Marek Gagolewski

Dr habil. Marek Gagolewski

ORCID ORCID=0000-0003-0637-6028

Senior Lecturer in Applied Artificial Intelligence (US equiv.: Associate Professor)
School of Information Technology
Deakin University
Melbourne Burwood Campus, Room T2.20
221 Burwood Hwy, Burwood, VIC 3125, Australia
Associate Professor in Data Science (on long-term leave)
Faculty of Mathematics and Information Science
Warsaw University of Technology
ul. Koszykowa 75, 00-662 Warsaw, Poland
Emails (pick one – and only one):
marekgagolewski·com (main)
M.Gagolewskideakin·edu·au (academic – Deakin University)
M.Gagolewskimini·pw·edu·pl (academic – Warsaw University of Technology)
See also:  ResearchGate Google Scholar
My academic CV


Researcher in Data Science

  • Research interests: Machine learning, data clustering, data fusion, learning to aggregate data, computational statistics, plagiarism detection and software quality, statistical modelling for informetrics, sports analytics, science of science.
  • Author or co-author of 74 publications (see featured papers), including:
    • 32 journal papers (in outlets such as Proceedings of the National Academy of Sciences (PNAS), Information Fusion, Information Sciences, IEEE Transactions on Fuzzy Systems, Journal of Informetrics and Statistical Modelling),
    • 34 papers in proceedings of international conferences,
    • 8 research monographs, textbooks and edited volumes.
  • Current h-index = 14 (Google Scholar) / 10 (Scopus) / 10 (Web of Science).

Machine Learning, Data Analysis and Scientific Computing Software Developer (Python, C, C++, R, etc.)

Data Science, Machine Learning, Python, R and C++ Tutor & Trainer

Recent News

2020-07-08 new paper

Paper on SimilaR in R Journal

SimilaR: R Code Clone and Plagiarism Detection by Maciej Bartoszuk and me has been accepted for publication in the R Journal. Read more…

2020-06-08 new paper

Paper in PNAS: Three Dimensions of Scientific Impact

In a paper recently published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) (doi:10.1073/pnas.2001064117; joint work with Grzesiek Siudem, Basia Żogała-Siudem and Ania Cena), we consider the mechanisms behind one’s research success as measured by one’s papers’ citability. By acknowledging the perceived esteem might be a consequence not only of how valuable one’s works are but also of pure luck, we arrived at a model that can accurately recreate a citation record based on just three parameters: the number of publications, the total number of citations, and the degree of randomness in the citation patterns. As a by-product, we show that a single index will never be able to embrace the complex reality of the scientific impact. However, three of them can already provide us with a reliable summary. Read more…


Benchmark Suite for Clustering Algorithms - Version 1

Let's aggregate, polish and standardise the existing clustering benchmark suites referred to across the machine learning and data mining literature! See our new Benchmark Suite for Clustering Algorithms.

2020-02-23 book draft

Lightweight Machine Learning Classics with R

A first draft of my new textbook Lightweight Machine Learning Classics with R is now available. Read more…

2020-02-10 new paper

Genie+OWA: Robustifying Hierarchical Clustering with OWA-based Linkages

Check out our (by Anna Cena and me) most recent paper on the best hierarchical clustering algorithm in the world – Genie. It is going to appear in Information Sciences; doi:10.1016/j.ins.2020.02.025. Read more…

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