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)

161 - 180 of 370 works

2010

Book Chapter

Clustering of high-dimensional data via finite mixture models

McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of high-dimensional data via finite mixture models. Advances in Data Analysis, Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC Helmut-Schmidt-University, Hamburg, July 16–18, 2008. (pp. 33-44) edited by Andreas Fink, Berthold Lausen, Wilfried Seidel and Alfred Ultsch. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-01044-6

Clustering of high-dimensional data via finite mixture models

2009

Journal Article

A score test for assessing the cured proportion in the long-term survivor mixture model

Zhao, Yun, Lee, Andy H., Yau, Kelvin K. W., Burke, Valerie and McLachlan, Geoffrey J. (2009). A score test for assessing the cured proportion in the long-term survivor mixture model. Statistics In Medicine, 28 (27), 3454-3466. doi: 10.1002/sim.3696

A score test for assessing the cured proportion in the long-term survivor mixture model

2009

Conference Publication

Ensemble approach for the classification of imbalanced data

Nikulin, Vladimir, McLachlan, Geoffrey J. and Ng, Shu Kay (2009). Ensemble approach for the classification of imbalanced data. AI 2009: Advances in Artificial Intelligence, Melbourne, VIC, Australia, 1-4 December 2009. Berlin, Germany: Springer. doi: 10.1007/978-3-642-10439-8_30

Ensemble approach for the classification of imbalanced data

2009

Book Chapter

EM

McLachlan, G. J. and Ng, S-K. (2009). EM. The Top Ten Algorithms in Data Mining. (pp. 93-115) edited by Wu, X. and Kumar, V.. Florida, United States: Chapman & Hall/CRC. doi: 10.1201/9781420089653-12

EM

2009

Journal Article

Classification of imbalanced marketing data with balanced random sets

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. Journal of Machine Learning Research, 7, 89-100.

Classification of imbalanced marketing data with balanced random sets

2009

Book Chapter

Model-based clustering

McLachlan, G. J. (2009). Model-based clustering. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 655-681) edited by Steven D. Brown, Roma Tauler and Beata Walczak. Oxford, U.K.: Elsevier Science. doi: 10.1016/B978-044452701-1.00068-5

Model-based clustering

2009

Conference Publication

Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data

Wang, Kui, Ng, Shu-Kay and McLachlan, Geoffrey J. (2009). Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data. 2009 Conference of Digital Image Computing: Techniques and Applications, Melbourne, Australia, 1-3 December 2009. Los Alamitos, California: IEEE Computer Society. doi: 10.1109/DICTA.2009.88

Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data

2009

Journal Article

Automated high-dimensional flow cytometric data analysis

Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., De Jager, P. L. and Mesirow, J. P. (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences of the United States of America, 106 (21), 8519-8524. doi: 10.1073/pnas.0903028106

Automated high-dimensional flow cytometric data analysis

2009

Conference Publication

On a general method for matrix factorisation applied to supervised classification

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). On a general method for matrix factorisation applied to supervised classification. 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, Washington, D.C., U.S.A., 1-4 November 2009. Piscataway, NJ, United States: IEEE. doi: 10.1109/BIBMW.2009.5332135

On a general method for matrix factorisation applied to supervised classification

2009

Conference Publication

Regularised k-means clustering for dimension reduction applied to supervised classification

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Regularised k-means clustering for dimension reduction applied to supervised classification. Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics 2009, Genova, Italy, 15-17 October 2009. Salerno, Italy: DMI Proceedings Series.

Regularised k-means clustering for dimension reduction applied to supervised classification

2009

Journal Article

Microarray data analysis for differential expression: a tutorial

Suarez, E., Burguete, A. and McLachlan, G. J. (2009). Microarray data analysis for differential expression: a tutorial. Puerto Rico Health Sciences Journal, 28 (2), 89-104.

Microarray data analysis for differential expression: a tutorial

2009

Book Chapter

Statistical analysis on microarray data: selection of gene prognosis signatures

Le Cao, Kim-Anh and McLachlan, Geoffrey J. (2009). Statistical analysis on microarray data: selection of gene prognosis signatures. Computational biology: issues and applications in oncology. (pp. 55-76) edited by Tuan Pham. New York, United States: Springer. doi: 10.1007/978-1-4419-0811-7_3

Statistical analysis on microarray data: selection of gene prognosis signatures

2009

Book Chapter

Clustering methods for gene-expression data

Flack, L. K. and McLachlan, G. J. (2009). Clustering methods for gene-expression data. Handbook of Research on Systems Biology Applications in Medicine. (pp. 209-220) edited by Andriani Daskalaki. United States: IGI Global. doi: 10.4018/978-1-60566-076-9.ch011

Clustering methods for gene-expression data

2009

Conference Publication

Classification of imbalanced marketing data with balanced random sets

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. AISTATS 2009, Clearwater Beach, FL, United States, 16-18 April 2009. Cambridge, MA, United States: M I T Press.

Classification of imbalanced marketing data with balanced random sets

2008

Journal Article

Wallace's approach to unsupervised learning: The Snob program

Jorgensen, Murray A. and McLachlan, Geoffrey J. (2008). Wallace's approach to unsupervised learning: The Snob program. The Computer Journal, 51 (5), 571-578. doi: 10.1093/comjnl/bxm121

Wallace's approach to unsupervised learning: The Snob program

2008

Journal Article

Characteristic Traffic Load Effects from a Mixture of Loading Events on Short to Medium Span Bridges

Caprani, C. C., O'Brien, E. J. and McLachlan, G. J. (2008). Characteristic Traffic Load Effects from a Mixture of Loading Events on Short to Medium Span Bridges. Structural Safety, 30 (3), 394-404. doi: 10.1016/j.strusafe.2006.11.006

Characteristic Traffic Load Effects from a Mixture of Loading Events on Short to Medium Span Bridges

2008

Book Chapter

Clustering of microarray data via mixture models

McLachlan, Geoffrey J., Ng, Angus and Bean, Richard W. (2008). Clustering of microarray data via mixture models. Statistical advances in the biomedical sciences: clinical trials, epidemiology, survival analysis, and bioinformatics. (pp. 365-383) edited by Atanu Biswas, Sujay Datta, Jason P. Fine and Mark R. Segal. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470181218.ch21

Clustering of microarray data via mixture models

2008

Journal Article

Bivariate mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African Americans, Hispanics, and whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study

McLaren, C. E., Gordeuk, V. R., Chen, W. -P., Barton, J. C., Action, R. T., Speechley, M., Castro, O., Adams, P. C., Snively, B. M., Harris, E. L., Reboussin, D. M., McLachlan, G. J. and Bean, R. (2008). Bivariate mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African Americans, Hispanics, and whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study. Translational Research, 151 (2), 97-109. doi: 10.1016/j.trsl.2007.10.002

Bivariate mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African Americans, Hispanics, and whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study

2008

Journal Article

On selection biases with prediction rules formed from gene expression data

Zhu, J. X., McLachlan, G. J., Jones, L. B. T. and Wood, I. A. (2008). On selection biases with prediction rules formed from gene expression data. Journal of Statistical Planning and Inference, 138 (2), 374-386. doi: 10.1016/j.jspi.2007.06.003

On selection biases with prediction rules formed from gene expression data

2008

Journal Article

Comments on: Augmenting the bootstrap to analyze high dimensional genomic data

McLachlan, Geoffrey J., Wang, K. and Ng, S. K. (2008). Comments on: Augmenting the bootstrap to analyze high dimensional genomic data. Test, 17 (1), 43-46. doi: 10.1007/s11749-008-0106-x

Comments on: Augmenting the bootstrap to analyze high dimensional genomic data

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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