World's Best Scientists 2026 revealed!
Bart De Moor

Bart De Moor

Award Badge
Computer Science
Belgium
2026

D-Index & Metrics

Computer Science

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

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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