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 (35) Conference Publication (74) Book (9) Other Outputs (2) Edited Outputs (1)

1 - 20 of 370 works

2024

Journal Article

Functional mixtures-of-experts

Chamroukhi, Faïcel, Pham, Nhat Thien, Hoang, Van Hà and McLachlan, Geoffrey J. (2024). Functional mixtures-of-experts. Statistics and Computing, 34 (3) 98. doi: 10.1007/s11222-023-10379-0

Functional mixtures-of-experts

2024

Journal Article

Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R

Lyu, Ziyang, Ahfock, Daniel, Thompson, Ryan and McLachlan, Geoffrey J. (2024). Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R. Australian and New Zealand Journal of Statistics, 66 (2), 146-162. doi: 10.1111/anzs.12413

Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R

2024

Journal Article

An overview of skew distributions in model-based clustering

Lee, Sharon X. and McLachlan, Geoffrey J. (2024). An overview of skew distributions in model-based clustering. Science Talks, 9 100298, 100298. doi: 10.1016/j.sctalk.2024.100298

An overview of skew distributions in model-based clustering

2024

Journal Article

A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopy

Koh, Edwin J.Y., Amini, Eiman, Spier, Carlos A., McLachlan, Geoffrey J., Xie, Weiguo and Beaton, Nick (2024). A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopy. Minerals Engineering, 205 108481, 1-16. doi: 10.1016/j.mineng.2023.108481

A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopy

2023

Journal Article

A new algorithm for support vector regression with automatic selection of hyperparameters

Wang, You-Gan, Wu, Jinran, Hu, Zhi-Hua and McLachlan, Geoffrey J. (2023). A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition, 133 108989, 1-9. doi: 10.1016/j.patcog.2022.108989

A new algorithm for support vector regression with automatic selection of hyperparameters

2022

Conference Publication

Exploratory data analysis of TCGA skin cutaneous melanoma RNA-seq data

Zhang, Min, Arief, Vivi, McLachlan, Geoffrey, Nguyen, Quan and Basford, Kaye (2022). Exploratory data analysis of TCGA skin cutaneous melanoma RNA-seq data. Australasian Applied Statistics Conference (AASC), Inverloch, VIC Australia, 28 November - 2 December 2022.

Exploratory data analysis of TCGA skin cutaneous melanoma RNA-seq data

2022

Journal Article

An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models

Koh, Edwin J. Y., Amini, Eiman, Gaur, Shruti, Becerra Maquieira, Miguel, Jara Heck, Christian, McLachlan, Geoffrey J. and Beaton, Nick (2022). An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models. Minerals Engineering, 189 107886, 107886. doi: 10.1016/j.mineng.2022.107886

An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models

2022

Journal Article

Order selection with confidence for finite mixture models

Nguyen, Hien D., Fryer, Daniel and McLachlan, Geoffrey J. (2022). Order selection with confidence for finite mixture models. Journal of the Korean Statistical Society, 52 (1), 154-184. doi: 10.1007/s42952-022-00195-z

Order selection with confidence for finite mixture models

2022

Journal Article

A spatial heterogeneity mixed model with skew-elliptical distributions

Farzammehr, Mohadeseh Alsadat and McLachlan, Geoffrey J. (2022). A spatial heterogeneity mixed model with skew-elliptical distributions. Communications for Statistical Applications and Methods, 29 (3), 373-391. doi: 10.29220/csam.2022.29.3.373

A spatial heterogeneity mixed model with skew-elliptical distributions

2022

Journal Article

Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces

Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D. and McLachlan, Geoffrey J. (2022). Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods, 52 (14), 1-12. doi: 10.1080/03610926.2021.2002360

Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces

2022

Other Outputs

Detecting accounting fraud with noisy labels

Ahfock, Daniel, McLachlan, Geoffrey, Yang, Liu and Zhu, Min (2022). Detecting accounting fraud with noisy labels. UQ Business School.

Detecting accounting fraud with noisy labels

2022

Journal Article

Statistical file-matching of non-Gaussian data: a game theoretic approach

Ahfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2022). Statistical file-matching of non-Gaussian data: a game theoretic approach. Computational Statistics and Data Analysis, 168 107387, 1-16. doi: 10.1016/j.csda.2021.107387

Statistical file-matching of non-Gaussian data: a game theoretic approach

2022

Journal Article

Semi-supervised learning of classifiers from a statistical perspective: a brief review

Ahfock, Daniel and McLachlan, Geoffrey J. (2022). Semi-supervised learning of classifiers from a statistical perspective: a brief review. Econometrics and Statistics, 26, 124-138. doi: 10.1016/j.ecosta.2022.03.007

Semi-supervised learning of classifiers from a statistical perspective: a brief review

2022

Journal Article

An overview of skew distributions in model-based clustering

Lee, Sharon X. and McLachlan, Geoffrey J. (2022). An overview of skew distributions in model-based clustering. Journal of Multivariate Analysis, 188 104853, 1-14. doi: 10.1016/j.jmva.2021.104853

An overview of skew distributions in model-based clustering

2021

Journal Article

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models

Nguyen, Hien Duy, Nguyen, TrungTin, Chamroukhi, Faicel and McLachlan, Geoffrey John (2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications, 8 (1) 13. doi: 10.1186/s40488-021-00125-0

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models

2021

Journal Article

Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach

Ng, Shu Kay, Tawiah, Richard, McLachlan, Geoffrey J. and Gopalan, Vinod (2021). Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach. Biostatistics, 24 (1), 108-123. doi: 10.1093/biostatistics/kxab037

Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach

2021

Journal Article

Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy

Koh, Edwin J. Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy. Minerals Engineering, 173 107230, 107230. doi: 10.1016/j.mineng.2021.107230

Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy

2021

Journal Article

Robust clustering based on finite mixture of multivariate fragmental distributions

Maleki, Mohsen, McLachlan, Geoffrey J. and Lee, Sharon X. (2021). Robust clustering based on finite mixture of multivariate fragmental distributions. Statistical Modelling, 23 (3), 1-26. doi: 10.1177/1471082X211048660

Robust clustering based on finite mixture of multivariate fragmental distributions

2021

Conference Publication

AEGC Machine Learning Workshop presentation

Chatterjee, Robindra, Valenta, Richard, McLachlan, Geoffrey and Weatherley, Dion (2021). AEGC Machine Learning Workshop presentation. Australian Exploration Geoscience Conference, Online, 14-17 September 2021.

AEGC Machine Learning Workshop presentation

2021

Journal Article

Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations

Koh, Edwin J.Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations. Minerals Engineering, 170 107026, 1-11. doi: 10.1016/j.mineng.2021.107026

Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations

Current funding

  • 2023 - 2026
    A Novel Approach to Semi-Supervised Statistical Machine Learning
    ARC Discovery Projects
    Open grant
  • 2017 - 2024
    ARC Training Centre for Innovation in Biomedical Imaging Technology
    ARC Industrial Transformation Training Centres
    Open grant

Past funding

  • 2018 - 2022
    Classification methods for providing personalised and class decisions
    ARC Discovery Projects
    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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