Dr habil. Marek Gagolewski

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:

My academic CV

Highlights

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.) Author of the fast&robust Genie hierarchical clustering algorithm Author of `stringi` – one of the most often downloaded R packages See also: Benchmark Suite for Clustering Algorithms - Version 1 Check me out at GitHub and StackOverflow
Data Science, Machine Learning, Python, R and C++ Tutor & Trainer Current: Deakin University, Melbourne Past: Warsaw University of Technology and Data Science Retreat, Berlin Author of best-selling textbooks on Programming and Data Analysis in R and Python (in Polish) Most-recent project (ongoing): Lightweight Machine Learning Classics with R

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…

2020-05-08

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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