Marek Gagolewski

Dr habil. Marek Gagolewski

(pronounced like: Maa’rek (Mark) Gong-o-leaf-ski)

🏷 Senior Lecturer in Applied AI/Data Science
🏛 School of Information Technology, Deakin University
📪 221 Burwood Hwy, Melbourne-Burwood, VIC 3125, Australia

🏷 Associate Professor in Data Science (on leave)
🏛 Systems Research Institute, Polish Academy of Sciences

Emails (pick one – and only one):
○ marek▮gagolewski▯com (main)
○ marek▮deakin▯edu▯au (academic)

🆔 ORCID: 0000-0003-0637-6028

Open-access textbooks: ○ Minimalist Data Wrangling with Python ○ Deep R Programming
Open-source software: ○ genieclust ○ stringi ○ clustering-benchmarks ○ stringx ○ realtest
Data: ○ A Framework for Benchmarking Clustering Algorithms
See also: ○ GitHub ○ StackOverflow ○ G**gle Scholar ○ Vitæ ○ MADAM Seminar


  • 🔍 Researcher in data science (with particular emphasis on modelling complex phenomena and developing usable, general-purpose algorithms)

    • Area editor (aggregation functions and data science) in Fuzzy Sets and Systems

    • Research interests: computational and applied statistics; machine learning; data fusion, aggregation, and clustering; *metrics; mathematical modelling in informetrics, bibliometrics, psychometrics, sports analytics, economics, social sciences, and science of science; systems research

    • Author or co-author of 90+ publications, including journal papers in outlets such as Proceedings of the National Academy of Sciences (PNAS), Journal of Statistical Software, Information Fusion, International Journal of Forecasting, Statistical Modelling, Physica A: Statistical Mechanics and its Applications, Information Sciences, Knowledge-Based Systems, IEEE Transactions on Fuzzy Systems, and Journal of Informetrics

  • 💻 Free (libre) and open-source data analysis software developer

    • 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 (text/natural language processing)

      RStudio CRAN mirror downloads RStudio CRAN mirror downloads
  • 🎓 Data science, machine learning, and statistical computing lecturer