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)

61 - 80 of 370 works

2017

Journal Article

Some theoretical results regarding the polygonal distribution

Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Some theoretical results regarding the polygonal distribution. Communications in Statistics: Theory and Methods, 47 (20), 5083-5095. doi: 10.1080/03610926.2017.1386312

Some theoretical results regarding the polygonal distribution

2017

Journal Article

Finite mixture models in biostatistics

Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Handbook of Statistics, 36, 75-102.

Finite mixture models in biostatistics

2017

Journal Article

Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution

Lin, Tsung-I, Wang, Wan-Lun, McLachlan, Geoffrey J. and Lee, Sharon X. (2017). Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution. Statistical Modelling, 18 (1), 50-72. doi: 10.1177/1471082X17718119

Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution

2017

Journal Article

Maximum pseudolikelihood estimation for model-based clustering of time series data

Nguyen, Hien D., McLachlan, Geoffrey J., Orban, Pierre, Bellec, Pierre and Janke, Andrew L. (2017). Maximum pseudolikelihood estimation for model-based clustering of time series data. Neural Computation, 29 (4), 990-1020. doi: 10.1162/NECO_a_00938

Maximum pseudolikelihood estimation for model-based clustering of time series data

2017

Book Chapter

Clustering

McLachlan, G. J., Bean, R. W. and Ng, S. K. (2017). Clustering. Bioinformatics Vol. II: Structure, Function, and Applications. (pp. 345-362) edited by Jonathan M. Keith. New York, NY, United States: Humana Press. doi: 10.1007/978-1-4939-6613-4_19

Clustering

2017

Conference Publication

Iteratively-reweighted least-squares fitting of support vector machines: a majorization–minimization algorithm approach

Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Iteratively-reweighted least-squares fitting of support vector machines: a majorization–minimization algorithm approach. Future Technologies Conference (FTC) 2017, Vancouver, Canada, 29-30 November 2017. Piscataway, NJ United States: IEEE.

Iteratively-reweighted least-squares fitting of support vector machines: a majorization–minimization algorithm approach

2017

Book Chapter

On the identification of correlated differential features for supervised classification of high-dimensional data

Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. Data science, innovative developments in data analysis and clustering. (pp. 43-57) edited by Francesco Palumbo, Angela Montanari and Maurizio Vichi. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6

On the identification of correlated differential features for supervised classification of high-dimensional data

2017

Conference Publication

Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering

Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering. 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia, 1 - 4 August 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/Trustcom/BigDataSE/ICESS.2017.356

Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering

2017

Book Chapter

Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test

Nguyen, Hien D., McLachlan, Geoffrey J. and Hill, Michelle M. (2017). Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test. Proteome bioinformatics. (pp. 109-117) edited by Shivakumar Keerthikumar and Suresh Mathivanan. New York, NY United States: Humana Press. doi: 10.1007/978-1-4939-6740-7_9

Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test

2017

Book Chapter

Finite mixture models in biostatistics

Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Disease Modelling and Public Health, Part A. (pp. 75-102) edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne and C.R. Rao. Amsterdam, Netherlands: Elsevier. doi: 10.1016/bs.host.2017.08.005

Finite mixture models in biostatistics

2017

Conference Publication

Privacy distributed three-party learning of Gaussian mixture models

Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Privacy distributed three-party learning of Gaussian mixture models. International Conference on Applications and Technologies in Information Security (ATIS), Auckland, New Zealand, 6-7 July 2017. Singapore: Springer Singapore. doi: 10.1007/978-981-10-5421-1_7

Privacy distributed three-party learning of Gaussian mixture models

2017

Conference Publication

On the identification of correlated differential features for supervised classification of high-dimensional data

Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. 15th Conference of the International Federation of Classification Societies (IFCS), Bologna, Italy, July 5-8, 2015. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6_4

On the identification of correlated differential features for supervised classification of high-dimensional data

2016

Journal Article

A universal approximation theorem for mixture-of-experts models

Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A universal approximation theorem for mixture-of-experts models. Neural Computation, 28 (12), 2585-2593. doi: 10.1162/NECO_a_00892

A universal approximation theorem for mixture-of-experts models

2016

Journal Article

Partial identification in the statistical matching problem

Ahfock, Daniel, Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Partial identification in the statistical matching problem. Computational Statistics and Data Analysis, 104, 79-90. doi: 10.1016/j.csda.2016.06.005

Partial identification in the statistical matching problem

2016

Journal Article

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Lloyd-Jones, Luke R., Nguyen, Hien D., Mclachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics, 72 (4), 1255-1265. doi: 10.1111/biom.12531

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

2016

Journal Article

Progress on a conjecture regarding the triangular distribution

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Progress on a conjecture regarding the triangular distribution. Communications in Statistics: Theory and Methods, 46 (22), 11261-11271. doi: 10.1080/03610926.2016.1263742

Progress on a conjecture regarding the triangular distribution

2016

Journal Article

Spatial clustering of time series via mixture of autoregressions models and Markov random fields

Nguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Spatial clustering of time series via mixture of autoregressions models and Markov random fields. Statistica Neerlandica, 70 (4), 414-439. doi: 10.1111/stan.12093

Spatial clustering of time series via mixture of autoregressions models and Markov random fields

2016

Journal Article

Linear mixed models with marginally symmetric nonparametric random effects

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Linear mixed models with marginally symmetric nonparametric random effects. Computational Statistics and Data Analysis, 103, 151-169. doi: 10.1016/j.csda.2016.05.005

Linear mixed models with marginally symmetric nonparametric random effects

2016

Journal Article

Maximum likelihood estimation of triangular and polygonal distributions

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Maximum likelihood estimation of triangular and polygonal distributions. Computational Statistics and Data Analysis, 102, 23-36. doi: 10.1016/j.csda.2016.04.003

Maximum likelihood estimation of triangular and polygonal distributions

2016

Journal Article

Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas. Statistics & Probability Letters, 116, 1-5. doi: 10.1016/j.spl.2016.04.004

Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

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
    UQ External Support Enabling Grant
    Open grant
  • 2003
    Classification of Microarray Gene-Expression Data
    ARC Discovery Projects
    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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