NIASRA runs a variety of events and seminar series open to academics, students and the community to attend.

Upcoming events

There are currently no events for this period

2021 seminars

view the seminar planning calendar

Seminars by date, presenter and topic
Friday, 30 July, 11:30, Zoom

Professor Robert Deardon, University of Calgary

Fast parameterization of spatial epidemic models: let’s emulate
Friday, 2 July, 11:00, Zoom

Senior Professor David Steel, University of Wollongong

Sample design for analysis using high influence probability sampling
Friday, 4 June, 14:00, Zoom

Dr Luca Maestrini, University of Technology Sydney

Variational approximations for random effect and latent variable models
Friday, 21 May, 14:00, Zoom

Dr Leah South, Queensland University of Technology 

Monte Carlo variance reduction using Stein operators
Friday, 14 May, 16:00, Zoom

Consulting statistician Jonathan Rougier 

Estimating volume from point-referenced thickness measurements
Friday, 30 April, 14:00, Zoom

Honorary professorial fellow, Siu-Ming, University of Wollongong

On removing the linkage bias from integrated data sets for analytic inference
Friday, 16 April, 14:00, Zoom

Professor Han Lin Shang, Macquarie University

Bootstrap rediction bands for functional time series
Friday, 9 April, 14:00, Zoom

Dr David Gunawan, University of Wollongong

Posterior probabilities for Lorenz and stochastic dominance of Australian income distributions
Friday, 19 March, 14:00, Zoom

Chris Lisle, University of Wollongong

A new diagnostic to assess information available for variance parameter estimation in Multi-Environment Trial (MET) analyses
Friday, 12 March, 14:00, Zoom

Dr Stephanie Clark, University of Technology Sydney

Multiple time series analysis with unsupervised and supervised machine learning: Groundwater level patterns in the Namoi region of NSW

Annual Statistical Science Lecture

in the School of Mathematics and Applied Statistics

Statistical Science is the science of uncertainty. More specifically, it is the principled collection, analysis, and interpretation of data, taking into account the uncertainties within and between each of these steps. A critical component of excellent science is the ability to weigh evidence appropriately – statistical thinking lies at the heart of this. The annual Statistical Science Lecture showcases the interdisciplinarity and key role a statistical scientist plays in extracting scientific knowledge from data in the presence of uncertainty.

The inaugural Statistical Science Lecture (SSL) was given in 2018 and is an annual event made possible by a philanthropic donation to the School of Mathematics and Applied Statistics (SMAS), University of Wollongong.

The 2020 Statistical Science Lecturer

Sally Cripps
Professor, University of Sydney, Australia

Sally Cripps is a Professor of Mathematics and Statistics and Director of the ARC Centre in Data Analytics for Resources and Environments (DARE Centre), at the University of Sydney. Sally’s research focus is the development of new and novel probabilistic models which are motivated by the need to solve an applied problem with the potential for impact. She has particular expertise in the use of mixture models for complex phenomena, modelling longitudinal data, nonparametric regression, the spectral analysis of time series, and the construction of transition kernels in MCMC schemes that efficiently explore posterior distributions of interest. Sally is also Chair of the International Society for Bayesian Analysis’ section, Bayesian Education and Research in Practice.

Statistical Science Lecture given on 18 November 2020:

Zen and the Art of Bayesian Geology/Hydrology/Ecology


    2020 lecture: Sally Cripps

    Noel Cressie, Sally Cripps  

    Professor Cripps with UOW Students

The 2019 Statistical Science Lecturer

Peter J Diggle
Distinguished Professor, Lancaster University and Health Data Research UK

Peter Diggle is a Distinguished University Professor of Statistics in the Centre for Health Informatics, Computing and Statistics, a teaching and research group within the Lancaster Medical School at Lancaster University working at the interface of statistics, epidemiology, and health informatics. Peter is also Director of Training at Health Data Research UK, working with academic institutions across the UK to draw up a training strategy that builds on existing best practice to create a programme that will transform the careers of future leaders in data science on a national scale. He holds adjunct positions at the Johns Hopkins University, Yale and Columbia Universities, and he was President of the Royal Statistical Society between July 2014 and December 2016.

Statistical Science Lecture given on 06 November 2019:

A Tale of Two Parasites: statistical science to support disease control programmes in Africa


    2019 lecture: Peter Diggle  


    Peter Diggle, V-C Paul Wellings, Noel Cressie   

The 2018 Statistical Science Lecturer

Renate Meyer
Professor, The University of Auckland, New Zealand

Renate Meyer is a Professor in the Department of Statistics at The University of Auckland, with research interests in applied Bayesian inference and MCMC methods. In particular, her research areas comprise time series analysis with applications in astrophysics (gravitational waves), state-space modelling in ecology, multivariate modelling using copulas, survival analysis in medical statistics, and stochastic volatility models for financial time series.

Statistical Science Lecture given on 31 October 2018:

Surfing Gravitational Waves: Black holes and Bayesian nonparametrics


    Noel Cressie, Renate Meyer 

    SMAS morning tea with Renate Meyer

Fellows Research Meetings

The NIASRA Fellows Meetings aim to provide an ongoing opportunity for researchers actively working in areas of interest to the Centre to present both work in progress and recent research results to an audience of fellow researchers and senior professionals who are also working in these areas.

Find out more