Professor Sally Cripps

BEng(Chem) Sydney; MBA UWA; PhD UNSW

Member of the Brain and Mind Centre

F07 - Carslaw Building
The University of Sydney

Telephone +61 2 9351 3835
Fax +61 2 9351 6409

Biographical details

Sally's research interests lie mainly in Bayesian methodology. In particular the spectral analysis of time series, flexible models for panel and longitudinal data, Gaussian and non-Gaussian nonparametric regression, and the development of efficient algorithms for large datasets. Sally holds an ARC Future Fellowship (2014-2018) and is an associate investigator in the ARC's centre of excellence Big Data, Big Models Big Insights.

Her applied work includes modelling cognitive development and voltage fluctuations obtained from an intracranial electroencephalogram (IEEG). She also works with researchers in the Centre of Ethical Leadership at Ormond College to study the development of leadership in China.

Sally's research papers have been published in Journal of the American Statistical Association, Journal of the Royal Statistical Society Series b, Biometrika, Journal of Computational and Graphical Statistics. She has been an invited and a regular speaker at international conferences such as the Institute of Mathematical Statistical (ISM) World Congress, MCMSki and the International Society for Bayesian Analysis (ISBA). She is on the editorial board for the journal Big Data Research.

Research interests

  • Bayesian modelling of longitudinal and panel data
  • Spectral Analysis
  • Mixture Models for time series

Current research students

Project title Research student
Bayesian methods for analysing non-stationary, multivariate time series: applications for machine learning Nick JAMES

Selected grants

2017

  • CMCRC Postdoctoral Researcher Postdoc - CMCRC Postdoctoral Researcher Aldo SAAVEDRA Postdoc - Tim SHAW Supervisor; Shaw T, Saavedra A, Morris J, Cripps S; Capital Markets CRC/Fellowship.

2014

  • Flexible Models for Longitudinal and Panel Data; Cripps S; Australian Research Council (ARC)/Future Fellowships (FT).

2009

  • Using Advances in Bayesian Statistics to Estimate Australian Rainfall Variations in a Climate Change World; Cripps S, Carter C, England M; Australian Research Council (ARC)/Linkage Projects (LP).

Selected publications

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Book Chapters

  • Wood, S. (2014). Applications of Bayesian Smoothing Splines. In Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens (Eds.), Bayesian Theory and Applications, (pp. 309-335). Oxford: Oxford University Press.

Journals

  • Marchant Matus, R., Haan, S., Clancey, G., Cripps, S. (2018). Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression. Security Informatics, 7(1). [More Information]
  • Chandra, R., Cripps, S. (2018). Coevolutionary multi-task learning for feature-based modular pattern classification. Neurocomputing, 319, 164-175. [More Information]
  • Gwynne, K., McCowen, D., Cripps, S., Lincoln, M., Irving, M., Blinkhorn, A. (2017). A comparison of two models of dental care for Aboriginal communities in New South Wales. Australian Dental Journal, 62(2), 208-214. [More Information]
  • Cripps, E., Wood, R., Beckmann, N., Lau, J., Beckmann, J., Cripps, S. (2016). Bayesian Analysis of Individual Level Personality Dynamics. Frontiers in Psychology, 7, 1-13. [More Information]
  • Sojo, V., Wood, R., Cripps, S., Wheele, M. (2016). Reporting requirements, targets, and quotas for women in leadership. The Leadership Quarterly, 27(3), 519-536. [More Information]
  • Rosen, O., Wood, S., Stoffer, D. (2012). AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series. Journal of the American Statistical Association, 107(500), 1575-1589. [More Information]
  • Wood, S., Rosen, O., Kohn, R. (2011). Bayesian Mixtures of Autoregressive Models. Journal of Computational and Graphical Statistics, 20(1), 174-195. [More Information]
  • Rosen, O., Stoffer, D., Wood, S. (2009). Local Spectral Analysis via a Bayesian Mixture of Smoothing Splines. Journal of the American Statistical Association, 104(485), 249-262. [More Information]

Conferences

  • Chandra, R., Azizi, L., Cripps, S. (2017). Bayesian neural learning via langevin dynamics for chaotic time series prediction. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
  • Saavedra, A., Cripps, S., Geoghegan, J., Holmes, E., Durrant-Whyte, H. (2015). Modelling the spread of influenza in Western Australia. First International Workshop on Population Informatics for Big Data (ACM-SIGKDD PopInfo'15), Sydney: anu.edu.au.

2018

  • Marchant Matus, R., Haan, S., Clancey, G., Cripps, S. (2018). Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression. Security Informatics, 7(1). [More Information]
  • Chandra, R., Cripps, S. (2018). Coevolutionary multi-task learning for feature-based modular pattern classification. Neurocomputing, 319, 164-175. [More Information]

2017

  • Gwynne, K., McCowen, D., Cripps, S., Lincoln, M., Irving, M., Blinkhorn, A. (2017). A comparison of two models of dental care for Aboriginal communities in New South Wales. Australian Dental Journal, 62(2), 208-214. [More Information]
  • Chandra, R., Azizi, L., Cripps, S. (2017). Bayesian neural learning via langevin dynamics for chaotic time series prediction. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]

2016

  • Cripps, E., Wood, R., Beckmann, N., Lau, J., Beckmann, J., Cripps, S. (2016). Bayesian Analysis of Individual Level Personality Dynamics. Frontiers in Psychology, 7, 1-13. [More Information]
  • Sojo, V., Wood, R., Cripps, S., Wheele, M. (2016). Reporting requirements, targets, and quotas for women in leadership. The Leadership Quarterly, 27(3), 519-536. [More Information]

2015

  • Saavedra, A., Cripps, S., Geoghegan, J., Holmes, E., Durrant-Whyte, H. (2015). Modelling the spread of influenza in Western Australia. First International Workshop on Population Informatics for Big Data (ACM-SIGKDD PopInfo'15), Sydney: anu.edu.au.

2014

  • Wood, S. (2014). Applications of Bayesian Smoothing Splines. In Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens (Eds.), Bayesian Theory and Applications, (pp. 309-335). Oxford: Oxford University Press.

2012

  • Rosen, O., Wood, S., Stoffer, D. (2012). AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series. Journal of the American Statistical Association, 107(500), 1575-1589. [More Information]

2011

  • Wood, S., Rosen, O., Kohn, R. (2011). Bayesian Mixtures of Autoregressive Models. Journal of Computational and Graphical Statistics, 20(1), 174-195. [More Information]

2009

  • Rosen, O., Stoffer, D., Wood, S. (2009). Local Spectral Analysis via a Bayesian Mixture of Smoothing Splines. Journal of the American Statistical Association, 104(485), 249-262. [More Information]

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