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 Diploma of Science (Advanced), The University of Queensland
  • Australian Mathematical Society, Australian Mathematical Society

Search Professor Geoffrey McLachlan’s works on UQ eSpace

370 works between 1972 and 2024
All (370) Journal Article (249) Book Chapter (34) Conference Publication (75) Book (9) Other Outputs (2) Edited Outputs (1)

81 - 100 of 370 works

2016

Journal Article

A block minorization-maximization algorithm for heteroscedastic regression

Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A block minorization-maximization algorithm for heteroscedastic regression. IEEE Signal Processing Letters, 23 (8) 7501879, 1131-1135. doi: 10.1109/LSP.2016.2586180

A block minorization-maximization algorithm for heteroscedastic regression

2016

Journal Article

Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models

Lee, Sharon X and McLachlan, Geoffrey J (2016). Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models. Statistics and Computing, 26 (3), 573-589. doi: 10.1007/s11222-015-9545-x

Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models

2016

Journal Article

Laplace mixture autoregressive models

Nguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Laplace mixture autoregressive models. Statistics and Probability Letters, 110, 18-24. doi: 10.1016/j.spl.2015.11.006

Laplace mixture autoregressive models

2016

Journal Article

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes

Aghaeepour, Nima, Chattopadhyay, Pratip, Chikina, Maria, Dhaene, Tom, Van Gassen, Sofie, Kursa, Miron, Lambrecht, Bart N., Malek, Mehrnoush, McLachlan, G. J., Qian, Yu, Qiu, Peng, Saeys, Yvan, Stanton, Rick, Tong, Dong, Vens, Celine, Walkowiak, Slawomir, Wang, Kui, Finak, Greg, Gottardo, Raphael, Mosmann, Tim, Nolan, Garry P., Scheuermann, Richard H. and Brinkman, Ryan R. (2016). A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry Part A, 89 (1), 16-21. doi: 10.1002/cyto.a.22732

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes

2016

Journal Article

Mixtures of spatial spline regressions for clustering and classification

Nguyen, Hien D., McLachlan, Geoffrey J. and Wood, Ian A. (2016). Mixtures of spatial spline regressions for clustering and classification. Computational Statistics and Data Analysis, 93, 76-85. doi: 10.1016/j.csda.2014.01.011

Mixtures of spatial spline regressions for clustering and classification

2016

Journal Article

Extending mixtures of factor models using the restricted multivariate skew-normal distribution

Lin, Tsung-I, McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Extending mixtures of factor models using the restricted multivariate skew-normal distribution. Journal of Multivariate Analysis, 143, 398-413. doi: 10.1016/j.jmva.2015.09.025

Extending mixtures of factor models using the restricted multivariate skew-normal distribution

2016

Conference Publication

Unsupervised component-wise EM learning for finite mixtures of skew t-distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Unsupervised component-wise EM learning for finite mixtures of skew t-distributions. 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, 12-15 December 2016. New York, NY, United States: Springer. doi: 10.1007/978-3-319-49586-6_49

Unsupervised component-wise EM learning for finite mixtures of skew t-distributions

2016

Conference Publication

On mixture modelling with multivariate skew distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2016). On mixture modelling with multivariate skew distributions. COMPSTAT: International Conference on Computational Statistics, Oviedo, Spain, 23-26 August 2016. The Hague, The Netherlands: The International Statistical Institute/International Association for Statistical Computing.

On mixture modelling with multivariate skew distributions

2016

Conference Publication

A simple parallel EM algorithm for statistical learning via mixture models

Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2016). A simple parallel EM algorithm for statistical learning via mixture models. International Conference on Digital Image Computing, Gold Coast, QLD, Australia, 30 November - 2 December,2016. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/DICTA.2016.7796997

A simple parallel EM algorithm for statistical learning via mixture models

2016

Book Chapter

Application of mixture models to large datasets

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Application of mixture models to large datasets. Big data analytics: methods and applications. (pp. 57-74) edited by Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao. New Delhi, India: Springer India. doi: 10.1007/978-81-322-3628-3_4

Application of mixture models to large datasets

2016

Journal Article

Laplace mixture of linear experts

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Laplace mixture of linear experts. Computational Statistics and Data Analysis, 93, 177-191. doi: 10.1016/j.csda.2014.10.016

Laplace mixture of linear experts

2016

Journal Article

Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure. Cytometry Part A, 89 (1), 30-43. doi: 10.1002/cyto.a.22789

Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure

2016

Book Chapter

Mixture models for standard p-dimensional Euclidean data

McLachlan, Geoffrey J. and Rathnayake, Suren I. (2016). Mixture models for standard p-dimensional Euclidean data. Handbook of cluster analysis. (pp. 145-171) edited by Christian Hennig, Marina Meila, Fionn Murtagh and Roberto Rocci. Boca Raton, FL, United States: CRC Press. doi: 10.1201/b19706-14

Mixture models for standard p-dimensional Euclidean data

2016

Conference Publication

Robust estimation of mixtures of skew-normal distributions

García-Escudero, L. A., Greselin, F., Mayo-Iscar, A. and McLachlan, G. J. (2016). Robust estimation of mixtures of skew-normal distributions. Scientific Meeting of the Italian Statistical Society, Salerno, Italy, 8-10 November 2016. Fisciano, Italy: Dipartimento di Scienze Economiche e Statistiche, University of Salerno..

Robust estimation of mixtures of skew-normal distributions

2016

Conference Publication

Finding group structures in "Big Data" in healthcare research using mixture models

Ng, Shu-Kay and McLachlan, Geoffrey J. (2016). Finding group structures in "Big Data" in healthcare research using mixture models. IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, China, 15-18 December 2016. Piscataway, NJ, United States: IEE Computer Society. doi: 10.1109/BIBM.2016.7822692

Finding group structures in "Big Data" in healthcare research using mixture models

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

Current funding

  • 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

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Supervision history

Current supervision

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

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