World's Best Scientists 2026 revealed!
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Computer Science
Netherlands
2025

D-Index & Metrics

Computer Science

D-Index
54
Citations
29835
World Ranking
4425
National Ranking
60

Research.com Recognitions

  • 2025 - Research.com Computer Science in Netherlands Leader Award
  • 2022 - Research.com Computer Science in Netherlands Leader Award
  • 2011 - Member of Academia Europaea

Overview

Paul M. B. Vitányi is affiliated with the Centrum Wiskunde & Informatica in the Netherlands. Their research spans multiple fields, primarily focusing on Biochemistry, Genetics and Molecular Biology as well as Computer Science.

The subfields of study covered in their research include:

  • Molecular Biology
  • Computational Theory and Mathematics
  • Infectious Diseases
  • Statistics and Probability
  • Artificial Intelligence

Key topics that feature prominently across their work are:

  • Computability, Logic, AI Algorithms
  • Fractal and DNA sequence analysis
  • Machine Learning in Bioinformatics
  • SARS-CoV-2 and COVID-19 Research
  • Genomics and Phylogenetic Studies
  • Benford's Law and Fraud Detection
  • Semigroups and automata theory

Vitányi's recent papers span a range of topics related to computational theory, phylogeny, and complexity, including:

  • How Incomputable Is Kolmogorov Complexity? (2020, Entropy)
  • Fast Phylogeny of SARS-CoV-2 by Compression (2022, Entropy)
  • Fast Whole-Genome Phylogeny of the COVID-19 Virus SARS-CoV-2 by Compression (2020, bioRxiv)
  • Fast Whole-Genome Phylogeny by Compression: the COVID-19 case (2021, Research Square)
  • The cluster structure function (2022, arXiv)

Frequent co-authors collaborating with Vitányi include:

  • Rudi Cilibrasi
  • Andrew R. Cohen

Publication venues where Vitányi's work appears regularly feature:

  • Entropy
  • Zenodo (CERN European Organization for Nuclear Research)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Research Square (Research Square)
  • arXiv (Cornell University)

Paul M. B. Vitányi was recognized as a Member of Academia Europaea in 2011.

Best Publications

  • An Introduction to Kolmogorov Complexity and Its Applications

    Ming Li;Paul Vitányi

  • Kolmogorov complexity and its applications

    Ming Li;Paul M. B. Vitányi

  • The Google Similarity Distance

    R.L. Cilibrasi;P.M.B. Vitanyi

  • The similarity metric

    Ming Li;Xin Chen;Xin Li;Bin Ma

  • Clustering by compression

    Ming Li;Xin Chen;Xin Li;Bin Ma

  • Information distance

    C.H. Bennett;P. Gacs;Ming Li;P.M.B. Vitanyi

  • Simplicity: a unifying principle in cognitive science?

    Nick Chater;Paul Vitányi

  • Algorithmic Clustering of Music Based on String Compression

    Rudi Cilibrasi;Paul Vitányi;Paul Vitányi;Ronald De Wolf

  • Algorithmic clustering of music

    R. Cilibrasi;P. Vitanyi;R. de Wolf

  • Minimum description length induction, Bayesianism, and Kolmogorov complexity

    P.M.B. Vitanyi;Ming Li

  • An Introduction to Kolmogorov Complexity

    M. Li;P.M.B. Vitányi

  • Algorithmic Statistics

    P. Gacs;J.T. Tromp;P.M.B. Vitanyi

  • Atomic shared register access by asynchronous hardware

    Paul M. B. Vitanyi;Baruch Awerbuch

  • Automatic Meaning Discovery Using Google

    Rudi Cilibrasi;Paul M.B. Vitanyi

  • Kolmogorov's structure functions and model selection

    N.K. Vereshchagin;P.M.B. Vitanyi

  • ‘Ideal learning’ of natural language: Positive results about learning from positive evidence

    Nick Chater;Paul Vitányi

  • Shannon Information and Kolmogorov Complexity

    Peter Grünwald;Paul M. B. Vitányi

  • Clustering by compression

    Unknown

  • The generalized universal law of generalization

    Nick Chater;Paul M.B. Vitányi

  • Distributed match-making

    Sape J. Mullender;Paul M. Vitányi

  • Learning simple concepts under simple distributions

    Ming Li;Paul M. B. Vitányi

  • Kolmogorov Complexity and Information Theory. With an Interpretation in Terms of Questions and Answers

    Peter D. Grünwald;Paul M. B. Vitányi

  • Algorithmic information theory

    Peter D. Grunwald;Paul M. B. Vitanyi

Frequent Co-Authors

Harry Buhrman
Harry Buhrman University of Amsterdam
Tao Jiang
Tao Jiang University of California, Riverside
Nick Chater
Nick Chater University of Warwick
Baruch Awerbuch
Baruch Awerbuch Johns Hopkins University
Evangelos Kranakis
Evangelos Kranakis Carleton University
Wojciech H. Zurek
Wojciech H. Zurek Los Alamos National Laboratory
Charles H. Bennett
Charles H. Bennett IBM (United States)
Alessandro Panconesi
Alessandro Panconesi Sapienza University of Rome
Juan A. Garay
Juan A. Garay Texas A&M University
Bin Ma
Bin Ma University of Waterloo

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