Professor Geoffrey McLachlan
Professor

Geoffrey McLachlan

Email: 
Phone: 
+61 7 336 52150

Background

Professor Geoffrey McLachlan's research interests are in: data mining, statistical analysis of microarray, gene expression data, finite mixture models and medical statistics.

Professor McLachlan received his PhD from the University of Queensland in 1974 and his DSc from there in 1994. His current research projects in statistics are in the related fields of classification, cluster and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognition, and in the field of statistical inference. The focus in the latter field has been on the theory and applications of finite mixture models and on estimation via the EM algorithm.

A common theme of his research in these fields has been statistical computation, with particular attention being given to the computational aspects of the statistical methodology. This computational theme extends to Professor McLachlan's more recent interests in the field of data mining.

He is also actively involved in research in the field of medical statistics and, more recently, in the statistical analysis of microarray gene expression data.

Availability

Professor Geoffrey McLachlan is:
Available for supervision
Media expert

Fields of research

Statistics

Qualifications

  • Bachelor (Honours) of Science (Advanced), The University of Queensland
  • Doctor of Philosophy, The University of Queensland
  • Doctoral (Research) of Science (Advanced), The University of Queensland
  • Australian Mathematical Society, Australian Mathematical Society

Search Professor Geoffrey McLachlan’s works on UQ eSpace

375 works between 1972 and 2026
All (375) Book (9) Journal Article (250) Other Outputs (2) Edited Outputs (1) Conference Publication (77) Book Chapter (36)

101 - 120 of 375 works

2016

Book Chapter

Mixture distributions - further developments

McLachlan, Geoffrey J. (2016). Mixture distributions - further developments. Wiley statsref: statistics reference online. (pp. 1-13) Chichester, United Kingdom: John Wiley & Sons. doi: 10.1002/9781118445112.stat00947.pub2

Mixture distributions - further developments

2015

Journal Article

Application of multiple imputation for missing values in three-way three-mode multi-environment trial data

Tian, Ting, McLachlan, Geoffrey J., Dieter, Mark J. and Basford, Kaye E. (2015). Application of multiple imputation for missing values in three-way three-mode multi-environment trial data. PLoS One, 10 (12) e0144370, e0144370.1-e0144370.25. doi: 10.1371/journal.pone.0144370

Application of multiple imputation for missing values in three-way three-mode multi-environment trial data

2015

Journal Article

Special issue on "New trends on model-based clustering and classification"

Ingrassia, Salvatore, McLachlan, Geoffrey J. and Govaert, Gerard (2015). Special issue on "New trends on model-based clustering and classification". Advances in Data Analysis and Classification, 9 (4), 367-369. doi: 10.1007/s11634-015-0224-8

Special issue on "New trends on model-based clustering and classification"

2015

Edited Outputs

Advances in Data Analysis and Classification

Advances in Data Analysis and Classification. (2015). 9 (4)

Advances in Data Analysis and Classification

2015

Journal Article

Maximum likelihood estimation of Gaussian mixture models without matrix operations

Nguyen, Hien D. and McLachlan, Geoffrey J. (2015). Maximum likelihood estimation of Gaussian mixture models without matrix operations. Advances in Data Analysis and Classification, 9 (4), 371-394. doi: 10.1007/s11634-015-0209-7

Maximum likelihood estimation of Gaussian mixture models without matrix operations

2015

Journal Article

Inference on differences between classes using cluster-specific contrasts of mixed effects

Ng, Shu Kay, McLachlan, Geoffrey J., Wang, Kui, Nagymanyoki, Zoltan, Liu, Shubai and Ng, Shu-Wing (2015). Inference on differences between classes using cluster-specific contrasts of mixed effects. Biostatistics, 16 (1), 98-112. doi: 10.1093/biostatistics/kxu028

Inference on differences between classes using cluster-specific contrasts of mixed effects

2015

Book Chapter

Mixture Models in Statistics

McLachlan, Geoffrey J. (2015). Mixture Models in Statistics. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 624-628) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42055-6

Mixture Models in Statistics

2015

Book Chapter

Computation: Expectation-Maximization Algorithm

McLachlan, Geoffrey J. (2015). Computation: Expectation-Maximization Algorithm. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 469-474) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42007-6

Computation: Expectation-Maximization Algorithm

2015

Book Chapter

Multivariate Analysis: Classification and Discrimination

McLachlan, Geoffrey (2015). Multivariate Analysis: Classification and Discrimination. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 116-120) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42150-1

Multivariate Analysis: Classification and Discrimination

2015

Journal Article

Nature and man: the goal of bio-security in the course of rapid and inevitable human development

Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2015). Nature and man: the goal of bio-security in the course of rapid and inevitable human development. Journal of the Indian Society of Agricultural Statistics, 69 (2), 117-125.

Nature and man: the goal of bio-security in the course of rapid and inevitable human development

2014

Journal Article

A robust factor analysis model using the restricted skew-t distribution

Lin, Tsung-I, Wu, Pal H., McLachlan, Geoffrey J. and Lee, Sharon X. (2014). A robust factor analysis model using the restricted skew-t distribution. Test, 24 (3), 510-531. doi: 10.1007/s11749-014-0422-2

A robust factor analysis model using the restricted skew-t distribution

2014

Journal Article

On the number of components in a Gaussian mixture model

McLachlan, Geoffrey J. and Rathnayake, Suren (2014). On the number of components in a Gaussian mixture model. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 4 (5), 341-355. doi: 10.1002/widm.1135

On the number of components in a Gaussian mixture model

2014

Journal Article

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

Pyne, Saumyadipta, Lee, Sharon X., Wang, Kui, Irish, Jonathan, Tamayo, Pablo, Nazaire, Marc-Danie, Duong, Tarn, Ng, Shu-Kay, Hafler, David, Levy, Ronald, Nolan, Garry P., Mesirov, Jill and McLachlan, Geoffrey J. (2014). Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLoS One, 9 (7) e100334, e100334.1-e100334.11. doi: 10.1371/journal.pone.0100334

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

2014

Journal Article

False discovery rate control in magnetic resonance imaging studies via Markov random fields

Nguyen, Hien D., McLachlan, Geoffrey J., Cherbuin, Nicolas and Janke, Andrew L. (2014). False discovery rate control in magnetic resonance imaging studies via Markov random fields. IEEE Transactions on Medical Imaging, 33 (8) 6811158, 1735-1748. doi: 10.1109/TMI.2014.2322369

False discovery rate control in magnetic resonance imaging studies via Markov random fields

2014

Journal Article

The 2nd special issue on advances in mixture models

Boehning, Dankmar, Hennig, Christian, McLachlan, Geoffrey J. and McNicholas, Paul D. (2014). The 2nd special issue on advances in mixture models. Computational Statistics and Data Analysis, 71, 1-2. doi: 10.1016/j.csda.2013.10.010

The 2nd special issue on advances in mixture models

2014

Journal Article

Mixture models for clustering multilevel growth trajectories

Ng S.K. and McLachlan G.J. (2014). Mixture models for clustering multilevel growth trajectories. Computational Statistics and Data Analysis, 71, 43-51. doi: 10.1016/j.csda.2012.12.007

Mixture models for clustering multilevel growth trajectories

2014

Journal Article

Finite mixtures of multivariate skew t-distributions: Some recent and new results

Lee, Sharon and McLachlan, Geoffrey J. (2014). Finite mixtures of multivariate skew t-distributions: Some recent and new results. Statistics and Computing, 24 (2), 181-202. doi: 10.1007/s11222-012-9362-4

Finite mixtures of multivariate skew t-distributions: Some recent and new results

2014

Conference Publication

Application of multiple imputation to incomplete three-way three-mode multi-environment trial data

Tian, T., McLachlan, G., Dieters, M. and Basford, K. (2014). Application of multiple imputation to incomplete three-way three-mode multi-environment trial data. International Biometric Conference, Florence (Italy), 6-11 July 2014. Florence, Italy: International Biometric Society.

Application of multiple imputation to incomplete three-way three-mode multi-environment trial data

2014

Conference Publication

Asymptotic inference for hidden process regression models

Nguyen, Hien D. and McLachlan, Geoffrey J. (2014). Asymptotic inference for hidden process regression models. 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, Australia, 29 June - 2 July 2014. Piscataway, NJ, United States: IEEE. doi: 10.1109/SSP.2014.6884624

Asymptotic inference for hidden process regression models

2014

Conference Publication

Mixture of regression models with latent variables and sparse coefficient parameters

Ng, Shu-Kay and McLachlan, Geoffrey J. (2014). Mixture of regression models with latent variables and sparse coefficient parameters. COMPSTAT 2014, Geneva Switzerland, 19- 22 August 2014. Hague, Netherlands: The International Statistical Institute/International Association for Statistical Computing.

Mixture of regression models with latent variables and sparse coefficient parameters

Current funding

  • 2026 - 2030
    The Screen Use Taxonomy: a new framework for investigating the harms and benefits of screen time among children and adolescents
    NHMRC IDEAS Grants
    Open grant
  • 2023 - 2026
    A Novel Approach to Semi-Supervised Statistical Machine Learning
    ARC Discovery Projects
    Open grant

Past funding

  • 2018 - 2022
    Classification methods for providing personalised and class decisions
    ARC Discovery Projects
    Open grant
  • 2017 - 2024
    ARC Training Centre for Innovation in Biomedical Imaging Technology
    ARC Industrial Transformation Training Centres
    Open grant
  • 2017 - 2020
    Power Quality Monitoring of Grids with High Penetration of Power Converters
    ARC Linkage Projects
    Open grant
  • 2017 - 2020
    Expanding the Role of Mixture Models in Statistical Analyses of Big Data
    ARC Discovery Projects
    Open grant
  • 2015 - 2018
    Gene expression profiling in critically ill patients with septic shock: The ADRENAL-GEPS Study
    NHMRC Project Grant
    Open grant
  • 2015 - 2017
    Large-Scale Statistical Inference: Multiple Testing
    ARC Discovery Projects
    Open grant
  • 2014 - 2016
    System to Synapse
    ARC Linkage Projects
    Open grant
  • 2014 - 2017
    Advanced Mixture Models for the Analysis of Modern-Day Data
    ARC Discovery Projects
    Open grant
  • 2012 - 2014
    System to synapse: a small animal imaging suite
    UQ Collaboration and Industry Engagement Fund
    Open grant
  • 2012 - 2014
    Joint Clustering and Matching of Multivariate Samples Across Objects
    ARC Discovery Projects
    Open grant
  • 2012 - 2014
    Statistical Modelling of Complex, High-Dimensional Data
    Vice-Chancellor's Senior Research Fellowship
    Open grant
  • 2011 - 2013
    A New Approach to Fast Matrix Factorization for the Statistical Analysis of High-Dimensional Data
    ARC Discovery Projects
    Open grant
  • 2008 - 2010
    Mixture models for high-dimensional clustering with applications to tumour classification, network intrusion, and text classification
    ARC Discovery Projects
    Open grant
  • 2007 - 2011
    Multivariate Methods for the Analysis of Microarray Gene-Expression Data with Applications to Cancer Diagnostics
    ARC Discovery Projects
    Open grant
  • 2007 - 2009
    Noncoding RNAs as prognostic markers and therapeutic targets in breast cancer
    NHMRC Project Grant
    Open grant
  • 2004
    ARC Network in Imaging Science and Technology
    ARC Seed Funding for Research Networks
    Open grant
  • 2004
    ARC Research Network in Microarray Technology
    ARC Seed Funding for Research Networks
    Open grant
  • 2003 - 2010
    ARC Centre of Excellence in Bioinformatics
    ARC Centres of Excellence
    Open grant
  • 2003
    Classification of Microarray Gene-Expression Data
    ARC Discovery Projects
    Open grant
  • 2003
    Classification of Microarray Gene-expression Data
    UQ External Support Enabling Grant
    Open grant
  • 2003
    Unsupervised learning of finite mixture models in data mining applications
    ARC Discovery Projects
    Open grant
  • 2000 - 2002
    Classification of Multiply Observed Features in Terms of Fitted Densities
    ARC Australian Research Council (Large grants)
    Open grant
  • 2000 - 2002
    On Algorithms for the Automatic Analysis and Segmentation of Correlated Images
    ARC Australian Research Council (Large grants)
    Open grant
  • 1999 - 2001
    Artificial Neural Networks and the EM Algorithm
    ARC Australian Research Council (Large grants)
    Open grant
  • 1999
    On mixture regression models with constrained components for application to failure data on heart valves
    ARC Australian Research Council (Small grants)
    Open grant
  • 1998
    Robust cluster analysis
    ARC Australian Research Council (Small grants)
    Open grant
  • 1997
    On mixture models in medical imaging
    ARC Australian Research Council (Small grants)
    Open grant
  • 1997 - 1999
    The Analysis of Plant Adaptation Data with Emphasis on Unbalanced Sets
    ARC Australian Research Council (Large grants)
    Open grant
  • 1995 - 1997
    Approximation of multi-dimensional functions for curve fitting and model building
    ARC Australian Research Council (Large grants)
    Open grant

Availability

Professor Geoffrey McLachlan is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Supervision history

Current supervision

  • Doctor Philosophy

    Role of Finite Mixture Models in Semi-Supervised Learning

    Principal Advisor

    Other advisors: Dr Sharon Lee

  • Doctor Philosophy

    An Adaptive Cross-Cultural Platform for Early Readiness Profiling of University Students

    Associate Advisor

    Other advisors: Professor Robyn Gillies

  • Doctor Philosophy

    Robust Multi-Agent Reinforcement Learning under Non-Stationarity, Incomplete Information, and Adversarial Dynamics

    Associate Advisor

    Other advisors: Professor Fred Roosta

Completed supervision

Enquiries

Contact Professor Geoffrey McLachlan directly for media enquiries about:

  • Bioinformatics
  • Computation - statistics
  • Computer learning
  • Data mining
  • Gene expression data
  • Image analysis - statistics
  • Machine learning
  • Neural networks
  • Pattern recognition - statistics
  • Statistical methodology
  • Statistics

Need help?

For help with finding experts, story ideas and media enquiries, contact our Media team:

communications@uq.edu.au