Model selection with cross-validations and bootstraps: application to time series prediction with RBFN models

    Amaury Lendasse;Vincent Wertz;Michel Verleysen

Frequent Co-Authors

Michel Verleysen
Michel Verleysen Université Catholique de Louvain
John Aldo Lee
John Aldo Lee Université Catholique de Louvain
Christian Jutten
Christian Jutten Grenoble Alpes University
Guang-Bin Huang
Guang-Bin Huang Nanyang Technological University
Erkki Oja
Erkki Oja Aalto University
Ignacio Rojas
Ignacio Rojas University of Granada
Kezhi Mao
Kezhi Mao Nanyang Technological University
Juha Karhunen
Juha Karhunen Aalto University
Héctor Pomares
Héctor Pomares University of Granada
Yew-Soon Ong
Yew-Soon Ong Nanyang Technological University

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