NIASRA runs a variety of events and seminar series open to academics, students and the community to attend.
Events & seminars
|Friday, 16 September, 14:00||Dr Pavel Krupskiy, University of Melbourne||
Modeling Spatial Tail Dependence with Cauchy Convolution Processes
|Friday, 2 September, 14:00||Dr Pauline O’Shaughnessy, University of Wollongong||
SSP Report and a talk on the applications of Multivariate Moment-Based Density Estimation
|Friday, 26 August, 14:00||Professor Sefa Awaworyi Churchill, School of Economics, Finance, and Marketing, RMIT University, Australia||
Local Area Crime and Energy Poverty
|Friday, 12 August, 14:00 (reschedule)||Dr Lauren Kennedy, Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia||
Combining multiple survey frames with multilevel regression and poststratification (MRP)
|Friday, 6 May, 14:00||Mo Namazi, TPG Telecom||AI/ML-driven automation systems and Data Products in Telecommunications Industry|
|Friday, 29 April, 10:00||Professor Ben Marwick, University of Washington||Transparency and reproducibility for quantitative research: Like the Layers of an Onion|
|Friday, 1 April, 14:00||Professor Eric Beh, Honorary Professor of Statistics with the School of Information and Physical Science, Honorary Professorial Fellow with NIASRA and Extraordinary Professor of Statistics at Stellenbosch University, South Africa||On the biplot for contingency tables with ordinal variables: features of the configuration obtained using orthogonal polynomials generated from Emerson’s recurrence formulae|
|Friday, 4 March, 14:00||Dr Matt Moore, University of Wollongong||The Annealed Leap-Point MCMC Sampler (ALPS) for multi-modal posterior distributions|
Annual Statistical Science Lecture
in the School of Mathematics and Applied Statistics
- The Statistical Science Lecture (SSL) began in 2018 and is an annual event made possible by a philanthropic donation to the School of Mathematics and Applied Statistics, University of Wollongong.
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 2021 Statistical Science Lecturer
Scott L. Zeger
John C. Malone Professor of Biostatistics and Medicine
The Johns Hopkins University Bloomberg School of
Public Health and School of Medicine,
Scott L. Zeger is The Johns Hopkins University’s co-Director of Hopkins in Health, the Johns Hopkins precision medicine partnership of the University, Health System, and Applied Physics Laboratory. He conducts statistical research on regression analysis for correlated responses and on methods for precision medicine. He has made substantive contributions to our understanding of the effects on health of smoking and air pollution, the global etiology of children’s pneumonia, and other topics. Dr. Zeger’s work has been recognized with several awards including most recently an honorary doctorate from Lancaster University in England and the 2015 Karl Pearson Prize from the International Statistical Institute with Kung-Yee Liang for their development of Generalized Estimating Equations (GEE). Dr. Zeger is most proud of his Golden Apple Awards from the Johns Hopkins Bloomberg School Student Assembly, for excellence in teaching.
Statistical Science Lecture given on 3 November 2021:
Saving Medical Dollars, Trillions at a Time: A Statistical Perspective
2021 lecture: Scott L. Zeger
2021 Statistical Science Lecture organisers: Karin Karr, Noel Cressie, Yi Cao (from left to right)
The 2020 Statistical Science Lecturer
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
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 6 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
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