About

Marek Gagolewski

Dr habil. Marek Gagolewski

(Pronounced like Mark Gaggle-Eve-Ski) 🙃

🏷 Senior Lecturer/Researcher
🏛 Deakin University, School of Information Technology
📪 Melbourne-Burwood Campus, Room T2.20, 221 Burwood Hwy, Burwood, VIC 3125, Australia
🆔 ORCID=0000-0003-0637-6028

Emails (pick one – and only one):
• marek🔶gagolewski🔹com (main)
• m🔹gagolewski🔶deakin🔹edu🔹au (academic)

See also:GitHubStackOverflowVitæ
Software:geniecluststringistringxrealtest
Data:Benchmark Suite for Clustering Algorithms — Version 1

Highlights

  • 🔍 Researcher in the Science of Data (with particular emphasis on modelling of complex phenomena and developing of usable, general purpose algorithms)

    • Area Editor (Data Science) in Fuzzy Sets and Systems

    • Research interests: machine learning; data fusion, aggregation, and clustering; computational statistics; mathematical modelling in informetrics, sports analytics, economics, social sciences, and science of science – amongst others

    • Author or co-author of 80+ publications, including journal papers in outlets such as Proceedings of the National Academy of Sciences (PNAS), Information Fusion, International Journal of Forecasting, Statistical Modelling, Journal of Statistical Software, Information Sciences, Knowledge-Based Systems, IEEE Transactions on Fuzzy Systems, and Journal of Informetrics

    • Eligible Principal Supervisor at PhD level (principal supervisor of 3 PhD and 11 MSc by research students from commencement through to successful completion) – feel welcome to contact me if you have any interesting research ideas (and essential capabilities such as programming skills (Python, R, C/C++, etc.), data wrangling, matrix algebra, probability and statistics, and optimisation)

  • 💻 Free (Libre) and Open Source Data Analysis Software Developer

    • 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
    • Author and maintainer of the fast&robust Genie hierarchical clustering algorithm, see Python and R package genieclust

  • 🎓 Data Science, Machine Learning, and Statistical Computing Teacher