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)

221 - 240 of 370 works

2004

Journal Article

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Ng, Shu-Kay and McLachlan, Geoffrey J. (2004). Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition, 37 (8), 1573-1589. doi: 10.1016/j.patcog.2004.02.012

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

2004

Journal Article

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

Ng, S. K. and McLachlan, G. J. (2004). Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification. IEEE Transactions on Neural Networks, 15 (3), 738-749. doi: 10.1109/TNN.2004.826217

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

2004

Conference Publication

On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

McLachlan, G. J., Chang, S., Mar, J. and Ambroise, C. (2004). On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples. Second Asia-Pacific Bioinformatics Conference, Dunedin, New Zealand, 18-22 January 2004. Sydney, Australia: Australian Computer Society.

On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

2004

Journal Article

Mixture modelling for cluster analysis

McLachlan, G. J. and Chang, S. U. (2004). Mixture modelling for cluster analysis. Statistical Methods In Medical Research, 13 (5), 347-361. doi: 10.1191/0962280204sm372ra

Mixture modelling for cluster analysis

2004

Book Chapter

The EM algorithm

Ng, S. K., Krishnan, T. and McLachlan, G. J. (2004). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 137-168) edited by J.E. Gentle, W. Hardle and Y. Mori. Germany: Springer-Verlag.

The EM algorithm

2004

Book

Analyzing microarray gene expression data

McLachlan, Geoffrey J., Do, Kim-Anh and Ambroise, Christophe (2004). Analyzing microarray gene expression data. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/047172842x

Analyzing microarray gene expression data

2004

Journal Article

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

McLachlan, GJ and Khan, N (2004). On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples. Journal of Multivariate Analysis, 90 (1), 90-105. doi: 10.1016/j.jmva.2004.02.002

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

2004

Conference Publication

Linking gene-expression experiments with survival-time data

Jones, L., Ng, A.S. K., Monico, K. A. and McLachlan, G. J. (2004). Linking gene-expression experiments with survival-time data. 19th International Workshop on Statistical Modelling, Florence, 4-8 July 2004. Italy: Firenze University Press.

Linking gene-expression experiments with survival-time data

2004

Book

Analyzing Microarray Gene Expression Data

McLachlan, G. J., Do, K. and Ambroise, C (2004). Analyzing Microarray Gene Expression Data. New York: Wiley-Interscience.

Analyzing Microarray Gene Expression Data

2004

Journal Article

Clustering objects on subsets of attributes - Discussion

Hand, DJ, Glasbey, C, Husmeier, D, Gower, JC, van Houwelingen, HC, Bugrien, JB, Nason, G, Critchley, F, Hoff, PD, McLachlan, GJ and Bean, RW (2004). Clustering objects on subsets of attributes - Discussion. Journal of The Royal Statistical Society Series B-statistical Methodology, 66 (4), 839-849.

Clustering objects on subsets of attributes - Discussion

2003

Journal Article

Model-based clustering in gene expression microarrays: an application to breast cancer data

Mar, J.C. and McLachlan, G.J. (2003). Model-based clustering in gene expression microarrays: an application to breast cancer data. International Journal of Software Engineering and Knowledge Engineering, 13 (6), 579-592. doi: 10.1142/S0218194003001482

Model-based clustering in gene expression microarrays: an application to breast cancer data

2003

Journal Article

Model-based clustering in gene expression microarrays: an application to breast cancer data

Mar, J. C. and McLachlan, G. J. (2003). Model-based clustering in gene expression microarrays: an application to breast cancer data. International Journal of Software Engineering And Knowledge Engineering, 13 (6), 579-592. doi: 10.1142/S0218194003001482

Model-based clustering in gene expression microarrays: an application to breast cancer data

2003

Journal Article

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

Ng, S. K. and McLachlan, G. J. (2003). An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data. Statistics In Medicine, 22 (7), 1097-1111. doi: 10.1002/sim.1371

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

2003

Journal Article

On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures

Ng, S. K. and McLachlan, G. J. (2003). On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures. Statistics And Computing, 13 (1), 45-55. doi: 10.1023/A:1021987710829

On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures

2003

Journal Article

Modelling High-Dimensional Data by Mixtures of Factor Analyzers

McLachlan, G. J., Peel, D. and Bean, R. W. (2003). Modelling High-Dimensional Data by Mixtures of Factor Analyzers. Computational Statistics & Data Analysis, 41 (3-4), 379-388. doi: 10.1016/S0167-9473(02)00183-4

Modelling High-Dimensional Data by Mixtures of Factor Analyzers

2003

Conference Publication

On clustering by mixture models

McLachlan, GJ, Ng, SK and Peel, D (2003). On clustering by mixture models. 25th Annual Conference of the German-Classification-Society, Munich Germany, Mar 14-16, 2001. BERLIN: SPRINGER-VERLAG BERLIN.

On clustering by mixture models

2003

Journal Article

On some variants of the EM algorithm for the fitting of finite mixture models

Ng, A.S. K. and McLachlan, G. J. (2003). On some variants of the EM algorithm for the fitting of finite mixture models. Austrian Journal of Statistics, 32 (1 & 2), 143-161.

On some variants of the EM algorithm for the fitting of finite mixture models

2003

Book Chapter

On clustering by mixture models

McLachlan, G. J., Ng, A.S. K. and Peel, D. (2003). On clustering by mixture models. Exploratory Data Analysis in Empirical Research. (pp. 141-148) edited by M. Schwaiger and O. Opitz. Germany: Springer. doi: 10.1007/978-3-642-55721-7_16

On clustering by mixture models

2003

Conference Publication

Segmentation of brain MR images with bias field correction

Kim, S-G., Ng, A.S. K., McLachlan, G. J. and Wang, D. (2003). Segmentation of brain MR images with bias field correction. WDIC 2003, The University of Queensland, Brisbane, 7 February 2003. Brisbane, Australia: The University of Queensland.

Segmentation of brain MR images with bias field correction

2003

Conference Publication

Robust estimation in Gaussian mixtures using multiresolution Kd -trees

Ng, A. S. K. and McLachlan, G. J. (2003). Robust estimation in Gaussian mixtures using multiresolution Kd -trees. Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003, Sydney, Australia, 10-12 December 2003. Melbourne, Australia: CSIRO Publishing.

Robust estimation in Gaussian mixtures using multiresolution Kd -trees

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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