Samuel Muller

PhD, Fellow of the American Statistical Association, Professor

  • 960 Citations
  • 13 h-Index
19992020

Research output per year

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Personal profile

Biography

Samuel Muller is a Professor of Statistics with expertise in variable selection and inference for statistically challenging data. He is the Head of Department in the Department of Mathematics and Statistics and a Theory and Methods Editor for the Australian and New Zealand Journal of Statistics. He is a Member of the ARC College of Experts for a three year term until 2021. His past employment includes

  • 2020-present Head of Department and Professor of Statistics, Department of Mathematics and Statistics, Macquarie University
  • 2020-present Honorary Professor, School of Mathematics and Statistics, University of Sydney
  • 2019-2020 Acting and then Interim Head of School, School of Mathematics and Statistics (SoMS), University of Sydney (USYD)
  • 2018 Professor of Statistics, SoMS, USYD
  • 2016-2019 Associate Dean Research Education, Faculty of Science, USYD
  • 2015 Associate Professor, SoMS, USYD
  • 2010 Senior Lecturer, SoMS, USYD
  • 2008 Lecturer, SoMS, USYD
  • 2006 Lecturer, SoMS, University of Western Australia
  • 2004 Lecturer, Department of Mathematical Statistics and Actuarial Science, University of Bern, Switzerland
  • 2003 Postdoctoral Fellowship (from the Swiss NSF), Mathematical Sciences Institute, The Australian National University

Research interests

Samuel Muller is a member of the Statistics Research Group. His research is motivated by complex data, including for longitudinal, clustered and correlated univariate and multivariate responses; classification and improved prediction methods for multiplatform data in but not limited to omics data. He investigates theoretical properties of regularization methods in various asymptotic scenarios and improves these methods by learning from resampling-based stability information. He develops advanced model visualisation methods to enable interactive and dynamic model building, investigates robust selection and estimation methods in regression type models, and devises statistical methods for the analysis of multi-layered and structured data in bioinformatics, microbiome and neuroscience applications.

Research student supervision

Samuel Muller has supervised to completion 8 PhD and 13 Honours students. Currently he supervises 6 PhD students.

Education/Academic qualification

Mathematics, PhD, University of Bern

Statistics and Insurance Mathematics, Diploma (MSc), University of Bern

External positions

Honorary Professor, University of Sydney

1 Sep 202031 Aug 2023

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Projects

ARC DP: Dimension reduction and model selection for statistically challenging data

Welsh, A. H., Muller, S., Hui, F. & Ma, Y.

13/04/1812/04/21

Project: Research

ARC DP: Prognosis based network-type feature extraction for complex biological data

Yang, J. Y. H., Muller, S., Omerod, J., Yang, P. & Mann, G. J.

1/05/171/05/20

Project: Research

ARC DP: Vertically integrated statistical modelling in multi-layered omics studies.

Yang, J. Y. H., Muller, S. & Mann, G. J.

1/06/1330/06/17

Project: Research

ARC DP: Building models for complex data

Welsh, A. H. & Muller, S.

1/01/1131/12/14

Project: Research

Research Outputs

  • 960 Citations
  • 13 h-Index
  • 61 Article
  • 1 Other report
  • 1 Editorial

mcvis: multi-collinearity visualisation

Wang, K. & Muller, S., 2020, In : Biometric Bulletin. 37, 3, p. 12-14 3 p.

Research output: Contribution to journalArticle

mcvis: a new framework for collinearity discovery, diagnostic, and visualization

Lin, C., Wang, K. & Muller, S., 30 Jul 2020, In : Journal of Computational and Graphical Statistics. 8 p.

Research output: Contribution to journalArticle

  • Prediction modelling - part 2 - using machine learning strategies to improve transplantation outcomes

    Coorey, C. P., Sharma, A., Muller, S. & Yang, J. Y. H., 8 Sep 2020, In : Kidney International.

    Research output: Contribution to journalArticle

  • The LASSO on latent indices for regression modeling with ordinal categorical predictors

    Hui, F. K. C., Müller, S. & Welsh, A. H., Sep 2020, In : Computational Statistics and Data Analysis. 149, p. 1-13 13 p., 106951.

    Research output: Contribution to journalArticle

  • Prizes

    Commendation Teaching

    Samuel Muller (Recipient), 2015

    Prize: Honorary award

  • Faculty Award in Mathematics

    Samuel Muller (Recipient), 1999

    Prize: Honorary award