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.

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.

Coastal science and engineering is a relatively young field and historically has lacked sufficient data to be able to understand how this complex earth system works at both large temporal and spatial scales. Yet, with a large portion of the world’s population living within 50km of the coastline, we are being asked to provide advice and understanding on how coastlines will change into the future.

This talk will first provide a bit of context on just how data sparse our field is, and how we are now engaging and rapidly trying to catch up to our hydrological colleagues. We will discuss how we are applying basic machine learning techniques to improve our ability to predict coastal change at a variety of timescales of interest to the public, from individual storms, to where the coast might be by 2100.

The talk will be aimed at a broadscale (non-expert) audience, discussing the challenges associated with trying to model the coastline, and the techniques we have so far applied, and we’d love thoughts and ideas from the audience as well.

Speaker: Associate Professor Kristen Splinter

Kristen is an ARC Future Fellow and Deputy Director of the Water Research Laboratory at UNSW Sydney. Her work encompasses a wide range of coastal topics examining sandy beach evolution from storms to multiple decades. She has developed a number of behavioural type numerical models to predict sandbar and shoreline evolution and the focus of her Fellowship will be to develop regional scale models for long-term shoreline prediction, along the embayed coastlines of NSW. She’s been dipping her toes into machine learning since about 2015 but her students are the real experts.

Speaker: Patrick ‘Kit’ Calcraft

Kit is a DARE affiliated PhD candidate in his first year working on machine learning methods for shoreline prediction, including bridging the gap between physics and ML. He is co-supervised by Associate Professor Kristen Splinter, Dr Josh Simmons (DARE) and Professor Lucy Marshall. He will present an overview of what he’s been up to in year 1 of his PhD.

Ensemble Kalman type methods have seen an explosion in use in data assimilation applications and more recently for a range of learning tasks. Despite their desirable stability properties, they are not consistent with Bayes theorem for non-linear, non-Gaussian systems.

Recently, a range of controlled particle filters have been proposed which aim to emulate the structure of Ensemble Kalman type methods whilst simultaneously providing consistent samples in the asymptotic limit. More specifically, such filters involve constructing a control law to steer particles such that the corresponding probability distribution satisfies a variational Bayes formula.

I will provide an overview of this new class of filters and how they can be used for nonlinear ensemble data assimilation and Bayesian inverse problems. A framework which allows to derive these filters will be explored, which will also highlight the main differences among them.

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.

The Bureau of Meteorology provides a range of water information products and forecast services to the Australian community. While the Bureau has been providing a flood forecast and warning service for several decades, new water forecasting services have been developed and brought into production over the last 15 years. These forecast services can be categorised as either nation-wide (grid-based) or targeting specific locations (point-based) and cover different temporal scales. This seminar will begin by providing an overview of these forecasting services, with an emphasis on forecasts at the seasonal timescale.

Water forecasting services encompass the Australian Water Outlook (AWO) and seasonal streamflow forecast (SSF) service. The AWO provides historical analysis, seasonal forecasts and decadal projections of key variables of the surface water balance: root-zone soil-moisture, runoff and actual evapotranspiration. AWO is underpinned by the Australian Water Resource Model (AWRA-L), run at a daily time-step and at a 5km resolution. The seasonal streamflow forecast (SSF) service provides point-based seasonal forecasts of river discharge at 341 point-locations across Australia, coincident with selected river gauging stations and major water storages.

The Bureau has embarked on a 10-year research plan focused on Earth System Modelling. A unified modelling system supporting all forecast products and services will drive efficiency gains, improve product consistency and remove the maintenance burden of disparate systems now in operation. The final part of this seminar will cover a scientific evaluation to unite both the AWO and SSF service. This unification has been achieved by applying statistical post-processing to AWO seasonal forecasts to generate seasonal streamflow forecasts.

Speaker: Dr Christopher Pickett-Heaps

Dr Christopher Pickett-Heaps is a hydrologist at the Bureau of Meteorology and is a member of the Hydrological Applications team in the Science and Innovation Group of the Bureau. Christopher has been with the Bureau since 2013. His primary role is a hydrological modeller, working to extend the capability of current water forecasting models and systems. Currently Christopher is the scientific lead of a project to integrate seasonal streamflow forecasting with seasonal landscape forecasting. Prior to this, Christopher worked on the Australian Water Outlook. Christopher has also contributed to the development of operational systems underpinning different water forecasting services.

Christopher was awarded a PhD from the University of Melbourne in earth-system modelling after studying in both Australia and France. Christopher then continued working in France before moving to Boston for two years as a post-doctoral fellow at Harvard University. Christopher returned to Australia in 2010 to take a 3-year position at The CSIRO before joining the Bureau. Christopher is based in Canberra.

Fabian Leal: Incorporating Deep Learning Into Statistical Models for Spatial Interpolation: Applications in Mineral Resource Estimation

Fabian is a DARE PhD student at the University of Western Australia.

He completed a Masters degree in Operations Research at the University of Chile, where his research consisted of applying mathematical programming to spatial conservation planning (SCP), a branch of ecology traditionally focused on strategically selecting the best areas for biodiversity conservation in a territory that, in recent years, has started to also take into consideration ecosystem services. Fabian formulated the first mixed integer programming (MIP) model capable of incorporating these additional variables, and applied it to the Daly River, in Australia, besting state-of-the-art heuristic Marxan with Zones’ results.

Throughout his career, he has worked as a data analytics consultant in the workforce management B2B firm SCM LATAM, applying his knowledge in statistics and data science to solve the company’s business problems and complement its services.

Fabian wishes to resume applying his mathematical background to resources and environments, and has joined DARE to continue learning data science techniques to do so. His current focus is on developing machine learning methods and statistical approaches that are capable of capturing uncertainty and ambiguity in data and models to improve decision making in the mining sector.

Yiyi Ma: A simulation study of the feedback filter with diffusion-map-based gain

Yiyi is a DARE PhD candidate at the University of New South Wales.

Yiyi graduated from Imperial College London with a BSc degree in Mathematics in 2020 and an MSc degree in Statistics (Data Science) in 2021. Her interests in applied and computational statistics have grown over the past four years. She explored different statistical models and machine learning methods through various student statistical projects in fields, including politics, finance, cyber security, etc. Currently, she is focusing on Bayesian approaches for inference and data assimilation, especially the techniques for nonlinear/non-Gaussian state-space models., as well as modelling the error for physical process-based systems using machine learning methods.

Samuel Davis: Bayesian Inference of Gaussian Plume Models with Hyperparameter Optimisation

Sam’s research focus is on cloud brightening technologies, utilising Bayesian Statistics and Applied Mathematics techniques to model plumes of sea water droplets. He has joined DARE specifically to learn more about data analytical techniques to help his research.

Sam completed his integrated Masters degree at the University of Manchester in 2019, where he studied for a dual Honours in Mathematics and Physics. During the final year of his degree, he worked on two Masters projects, one in Mathematics and one in Physics, titled “Solving the Helmholtz Equation Using Finite Element Methods” and “Modelling Chaotic Magnetic Fields in Plasma” respectively. Both projects involved applied Mathematics and Data Science techniques.

In addition to his studies, Sam has worked for the European Space Agency, Satellite Applications Catapult and as a Software Developer for Apadmi Ltd.

Outside of academia, Sam is a keen rock climber and surfer. In August 2020, he spent a month on expedition in Kyrgyzstan where he was part of a team who made the first ascent of a 4000m peak. He is keen to have some more adventures and start a new chapter in Australia.

Arpit Kapoor: Hybridization of process-based hydrological models

Arpit is a DARE PhD candidate at the School of Mathematics and Statistics, University of New South Wales, focusing on Bayesian Deep Learning for Spatio-temporal modelling in hydrological and environmental processes.

He has worked in multiple industries solving business-critical data intelligence and computer vision problems through machine learning and data science. He received his undergraduate degree in Computer Science and Engineering in 2019. During this period, he was the team leader of the humanoid robotics research lab at the University.

Arpit’s recent research has focused on Bayesian Neural Networks, evolutionary and tempered-MCMC methods and their applications in time-series modelling and pattern classification problems. Arpit wants to use his data science, mathematics and software development skills to solve advanced problems in earth and environmental science.

Samudra Madushani: Improving Closed-loop Supply Chain Network with Sustainable Objectives

Samudra graduated from the University of Peradeniya, Sri Lanka with First Class Honours from a B.Sc. in Statistics and Operations Research. She also completed her M.Sc. in Industrial Mathematics in 2021 at the University of Sri Jayewardenepura, Sri Lanka with a GPA of 3.93/4.00. Her Master’s thesis involved forecasting dengue incidence in Sri Lanka using the spatial and temporal hierarchical structure of dengue incidence. As a DARE PhD candidate, her research interest is on time series analysis, Bayesian Inference, hierarchical modeling, and data visualisation.

Sam Mason: Spatio-temporal Species Distribution Models

Sam is a DARE PhD candidate at UNSW having worked in the Australian technology industry for over two decades in software engineering and data science. Over the last five years he has worked professionally at Nearmap – an Australian aerial imagery company which started his interest in geographic information systems and geospatial statistics.

As part of his academic journey in statistics, his master’s thesis in 2020 focused on tree canopy research using Nearmap’s visible spectrum imagery and AI datasets. This triggered an interest in ecology and environmental science and he is now part of the Eco-Stats Research Group at UNSW.

His PhD centres on using and developing robust spatio-temporal species distribution models to assess the impact of climate change on species diversity and distributions. In particular, how we can do this in the face of increasingly available (but noisy) environmental and geographic predictors, combined with the large amounts of (opportunistic) observation data coming from the rise of citizen science as well as systematic surveys.

Maria Lopes: Quantifying the interconnections between subterranean fauna habitats and geological features

Maria is a DARE PhD candidate at the University of Western Australia.

Maria Clara Lopes Paula has a Master’s degree in Applied Geophysics (2019) and a Bachelor’s degree in Geophysics (2016) from the University of Brasília, Brazil. From 2015, she has been using geophysical methods to mitigate deforestation in Brazilian biomes, especially in those areas of artisanal and small-scale mining. In 2018, Maria started to apply statistical methods to assess their uses in geophysical interpretation. Her work at DARE will focus on environmental area protection using non-invasive techniques.

El Niño-Southern Oscillation (ENSO) strongly influences the regional climate in Australia via atmospheric teleconnections (Sharmila and Hendon 2020). The warm (El Niño) and cold (La Niña) phases of ENSO typically span over 9-12 months and tend to recur every 2-7 years. On occasions, El Niño and La Niña events can persist beyond a year, exacerbating their length of impacts on regional climate at multiyear timescales. Given their potential link to prolonged droughts and widespread flooding followed by extensive socioeconomic impacts, skilful prediction of ENSO events beyond a year is thus crucial.

Despite the growing demand for likelihood of ENSO events in the following year, quantifying the prediction skill of ENSO beyond a year remains a major challenge, partly due to lack of long records of seasonal reforecasts. As part of the Northern Australia Climate Program (NACP), we recently assessed the predictability and prediction skill of ENSO using 24-month long reforecasts of 10 ensemble members from the ECMWF coupled model (SEAS5-20C; Weisheimer et a. 2022) initialised on 1st November and 1st May for the period 1901-2010. Our study shows that ENSO can be skilfully predicted up to 18 lead-months but the skill beyond the first year is conditioned to the ENSO phase (Sharmila et al. 2023a). We further access the capability of ECMWF model in predicting single and multi-year ENSO events and show that the model can predict the duration of back-to-back El Niño events and the first 18-months of multiyear La Niña from strong El Niña initialised state (Sharmila et al. 2023b). The model could also predict the differences in the regional impacts from 1-yr and multiyear ENSO events reasonably well. This foundational research outcomes will be valuable for the future strategy of extending Bureau’s operational ENSO outlooks beyond a season.

Speaker: Dr Sharmila Sur

Sharmila is a Research Scientist at the Bureau of Meteorology within the Hydrological Applications team and based in Melbourne. She has 13+ years of theoretical & modelling research experience in Australia and India, particularly on tropical weather and climate variability, their predictability & prediction and climate change. She has published over 23 research papers in top-ranking journals with 1100+ citations. Sharmila has been at the Bureau since 2018 and led the research on assessing potential for multiyear climate prediction in Australia as part of Northern Australia Climate Program. Currently, Sharmila is the scientific and technical lead of compound event research and applications.

Sharmila completed her doctoral research on Indian monsoon variability and dynamical prediction from Indian Institute of Tropical Meteorology and received her Ph.D. in Atmospheric & Space Science from Savitribai Phule Pune University, India in 2015. She then moved to Australia to pursue her postdoctoral research on tropical cyclone at the University of Melbourne (2015-2018). Sharmila is also an adjunct Research Fellow at the University of Southern Queensland and a member of UN WMO expert network.

Estimating future climate change and its uncertainties relies on the analysis of a range of global climate models (GCMs) and the assessment of their spread. To meet the spatial scales required to study the local impacts of climate change, GCMs are downscaled dynamically using regional climate models or empirically using statistical methods and machine learning techniques. Due to the high computational cost involved in dynamical downscaling (DD), only a few GCMs are considered in this approach, resulting in a limited range of predictions that might not be sufficient to accurately assess the uncertainty in the predicted changes. Statistical methods and machine learning, on the other hand, perform downscaling at a much lower cost, but can perform poorly when extrapolated to future climates.

We introduce a hybrid downscaling framework that leverages the merits of dynamical downscaling and machine learning while overcoming the limitations of a single approach. In the new framework, a machine learning model is developed for each coarse grid cell to predict the subgrid distribution of the variable of interest as a function of the local climate and subgrid land surface characteristics. The fine-scale data needed for training ML is sourced from dynamically downscaling 10 representative years from the entire distribution of the coarse data.

As a proof of concept, we apply the new framework to downscale daily Evapotranspiration from the Australian BARRA-R reanalysis dataset over Sydney from 12.5km down to 1.5km. We employ three machine learning algorithms and demonstrate their performance. We also explore spatial transitivity, i.e. the capability of the trained ML models to downscale regions outside the spatial domain they were trained in, and we demonstrate when it is effective.

In the proposed framework, multiple GCMs can be downscaled for the same cost as downscaling a single GCM. Ultimately, this should improve our ability to analyse future changes in local climate, and provide more robust information for impacts adaptation planning.

Speaker: Dr Sanaa Hobeichi

Sanaa Hobeichi is a post-doctoral researcher at the University of New South Wales (UNSW) Climate Change Research Centre and the ARC Centre of Excellence for Climate Extremes (CLEX). Her research spans climate science and machine learning, focusing on developing machine learning methods for downscaling climate data and improving drought predictions. She is also interested in explainable machine learning and physics-informed machine learning.

Sanaa is passionate about advancing climate science education for secondary school students. By leading the Climate Classrooms workshops for teachers, she facilitates the development of teaching resources that effectively incorporate climate science research into the Australian Curriculum.

Sanaa obtained her PhD in Climate Science from UNSW, and she holds a BSc in Computer Science and Applied Mathematics from the Lebanese University, and a MSc in Environmental Remote Sensing from Qatar University.

Numerical models of groundwater flow play a critical role for water management scenarios under climate extremes. Large‑scale models play a key role in determining long range flow pathways from continental interiors to the oceans, yet struggle to simulate the local flow patterns offered by small‑scale models. We have developed a highly scalable numerical framework to model continental groundwater flow which capture the intricate flow pathways between deep aquifers and the near surface. The coupled thermal‑hydraulic basin structure is inferred from hydraulic head measurements, recharge estimates from geochemical proxies, and borehole temperature data using a Bayesian framework. We use it to model the deep groundwater flow beneath the Sydney–Gunnedah–Bowen Basin, part of Australia’s largest aquifer system. Coastal aquifers have flow rates of up to 0.3 m/ day, and a corresponding groundwater residence time of just 2,000 years.

In contrast, our model predicts slow flow rates of 0.005 m/day for inland aquifers, resulting in a groundwater residence time of ∼ 400,000 years. Perturbing the model to account for a drop in borehole water levels since 2000, we find that lengthened inland flow pathways depart significantly from pre‑2000 streamlines as groundwater is drawn further from recharge zones in a drying climate. Our results illustrate that progressively increasing water extraction from inland aquifers may permanently alter long‑range flow pathways. Our open‑source modelling approach can be extended to any basin and may help inform policies on the sustainable management of groundwater.

Speaker: Dr Ben Mather

Dr. Ben Mather is a research fellow in the EarthByte Group within the School of Geosciences at The University of Sydney. He is an expert in fusing multi-disciplinary datasets with Earth evolution models to understand the occurrence of enigmatic volcanoes. Related research interests include the cycling of volatiles within the Earth, probabilistic thermal models of the lithosphere to unravel past tectonic and climatic events, and the response of groundwater flow pathways to tectonic forces.

A firm supporter of open-source software, Dr. Mather develops computational methods and tools that adhere to Findable, Accessible, Interoperable and Reusable (FAIR) standards and which are hosted in public repositories. He is a vocal advocate for the integral role of geoscience in responding to challenges we face in transitioning to the carbon-neutral economy. Dr. Mather has been interviewed in national and international print media, TV, and radio on a wide variety of subjects including earthquakes, volcanoes, groundwater, and critical minerals.

GitHub: github.com/brmather
Twitter: @BenRMather

To support the launch of the NSW State Vegetation Type Map the NSW Department of Planning, Industry and Environment released a mobile app that allows anyone to perform complex spatial queries on Plant Community Types. It works just like Google Maps but for trees. We call it Trees Near Me NSW and it recently won an international design award. It is giving DPE a new way to engage directly with our customers by democratising spatial analysis. It is beginning to change how DPE interacts with their customers.

The NSW Wildlife Drone Hub, or Drone Hub, was launched in February of 2022 and funded by the NSW Digital Restart Fund. It is committed to giving New South Wales a drone capability for biodiversity monitoring. Since its inception the Drone Hub has trained 55 ecologists to fly drones and collect scientific data using thermal sensors and object detection models. In the last 12 months Drone Hub pilots have conducted over 700 surveys for partners in universities, government and industry across NSW.

Speaker: Dr Adam Roff

Dr Adam Roff is a Senior Research Scientist in the Vegetation and Biodiversity Mapping team of the Science, Economics, and Insights Division at DPE.

Adam’s speciality is bringing technological innovation to ecology. He works closely with ecologists to seek a deep understanding of their requirements and then design innovative solutions that increase their productivity. His background is in machine vision, machine learning and remote sensing.

The mining industry is currently going through a significant phase of digital transformation to try and meet the rising global demand for minerals. As part of this digitalisation and modernisation, mining companies are collecting larger volumes of more complex data than ever before. Technologies that can assist in turning data into information and insights are key to prevent geoscientists from drowning in their new sea of data.

In this talk, we will explore how recent advances in computer vision – specifically in the field of deep learning – have provided algorithms and workflows that have the ability to efficiently augment and automate the many observational tasks in geoscience such as drill core logging. Several case studies will be presented to demonstrate how these models are trained and deployed to solve challenging geoscience problems.

Speaker: Brenton Crawford

Brenton Crawford is a geologist, data scientist, entrepreneur and mining technology enthusiast. He studied geology and geophysics at Monash University and began his career in consulting working for PGN Geoscience in a number of geological and geophysical roles in both exploration and mining. Brenton has also worked as a geophysicist and data scientist for MMG Exploration working in nickel, copper and zinc exploration and project generation in Australia, Africa and South America.

In 2015, Brenton co-founded Solve Geosolutions – Australia’s first exploration and mining focused data science consultancy which has since been acquired. In 2018, Brenton co-founded Datarock – a computer vision technology company geared at building productionised image and video analysis solutions for exploration and mining where he has served as both its Head of Business Development and Chief Operating Officer. Brenton currently serves as Datarock‘s Chief Geoscientist and Technologist.

Advances in Big Data make it possible to make short-term forecasts for market trends from previously unexplored sources. Trading strategies were recently developed by exploiting a link between the online search activity of certain terms semantically related to finance and market movements. Here we build on these earlier results by exploring a data-driven strategy which adaptively leverages the Google Correlate service and automatically chooses a new set of search terms for every trading decision.

In a backtesting experiment run from 2008 to 2017 we obtained a 499% cumulative return which compares favourably with benchmark strategies. A crowdsourcing exercise reveals that the term selection process preferentially selects highly specific terms semantically related to finance (e.g. Wells Fargo Bank), which may capture the transient interests of investors, but at the cost of a shorter span of validity. The adaptive strategy quickly updates the set of search terms when a better combination is found, leading to more consistent predictability. We anticipate that this adaptive decision framework can be of value not only for financial applications, but also in other areas of computational social science, where linkages between facets of collective human behaviour and online searches can be inferred from digital footprint data.

Xu Zhong and Michael Raghib

Speaker: Dr Michael Raghib

Michael Raghib, PhD, is a Principal Data Scientist & Manager in the AI & Analytics Practice at IBM Consulting based in Perth, WA. He has over 20 years of research and consulting experience in the application of mathematics and computational science to industrial and scientific problems. He earned a Civil Engineer degree in Colombia, followed by a PhD in Applied Maths from The University of Glasgow. He spent several years as a postdoctoral researcher at Princeton University and Los Alamos National Labs, where he focused on multiscale modelling for complex systems. He then worked as an external consultant in computational science for the exploration group of Ecopetrol in Colombia. After this, he joined IBM Research in Rio de Janeiro where he focused in AI for Oil & Gas.

Michael moved to IBM Research Australia in 2016 and is now a Principal Data Scientist for the AI & Analytics practice for IBM Consulting. He has published a number of scientific articles and patents. His current focus includes AI and advanced analytics offerings for energy, resources and utilities.

Tuesday 3rd October
4pm – 5pm AEDT on Zoom

Seminar details to be confirmed.

Speaker: Professor Sally Cripps

Sally Cripps is an internationally recognized scholar and leader in Bayesian Machine Learning (ML) and Artificial Intelligence (AI). In addition to her role as Director of Technology at the Human Technology Institute she is a Professor of Mathematics and Statistics at the University of Technology Sydney. Sally has held a number of leadership positions in ML and AI. She was cofounder and co-director of the University of Sydney’s Centre for Translation Data Science (CTDS), she was founder and Director of the Australian Research Council’s Industrial Transformation Training Centre (ARC ITTC) Data Analytics for Resources and Environments (DARE). Most recently Sally was Research Director of Analytics and Decision Science and Science Director of the Next Gen AI Training Programme in CSIRO’s Data61. She was also chair of the International Bayesian Society for Bayesian Analysis (ISBA) section on Education and Research in practice. She has served as a board member for Climate Services for Agriculture in the Department of Water and the Environment and as a member of the Data Analytics Centre of NSW Health and Human Services Expert Working Group and the NSW Smart Cities Research & Academic Working Group.

Sally’s research focuses on the development of new foundational methods in AI to address global challenges. Her work has been published in the world’s most prestigious statistical and machine learning journals such as, The Journal of the Royal Statistical Society, and the Journal of the American Statistical Association; Theory and methods, (JASA), Biometrika and Journal of Computational and Graphical Statistics (JCGS), Conference on Neural Information Processing Systems (NeurIPS) and Conference on Artificial Intelligence and Statistics (AIStats). She has applied these methods to a diverse range of fields including social disadvantage, mental health, climate, minerals and the environment. In recognition of the quality of her research Sally was awarded an ARC Future Fellowship and a visiting scholar fellowship to the Alan Turing Institute in the UK. Sally has attracted over $25M in industry, government and philanthropic funding.

Tuesday 17th October
4pm – 5pm AEDT

Seminar details to be confirmed.

Speaker: Professor Gill Dobbie

At school, I excelled in Sciences and discovered my love of Computer Science. I went on to study Applied Engineering at undergraduate and postgraduate level, with a focus on computing and process control. After my undergraduate degree I spent a couple of years in industry working for PEC, programming in assembly language. My PhD research addressed theoretical aspects of database systems. I was one of the first women to complete a PhD in Computer Science at the University of Melbourne.

My current research focuses on machine learning, in particular data stream mining and adversarial attacks. My research group creates algorithms that can be used in various application areas, such as predicting peaks and troughs in COVID-19 cases, predicting dementia using routinely collected data, monitoring critical and/or remote sensors, and detecting and defending against various adversarial attacks.

Tuesday 31st October
4pm – 5pm AEDT on Zoom