World's Best Scientists 2026 revealed!
Bart De Moor

Bart De Moor

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Computer Science
Belgium
2026

D-Index & Metrics

Computer Science

D-Index
111
Citations
73237
World Ranking
212
National Ranking
2

Bart De Moor 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 Bart De Moor 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: 999 publications — 99th percentile

99% of scientists in this discipline score the same or lower.

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

Bart De Moor 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 Bart De Moor 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: 111 D-Index — 99th percentile

99% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in Belgium Leader Award
  • 2025 - Research.com Computer Science in Belgium Leader Award
  • 2022 - Research.com Computer Science in Belgium Leader Award
  • 2017 - SIAM Fellow For contributions to concepts and algorithms in numerical multilinear algebra and applications in engineering.
  • 2004 - IEEE Fellow For contributions to algebraic and numerical methods for systems and control.

Overview

Bart De Moor is affiliated with KU Leuven in Belgium and has an extensive publication record primarily in the field of Computer Science. Their research spans several subfields, including Artificial Intelligence, Molecular Biology, Electrical and Electronic Engineering, Computational Theory and Mathematics, and Computer Vision and Pattern Recognition.

The main topics covered in their work include:

  • Metabolomics and Mass Spectrometry Studies
  • Cell Image Analysis Techniques
  • Energy Load and Power Forecasting
  • Matrix Theory and Algorithms
  • Mass Spectrometry Techniques and Applications
  • Machine Learning in Healthcare
  • Data Quality and Management

De Moor has contributed to publications in various venues. Notable frequent publication venues are:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • IFAC-PapersOnLine
  • arXiv (Cornell University)
  • IEEE Control Systems Letters
  • Analytical Chemistry

Recent papers authored by Bart De Moor include:

  • "Prioritization of m/z-Values in Mass Spectrometry Imaging Profiles Obtained Using Uniform Manifold Approximation and Projection for Dimensionality Reduction" (2020, Analytical Chemistry)
  • "Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning" (2021, Analytical and Bioanalytical Chemistry)
  • "An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge" (2021, BMC Medical Informatics and Decision Making)
  • "A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data" (2021, Rapid Communications in Mass Spectrometry)
  • "Correspondence-Aware Manifold Learning for Microscopic and Spatial Omics Imaging: A Novel Data Fusion Method Bringing Mass Spectrometry Imaging to a Cellular Resolution" (2021, Analytical Chemistry)

Frequent collaborators include:

  • Etienne Waelkens
  • Oscar Mauricio Agudelo
  • Konstantinos Theodorakos
  • Johan A. K. Suykens
  • Wanqiu Zhang

De Moor has received distinctions such as:

  • SIAM Fellow in 2017 for contributions to concepts and algorithms in numerical multilinear algebra and applications in engineering
  • IEEE Fellow in 2004 for contributions to algebraic and numerical methods for systems and control

Best Publications

  • Least Squares Support Vector Machines

    Johan A K Suykens;Tony Van Gestel;Jos De Brabanter;Bart De Moor

  • Subspace Identification for Linear Systems: Theory - Implementation - Applications

    Peter van Overschee;Bart L. R. de Moor

  • A Multilinear Singular Value Decomposition

    Lieven De Lathauwer;Bart De Moor;Joos Vandewalle

  • N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems

    Peter Van Overschee;Peter Van Overschee;Bart De Moor

  • BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis

    Steffen Durinck;Yves Moreau;Arek Kasprzyk;Sean Davis

  • On the Best Rank-1 and Rank-( R 1 , R 2 ,. . ., R N ) Approximation of Higher-Order Tensors

    Lieven De Lathauwer;Bart De Moor;Joos Vandewalle

  • Assessing computational tools for the discovery of transcription factor binding sites.

    Martin Tompa;Nan Li;Timothy L. Bailey;George M. Church

  • Subspace identification for linear systems

    Peter Van Overschee;Bart De Moor

  • Gene prioritization through genomic data fusion.

    Stein Aerts;Diether Lambrechts;Sunit Maity;Peter Van Loo

  • Benchmarking Least Squares Support Vector Machine Classifiers

    Tony Van Gestel;Johan A. K. Suykens;Bart Baesens;Stijn Viaene

  • Four qubits can be entangled in nine different ways

    Frank Verstraete;Frank Verstraete;J Dehaene;B De Moor;Henri Verschelde

  • Subspace algorithms for the stochastic identification problem

    Peter van Overschee;Bart De Moor

  • Brief paper: Unbiased minimum-variance input and state estimation for linear discrete-time systems

    Steven Gillijns;Bart De Moor

  • Optimal control by least squares support vector machines

    J. A. K. Suykens;J. Vandewalle;B. De Moor

  • Cellular automata models of road traffic

    Sven Maerivoet;Bart De Moor

  • Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

    Lonneke A M Gravendeel;Mathilde C M Kouwenhoven;Olivier Gevaert;Johan J de Rooi

  • Financial time series prediction using least squares support vector machines within the evidence framework

    T. Van Gestel;J.A.K. Suykens;D.-E. Baestaens;A. Lambrechts

  • Mixed integer programming for multi-vehicle path planning

    Tom Schouwenaars;Bart De Moor;Eric Feron;Jonathan How

  • Artificial Neural Networks for Modelling and Control of Non-Linear Systems

    Johan A. K. Suykens;Joos P. L. Vandewalle;B. L. de Moor

  • Technical communique: Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough

    Steven Gillijns;Bart De Moor

  • Fetal electrocardiogram extraction by blind source subspace separation

    L. de Lathauwer;B. de Moor;J. Vandewalle

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