Advances in technology and the availability of data-acquisition devices have increasingly centralised the role of the data analytics in the earth sciences, which in turn inform data driven decision making across science, industry and government. Still, empirical based decision making continues to be made under conditions of uncertainty: data is messy; statistical model selection/estimation is complex; underlying physics that discretised numeric methods attempt to resolve are mis-specified. This recognition implies that, when the consequences of a decision is substantial, robust uncertainty quantification ought to accompany the fusion of domain knowledge and empirical evidence.

This talk is based on a series of recent papers, providing an overview on: recent applications/methods developed with earth scientists and industry partners for probabilistic models of meteorological, oceanographical and geophysical processes; experiences on conveying to non-statistical colleagues the meaning of uncertainty and its consequences for decision making; deployment of software for private (industry) and public use.

Speaker: Associate Professor Edward Cripps (DARE Deputy Director)

Edward has a Bachelor of Economics from University of Western Australia (UWA) and Honours (First Class) in Statistics from University of NSW where he also completed his PhD at the School of Mathematics and Statistics, UNSW.

Currently, he is an Associate Professor in the Department of Mathematics and Statistics at the University of WA. His research interests are in Bayesian longitudinal analysis and spatio-temporal models, and the integration of statistical and physical models for uncertainty quantification. His primary applications are in the statistical modelling of environmental, meteorological and oceanographic processes and their interaction with engineering decision making and asset management.

In addition to the DARE Centre, Edward has extensive experience in industry collaboration and translating academic research output into commercially industrial applications. He is involved with two other Industrial Transformation Training Centres (ARC ITRH for Offshore Floating Facilities and ARC ITTC Transforming Maintenance through Data Science) researching uncertainty quantification of oceanographic and meteorological processes and the consequences for offshore engineering operations and design on the North West Shelf off the Australian Coast.

Reliability Inference with Extended Sequential Order Statistics by Tim Pesch

In this presentation I will address the complexity of non-identical components in multi-component, load sharing systems. For most technical systems the assumption of heterogeneous components is reasonable since components are either of different type or vary in their functions within the system. While most reliability related work resorts to the assumption of homogeneous components, I aim to address the often more realistic assumption of heterogeneous components extending the model of so called ‘Extended Sequential Order Statistics’ by two novel inferential methods.

Firstly, the derivation of Maximum Likelihood Estimates (MLE’s) of the underpinning model parameters, and secondly, the introduction of a likelihood ratio test which can decide on whether components can be assumed identical. Both methods are powerful tools in reliability contexts. The former increases our understanding of component behaviour, especially upon failure of other components. This knowledge empowers system operators to make better decisions regarding maintenance schedules and failure time prediction. The latter supports operators in their quest of identifying component equivalence.

Speaker: Tim Pesch

Tim is a mathematician with experience in reliability probability estimation. He completed his studies in mathematics with a focus on frequentist statistics at RWTH Aachen University in Germany. His Master thesis featured the combination of two well established models in reliability theory, the Stress-Strength model and the Competing Risk model, in the presence of censored data. His research yielded maximum likelihood-estimators for model parameters as well as the reliability probability under an exponential assumption, amongst other inferential results.

Conveyor Belt Wear Forecasting through a Bayesian Hierarchical Modeling Framework using Functional Data Analysis and Gamma Processes by Ryan Leadbetter

Reliability engineers make critical decisions about when and how to maintain conveyor belts, decisions that can significantly impact the production of the mine. The engineers use thickness measurements across the belt’s width to justify these decisions. However, the current approaches to forecast the wear of the conveyor belts are naive and throw away valuable information about the special wear characteristics of the conveyor. We have developed a new method for forecasting belt wear that retains the wear profile’s spatial structure and considers the wear rate’s heterogeneity – caused by operation and ore body composition variations.

Speaker: Ryan Leadbetter

Ryan is a mechanical engineer who is now undertaking a PhD in applied statistics through the Centre for Transforming Maintenance Through Data Science. Ryan’s PhD focuses on the predictive maintenance of overland iron ore conveyors. More specifically, he focuses on using condition monitoring and maintenance data to inform decisions on how and when to maintain mining machinery.

There has been a lot of recent interest in various computational methods that allow one to extract models of the deterministic evolution operator of a dynamical system from time series data. These methods have become increasingly successful as they are able to leverage increasing computational resource available today. I will start by contrasting these efforts against some earlier attempts to do this (including some of my own) and then move on to describe our recent work with reservoir computers.

Viewed in this setting, reservoir computers are a pattern generator which appear particularly appropriate to the task of reconstructing dynamics as their memory mimics the role of Takens’ theorem in delay reconstruction. I will briefly explore some of these ideas and finish by describing our attempts to quantify the performance of reservoirs and apply them to modelling tasks in industrial settings.

Speaker: Professor Michael Small 

Michael Small is the CSIRO-UWA Chair of Complex Systems and a former Future Fellow. He is the Deputy Editor-In-Chief of the Journal Chaos, and Main Editor of Physica A. He is a Chief Investigator of the ARC Industrial Transformation Training Centre for Transforming Maintenance Through Data Science, the Industrial Transform Research Hub for Transforming energy Infrastructure through Digital Engineering, and the Medical Research Future Fund project for Transforming Indigenous Mental Health and Wellbeing.

When he is not transforming things, his research relates to complex systems, network science, dynamical systems and chaos, and focusses on data-driven approaches to understanding dynamical systems.

Suppose you get a set of (independent) data points in some discrete but huge domain {1,2,…,k}, and want to determine if this data is uniformly distributed. This is a basic and fundamental problem in statistics, and has applications in computer science, not all made up: from testing the mixing time of a random walk, to detecting malicious changes in a data stream, to selecting a good algorithm depending on the input distribution.

The goal, of course, is to perform this task efficiently, both time- (time complexity) and data-wise (sample complexity). In this talk, I will survey and discuss seven algorithms for uniformity testing, and explain some of their advantages and disadvantages.

Speaker: Dr Clément Canonne

Dr Clément Canonne is a Lecturer in the School of Computer Science at the University of Sydney, where he does research in theoretical computer science. His main research interests lie in property testing, learning theory, and, more generally, randomised algorithms and the theory of machine learning.

Prior to joining the University of Sydney, Clément was a Goldstine postdoctoral fellow at IBM Research Almaden, and a Motwani fellow at Stanford University. He obtained his Ph.D. from Columbia University in 2017.

Global climate change has altered precipitation patterns and disrupted the characteristics of drought and rainfall events. This, combined with nutrient and grazing practices in grasslands, will likely expose vegetation to conditions beyond their adaptive capacity, altering biodiversity and productivity, and changing ecosystem function. Knowledge on how grasslands respond to these pressures, and their potential to recover, is needed to maintain essential ecosystem services in the future.

In this talk, I will introduce my PhD research and present some of the findings from my project to date. I will describe how we used a new drought tracking technique to characterise the spatiotemporal dynamics for past drought and rainfall events in Australia. I will also present some preliminary results from our field experiment, including how grassland productivity and diversity respond to extreme precipitation, nutrient addition, and cattle grazing.

Speaker: Elise Verhoeven

Elise is a PhD candidate with the School of Life and Environmental Sciences at The University of Sydney. Elise is interested in how plant communities respond to disturbances, and which plants could be important for maintaining ecosystem function under global change conditions. Her PhD research is looking at the interactive effect of extreme precipitation (drought and rain), nutrient addition, and cattle grazing on the structure, productivity, and ecosystem function in grasslands in north-west NSW.

Tuesday 4th April
4pm – 5pm AEST on Zoom

In this talk, I describe an approach to modelling social networks that has its origins in models for interactive spatial processes, including in plant ecology. The approach construes global network structure as the outcome of dynamic, potentially realisation-dependent processes occurring within local neighbourhoods of a network. I describe a hierarchy of models implied by the approach and note that they can be estimated from partial network data structures obtained through certain types of network sampling schemes. I illustrate how these models enhance our capacity to model observed human networks and present an example of their application to the transmission of an infectious disease.

Speaker: Professor Philippa “Pip” Pattison AO (Chair of DARE Advisory Board)

A quantitative psychologist by background, Professor Pattison began her academic career at the University of Melbourne. She served in a number of academic leadership roles at the University of Melbourne, including president of its Academic Board from 2007-2008 and Deputy Vice-Chancellor (Academic) from 2011-2014 before taking up the role in 2014 of Deputy Vice-Chancellor Education at the University of Sydney. During her term, Pip led the University’s strategy for learning and teaching, with a major focus on transformation of the undergraduate curriculum, the student experience and new approaches to postgraduate education and microcredentials. Pip retired from the role at the end of 2021.

The primary focus of Professor Pattison’s research is the development and application of mathematical and statistical models for social networks and network processes. Applications have included the transmission of infectious diseases, the evolution of the biotechnology industry in Australia, and community recovery following bushfire.

Professor Pattison was elected a Fellow of the Academy of the Social Sciences in Australia in 1995 and of the Royal Society of NSW in 2017.

Professor Pattison was named on the Queen’s Birthday 2015 Honours List as an Officer of the Order of Australia for distinguished service to higher education, particularly through contributions to the study of social network modelling, analysis and theory, and to university leadership and administration.

Tuesday 18th April
4pm – 5pm AEST on Zoom

Seminar details to be confirmed.

Speakers: Associate Professor Kristen Splinter and Patrick ‘Kit’ Calcraft

Tuesday 2nd May
4pm – 5pm AEST at UNSW and on Zoom

Seminar details to be confirmed.

Speaker: Dr Sahani Pathiraja (DARE Chief Investigator)

Sahani Pathiraja is a Lecturer (tenure track assistant professor) in Data Science at the University of New South Wales (UNSW) in Sydney. Sahani received her double Bachelor of Science (mathematics) and Engineering (environmental) in 2011 with Hons (1st Class) and the University Medal, and her PhD in Civil and Environmental Engineering in 2018, all from the University of New South Wales (UNSW Sydney). Her dissertation topic was on improved data assimilation methods for hydrologic applications.

From 2017-2022 she was a postdoctoral researcher in the Institute of Mathematics at the University of Potsdam, Germany as part of the Collaborative Research Centre on Data Assimilation. She worked primarily on the theoretical analysis of modern sequential Monte Carlo methods as well as on new applications of data assimilation in biomedical modelling.

Sahani’s technical expertise spans both the mathematical theory and applications of data science methods, especially in hydrology. Her research is motivated by 1) how applications can inspire new theory and 2) how theory be developed in a more practically relevant way. Specifically, her research primarily focuses on Bayesian inference, Monte Carlo methods, stochastic analysis of data assimilation methods and uncertainty quantification.

Tuesday 16th May
4pm – 5pm AEST on Zoom