World's Best Scientists 2026 revealed!
Geoffrey J. McLachlan

Geoffrey J. McLachlan

Award Badge
Mathematics
Australia
2026

D-Index & Metrics

Mathematics

D-Index
65
Citations
69945
World Ranking
371
National Ranking
10

Engineering and Technology

D-Index
59
Citations
65360
World Ranking
2268
National Ranking
118

Research.com Recognitions

  • 2026 - Research.com Mathematics in Australia Leader Award
  • 2025 - Research.com Mathematics in Australia Leader Award
  • 2023 - Research.com Mathematics in Australia Leader Award
  • 1998 - Fellow of the American Statistical Association (ASA)

Overview

Geoffrey J. McLachlan is affiliated with the University of Queensland in Australia and specializes in research spanning computer science and mathematics. The primary areas of their work include artificial intelligence, statistics and probability, mechanical engineering, computer vision and pattern recognition, and economics and econometrics.

The scientist's main research topics focus largely on Bayesian methods and mixture models as well as statistical methods and Bayesian inference. Other notable areas of investigation include statistical distribution estimation and applications, machine learning and data classification, mineral processing and grinding, and advanced statistical methods and models.

Geoffrey J. McLachlan has published extensively in various academic venues. The frequent publication outlets include:

  • arXiv (Cornell University)
  • Minerals Engineering
  • Statistics and Computing
  • Communication in Statistics- Theory and Methods
  • Computational Statistics & Data Analysis

Recent papers authored or coauthored by the scientist illustrate the range and focus of their research:

  • Approximation by finite mixtures of continuous density functions that vanish at infinity (2020, Cogent Mathematics & Statistics)
  • A new algorithm for support vector regression with automatic selection of hyperparameters (2022, Pattern Recognition)
  • Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy (2021, Minerals Engineering)
  • Mini-batch learning of exponential family finite mixture models (2020, Statistics and Computing)
  • An overview of skew distributions in model-based clustering (2021, Journal of Multivariate Analysis)

Collaboration is a significant aspect of Geoffrey J. McLachlan's research activity, working frequently with several coauthors including:

  • Daniel Ahfock
  • Hien D. Nguyen
  • Faïcel Chamroukhi
  • Sharon Lee
  • TrungTin Nguyen

Geoffrey J. McLachlan has been recognized as a Fellow of the American Statistical Association (ASA) since 1998, which marks a noted point in their professional career.

Best Publications

  • Finite Mixture Models

    Geoffrey McLachlan;David Peel

  • The EM algorithm and extensions

    Geoffrey J. McLachlan;Thriyambakam Krishnan

  • Top 10 algorithms in data mining

    Xindong Wu;Vipin Kumar;J. Ross Quinlan;Joydeep Ghosh

  • Finite mixture models: McLachlan/finite mixture models

    Geoffrey McLachlan;David Peel

  • Discriminant Analysis and Statistical Pattern Recognition

    Geoffrey John McLachlan

  • Mixture models : inference and applications to clustering

    Geoffrey J. McLachlan;Kaye E. Basford

  • Selection bias in gene extraction on the basis of microarray gene-expression data.

    Christophe Ambroise;Geoffrey J. McLachlan

  • Modelling Survival Data in Medical Research.

    G. J. McLachlan;D. Collett

  • The EM Algorithm and Extensions: Second Edition

    Geoffrey J. McLachlan;Thriyambakam Krishnan

  • Robust mixture modelling using the t distribution

    D. Peel;G. J. McLachlan

  • On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture

    G. J. McLachlan

  • Analyzing Microarray Gene Expression Data

    Geoffrey J. McLachlan;Kim-Anh Do;Christophe Ambroise

  • A mixture model-based approach to the clustering of microarray expression data

    Geoffrey J. McLachlan;Richard Bean;David Peel

  • Discriminant Analysis and Statistical Pattern Recognition: McLachlan/Discriminant Analysis & Pattern Recog

    Unknown

  • On Bayesian analysis of mixtures with an unknown number of components. Discussion. Author's reply

    S. Richardson;P. J. Green;C. P. Robert;M. Aitkin

  • Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages

    Kate Schroder;Katharine M Irvine;Martin S Taylor;Nilesh J Bokil

  • Modelling high-dimensional data by mixtures of factor analyzers

    G. J. McLachlan;D. Peel;R. W. Bean

  • Automated high-dimensional flow cytometric data analysis

    Saumyadipta Pyne;Xinli Hu;Kui Wang;Elizabeth Rossin

  • Mixtures of Factor Analyzers

    Geoffrey J. McLachlan;David Peel

  • Mixture Models: Inference and Applications to Clustering.

    Bruce Lindsay;G. L. McLachlan;K. E. Basford;Marcel Dekker

  • Comprehensive chemometrics: chemical and biochemical data analysis

    G. J. McLachlan;S. Rathnayake;S. X. Lee

  • Clustering objects on subsets of attributes

    DJ Hand;C Glasbey;D Husmeier;JC Gower

Frequent Co-Authors

Kim Anh Do
Kim Anh Do The University of Texas MD Anderson Cancer Center
John J. McGrath
John J. McGrath Aarhus University
Dianhui Wang
Dianhui Wang La Trobe University
David A. Hafler
David A. Hafler Yale University
Jill P. Mesirov
Jill P. Mesirov University of California, San Diego
Pablo Tamayo
Pablo Tamayo University of California, San Diego
Jason P. Lerch
Jason P. Lerch Hospital for Sick Children
David J. Hand
David J. Hand Imperial College London
David C. Reutens
David C. Reutens University of Queensland
Suzanne K. Chambers
Suzanne K. Chambers University of Technology Sydney

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