Dr habil. Marek Gagolewski

(Pronounced like Mark Gaggle-Eve-Ski)

Senior Lecturer in Applied Artificial Intelligence
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: CV

ORCID=0000-0003-0637-6028

Highlights

Researcher in Data Science Research interests: Machine learning, data clustering, data fusion, learning to aggregate data, computational statistics, usable statistical software, 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, R Journal, 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).
Open Source Machine Learning, Data Analysis and Scientific Software Developer (Python, C, C++, R, etc.) Author and maintainer of the fast&robust Genie hierarchical clustering algorithm, see Python and R package `genieclust` Author and maintainer of `stringi` – one of the most often downloaded R packages that aims at text/natural language processing See also: Benchmark Suite for Clustering Algorithms - Version 1 Check me out at GitHub and StackOverflow
Data Science, Machine Learning, and Statistical Computing 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

2020-09-09 software

R package stringi 1.5.3 released

A new, major release of my R package stringi brings quite a few new features and bug fixes. Read more…

2020-09-07 software

Tutorial on stringi

A comprehensive tutorial on the stringi package is now available.

2020-08-17 software

stringi Has a New Website

I have created a new home(page) for my stringi package, see stringi.gagolewski.com/.

2020-07-31 software

Python and R package genieclust 0.9.4

A reimplementation of my robust hierarchical clustering algorithm Genie is now available on PyPI and CRAN. Now even faster and equipped with many more features, including noise point detection. See genieclust.gagolewski.com/ for more details, documentation, benchmarks, and tutorials.

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…

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