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 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

• Slides
• Video

_{2022 Statistical Science Lecturer Michael I. Jordan}

_{2022 Statistical Science Lecture highlight 1: "Three Foundational Disciplines"}

_{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

• Slides
• Video

_{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

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} 

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

• 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}