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

Upcoming events

Upcoming seminars

view the seminar planning calendar


Seminars by date, presenter and topic
Thursday, 17 November, 10:45 for 11:00 start, Building 43, Room G01 and Zoom. Michael I. Jordan, UC Berkeley

On Learning-Aware Mechanism Design

Friday, 11 November, 14:00, Building 8 Room G24 Professor Stephen Haslett, Massey University, New Zealand

Equality of BLUEs for Full, Small, and Intermediate Linear Models under Covariance Change, with links to Data Confidentiality and Encryption

Friday, 28 October, 14:00, Building 3 Room 122 and Zoom Professor Maurizio Filippone, Chair of Computational Statistics at EURECOM, France

Functional Priors for Bayesian Deep Learning

Thursday, 27 October, 11:30, Zoom (A Joint NIASRA and DDSN seminar) Associate Professor Jenny Fisher, University of Wollongong

Teaching Programming & Modelling to First-Year Undergraduates: Tips and Lessons Learned

Friday, 21 October, 14:00, Zoom Dr Mohamad Khaled, University of Queensland

Rank-based conditional measures of inequality under complex sampling designs (joint with Paul Makdissi and Myra Yazbeck, University of Ottawa)

Friday, 14 October, 13:30, Building 20 Room 5 and Zoom Dr Michael Bertolacci, Centre for Environmental Informatics, NIASRA, University of Wollongong

A shifting carbon cycle: Hunting for changes in ecosystems' emission and absorption of CO2

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

Past seminars

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 2022 Statistical Science Lecturer

Michael I. Jordan
Pehong Chen Distinguished Professor in Dept of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley, USA

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Master’s in Mathematics from Arizona State University and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive, biological, and social sciences. In 2016, Prof. Jordan was named the "most influential computer scientist" worldwide in an article in Science, based on rankings from the Semantic Scholar search engine. Prof. Jordan is a Member of the National Academy of Sciences, the National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science, a Foreign Member of the Royal Society, UK, and a Fellow of a broad range of societies. He received the World Laureates Association Prize (2022), the Ulf Grenander Prize from the American Mathematical Society (2021), the IEEE John von Neumann Medal (2020), the IJCAI Research Excellence Award (2016), the David E. Rumelhart Prize for contributions to human cognition (2015), and the ACM/AAAI Allen Newell Award (2009).

Statistical Science Lecture given on 17 November 2022:

On Learning-Aware Mechanism Design

Michael I. Jordan profile photo
2022 Statistical Science Lecturer Michael I. Jordan

Three Foundational Disciplines
2022 Statistical Science Lecture highlight 1: "Three Foundational Disciplines"

music in the data age
2022 Statistical Science Lecture highlight 2: "Music in the Data Age"

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,
Baltimore, USA

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

Scott L. Zeger, John C. Malone Professor of Biostatistics and Medicine, gives 2021 Statistical Science Lecture
2021 lecture: Scott L. Zeger

2021 Statistical Science Lecture organisers: Karin Karr, Noel Cressie, Yi Cao (from left to right)
2021 Statistical Science Lecture organisers: Karin Karr, Noel Cressie, Yi Cao (from left to right)

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 

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

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