Events & seminars

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 SMAS 2021 Statistical Science Lecture will be held on Wednesday 3 November 2021

Time: 10.45am for an 11am start, followed by refreshments, with attendance on campus (subject to COVID-19 restrictions)
Location: UOW Building 43, Room G01. Also, delivery by Zoom
Register: register at eventbrite

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

Title:
Saving Medical Dollars, Trillions at a Time: A Statistical Perspective

Abstract:
American Medicine wastes $1.2 trillion per year, 5% of GDP, and produces noncompetitive health outcomes. A substantial fraction of this waste is attributable to poor use of information in clinical decisions.  This talk addresses a statistical framework that is essential infrastructure to learn from past experience how to improve care for future patients at more affordable costs. We frame four core medical questions in statistical terms, then offer a Bayesian hierarchical modeling approach that integrates complex data with prior biomedical knowledge to address the questions.  This approach is starting to be implemented in academic health centers. We use two recent examples from autoimmune disease and Covid-19 clinics to illustrate the methods, identify current obstacles to success, and point out opportunities for data scientists to directly impact future healthcare decisions.

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. Professor Zeger has served as expert witness to the U.S. Department of Justice and several states in their civil suits against the tobacco industry and as a member of the Board of Scientific Advisors for the Merck Research Laboratory. He is a member of the Springer-Verlag editorial board for Statistics and was the founding co-editor of the Oxford University Press journal, Biostatistics. 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.

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

• Slides
• Video

    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

• Slides
• Video

    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

• Slides
• Paper

    Noel Cressie, Renate Meyer 

    SMAS morning tea with Renate Meyer