Welcome: DARE Director, Professor Willem Vervoort (University of Sydney)

Willem has a PhD in field hydrology from the University of Georgia in the US and Agricultural Engineering undergraduate degree from Wageningen University. He is the leading hydrologist at The University of Sydney and an expert in quantitative Hydrology and Catchment Management and simulation modelling. His main research focus is on sustainable water management to balance climate and human impacts. He has a specific interest in agricultural management and impacts.

Willem combines remote sensing, field data and simulation modelling to develop quantitative tools scaling from the field to the continent. Current projects include Bayesian model optimisation, multi-objective model optimisation, understanding model uncertainty in relation to observed water quality and quantity, and the use of satellite data to improve model structures and predictions. Recent work developed soil moisture predictions for Australia and water accounting tools from the field to the landscape.

Acknowledgement of Country: DARE PhD Candidate, Fabian Leal (University of Western Australia)

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

Fabian 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.

Emma Nguyen (University of Sydney)

Emma is a PhD candidate in Statistics at the University of Sydney. Her interests are in probabilistic graphical models and Bayesian statistics, as well as their applications in providing informed decision making. She graduated from the University of Wollongong with first class Honours in Mathematics and Finance. Her Honours thesis focused on statistical paired comparison models using both Frequentist and Bayesian methods with emphasis on sparse and irregular datasets in many sporting competitions.

Throughout her undergraduate studies, Emma also participated in many student research projects in statistical climate models, option pricing, and data privacy, with the School of Earth and Environmental Sciences at the University of Wollongong, the Australian Mathematical Sciences Institute (AMSI), and the Information Security and Privacy (ISP) Group at CSIRO Data61.

Linduni Rodrigo (University of Sydney)

Linduni Rodrigo is a DARE PhD student who joined in February 2021. Her research interests include probabilistic modelling and variable selection methods, with particular application to -omics data.

She holds a Master by research degree in Bioinformatics from the Queensland University of Technology and there she studied about the machine learning methods to identify interaction between genetic variants associated with complex diseases. She also holds a B.Sc. in Statistics from the University of Sri Jayewardenepura, Sri Lanka. After her bachelors she worked as a Research Assistant and a Probationary Lecturer at Sri Lanka Technological campus for 2 years.

Maryam Zeinolabedini (University of New South Wales)

Maryam joined DARE as a PhD student in 2021. Her research interest is in using Data-Driven, Artificial Intelligence (AI), Hydrological Modelling, Statistical/Mathematical Modelling and Remote Sensing in water and environment fields. Her current PhD involves working on water quality prediction using data science techniques.

She completed her Master’s degree at the Kerman Graduate University of Technology, Iran. Her Master’s thesis studied the impact of climate change on river flow by using AI and Global Climate Models (GCMs). After graduation, she worked on wastewater treatment plants and hydrology by using machine learning and optimisation techniques.

In 2019 she joined a research group in Copenhagen University, working in the Department of Geosciences and Natural Resource Management where she focused on drone-based data.

Mike Day (NSW Dept Planning and Environment)

Mike leads the Spatial Insights team in the NSWDPE Science Division. His team exists to resolve biodiversity challenges with spatial data and to provide data-driven evidence for optimising environmental decisions.

Mike is a spatial scientist by trade, specialising in remote sensing and programming. He worked for a number of years supporting the development of the NSW Plant Community Type Map. More recently he has led a number of initiatives including the mapping of Threatened Ecological Communities and Koala Habitat.

He is currently developing new spatial information for the NSW Koala Strategy, as well as digital tools for the NSW Biodiversity Offset Scheme. He lives in the Blue Mountains and enjoys getting his 4WD bogged as often as possible.

NSW Department of Planning and Environment is a valued partner of DARE.

Megan Nguyen (University of Sydney)

Megan graduated from Vanderbilt University with a B.Sc.(Hons) in Applied Mathematics and Human and Organisational Development. She has worked extensively in private and public sectors in the United States. She was an investment banking summer analyst for Nomura Securities in its Mergers and Acquisitions team in New York City. As a Business Development Analyst for the State of Tennessee, she used R to generate predictive factors for the annual cost of water losses by considering variables like socioeconomic status of different state counties.

Megan was admitted into Harvard University’s Masters program in Computational Science and Engineering but was inspired by DARE’s strong research and mentorship focus, so decided to commit her passion for academia to the program and solving Australia’s environmental problems.

Her research interests include Variational Bayesian, MCMC, Bayesian neural networks and likelihood-free inference.

Paco Tseng (University of Sydney)

Paco is a DARE PhD candidate studying Statistics at the University of Sydney with a research interest in Bayesian Networks and its applications.

Prior to that, he completed his MSc in 2018 at the University of Melbourne, majoring in Statistics and Stochastic process. In his postgraduate degree, he studied Poisson process approximations in depth to assist in writing his master’s degree research thesis. Over the years, he has developed a particular interest in modern statistical theory, particularly high-dimensional statistics, and causal inference. After completing his MSc, he worked as a data science analyst at Suncorp Group for two years, where his role involved application of modern statistical techniques to business problems.

Rajitha Athukorala (University of Sydney)

Rajitha Athukorala is a DARE PhD student at University of Sydney joining from 2021. He has worked as a Research Associate at Geoinformatics Center (GIC), Asian Institute of Technology (AIT) in Thailand focusing on remotes sensing and GIS applications for real world problems. Rajitha has past involvements in projects related to climate projection, modelling, and downscaling, multi hazard risk assessment, and mapping with drones in several countries of central and southeast Asia.

He has also involved in several capacity building programs as a technical lead in remote sensing and GIS applications in agriculture, disaster risk management, and drone image processing for participants from India, Sri Lanka, Maldives, Thailand, Laos, and Tajikistan. He is also an Associate member of Institution of Engineers (IESL), Sri Lanka.

Please enjoy lunch and return at 1.05pm for the Keynote address.

Professor David Warton (University of New South Wales)

When deciding how to analyse data, two key things we should always keep front of mind are the Properties of the data and the Question we are trying to answer – that is, we should mind our Ps and Qs. We will discuss some key data properties which, when not accounted for, can lead to substantial artefacts in analyses. We will also see the importance of the research question by looking at an example dataset and seeing that it could be analysed many different ways depending on the research question. Then we shall discuss a few projects where my research group is proposing new approaches in biodiversity research so that we can better mind our Ps and Qs: when monitoring the effects of ecological restoration efforts; automatically detecting and classifying wildlife from underwater videos and camera traps; and mapping species as a function of their environment in a changing climate.

David Warton is a Highly Cited Researcher at the interface between data science and ecology. At the University of Sydney, he completed an Honours project in plant ecology and a coursework Masters degree in statistics, before doing a PhD at the interface between the two at Macquarie University.

Since 2003 David has had an academic appointment at UNSW, where he leads the Eco-Stats group and has attracted over $4M in competitive research project funding. His cross-disciplinary research involves evaluating the methods for data analysis currently used in ecology, and where necessary, developing new methodologies to assist ecologists answer key research questions, with a special focus on multivariate techniques that can be used to understand ecological communities, and point process models that can be used in species distribution modelling. David is also the founding director of Stats Central, the UNSW statistical consulting and collaboration unit.

Dr Andrew McPherson (Geoscience Australia)

Andrew is a senior geoscientist at Geoscience Australia, with 18 years experience across natural hazard and risk assessment, pre-competitive mineral exploration, environmental geoscience and hydrogeology. He has undergraduate qualifications from the University of Canberra in ecology and soil science, with an Honours degree in regolith geology. His PhD in Earth Sciences from the Australian National University focused on assessing sources and stores of salt in the landscape with applications to salinity management. He also has a Certificate IV in Asset Management (GIS Practices) from the Canberra Institute of Technology, and has recently completed a Graduate Certificate in Data Science at RMIT.

In collaboration with many colleagues, Andrew’s recent work has focused on using geophysical techniques (specifically airborne electromagnetics) to provide insights into geological variability within the recharge beds of the Great Artesian Basin in Queensland. Similar techniques are now being employed in the Upper Darling River floodplain region of western NSW, as part of the Australian Government’s Exploring for the Future program.

Geoscience Australia is a valued partner of DARE.

Mike Day (NSW Department of Planning and Environment)

Mike leads the Spatial Insights team in the NSWDPE Science Division. His team exists to resolve biodiversity challenges with spatial data and to provide data-driven evidence for optimising environmental decisions.

Mike is a spatial scientist by trade, specialising in remote sensing and programming. He worked for a number of years supporting the development of the NSW Plant Community Type Map. More recently he has led a number of initiatives including the mapping of Threatened Ecological Communities and Koala Habitat.

He is currently developing new spatial information for the NSW Koala Strategy, as well as digital tools for the NSW Biodiversity Offset Scheme. He lives in the Blue Mountains and enjoys getting his 4WD bogged as often as possible.

Dr Andrew McPherson (Geoscience Australia)

Andrew is a senior geoscientist at Geoscience Australia, with 18 years experience across natural hazard and risk assessment, pre-competitive mineral exploration, environmental geoscience and hydrogeology. He has undergraduate qualifications from the University of Canberra in ecology and soil science, with an Honours degree in regolith geology. His PhD in Earth Sciences from the Australian National University focused on assessing sources and stores of salt in the landscape with applications to salinity management. He also has a Certificate IV in Asset Management (GIS Practices) from the Canberra Institute of Technology, and has recently completed a Graduate Certificate in Data Science at RMIT.

In collaboration with many colleagues, Andrew’s recent work has focused on using geophysical techniques (specifically airborne electromagnetics) to provide insights into geological variability within the recharge beds of the Great Artesian Basin in Queensland. Similar techniques are now being employed in the Upper Darling Floodplain Project area in western NSW.

Dr Clara Grazian (DARE Chief Investigator)

Clara Grazian received a joint PhD in 2016 from University Paris-Dauphine (France) and Sapienza University of Rome (Italy), working on Bayesian analysis for mixture models and copula models. She then joined the Nuffield Department of Medicine and the Big Data Institute of the University of Oxford to work on an international project trying to investigate mechanisms of drug resistance developed by tuberculosis. Before joining the School of Mathematics and Statistics of the University of Sydney, Clara was Senior Lecturer in Statistics at University of New South Wales.

Clara’s research focuses on Bayesian modelling and computational aspects. In particular, she is interested in Bayesian clustering through mixture models, and the study of properties of prior distributions for clustering problems, and in dependence models such as copula models, when the interest lies on functional of the dependence. Part of her research is focused on computational algorithms to efficiently estimate Bayesian models, such as MCMC, ABC and variational Bayes. She has developed extensive applied collaborations, including modelling animal behaviours, association studies in genomics, portfolio optimization models in finance, long memory processes, machine learning for cybersecurity, modelling air pollution and its effect on vegetation and human health, modelling preferences.

Professor David Warton (DARE Chief Investigator)

David Warton is a Highly Cited Researcher at the interface between data science and ecology. At the University of Sydney, he completed an Honours project in plant ecology and a coursework Masters degree in statistics, before doing a PhD at the interface between the two at Macquarie University.

Since 2003 David has had an academic appointment at UNSW, where he leads the Eco-Stats group and has attracted over $4M in competitive research project funding. His cross-disciplinary research involves evaluating the methods for data analysis currently used in ecology, and where necessary, developing new methodologies to assist ecologists answer key research questions, with a special focus on multivariate techniques that can be used to understand ecological communities, and point process models that can be used in species distribution modelling. David is also the founding director of Stats Central, the UNSW statistical consulting and collaboration unit.

Chair: Dr Katie Silversides (DARE Post Doctoral Research Fellow)

Katie completed her PhD in geology in 2014 at The University of Sydney. From 2014 – 2022 she was a postdoctoral researcher at the Rio Tinto Centre for Mine Automation. She worked on the banded iron formation (BIF) hosted iron ore deposits of Western Australia’s Hammersley region, with the goal to increase the rate of ore boundary updates and improve geological mine models. Her work focused on applying machine learning to provide quick, automated or semi-automated processing of the relevant geological or geophysical data.

Katie has expertise in applying machine learning techniques to a range of data sources in classification and regression problems. Her work included providing measurements of uncertainty for these outputs.

Katie’s current multi-domain research dives into geology and hydrology projects, focusing on missing data, uncertainty and model selection.

Chair: Dr Monica Bian (DARE Post Doctoral Research Fellow)

Dr Ranran (Monica) Bian received her PhD from The University of Auckland and is currently working as a Research Fellow at ARC Training Centre in Data Analytics for Resources and Environments (DARE) of The University of Sydney. Prior to joining The University of Sydney, she worked as a postdoctoral researcher at The University of Adelaide.

Dr Bian’s PhD focused on heterogeneous network mining and analysis, where novel algorithms for community discovery, ranking, dynamic embedding and change modelling in different heterogeneous networks were developed. The key areas of her research interests include representation learning for large-scale dynamic heterogeneous networks, social network analysis, applying and adapting network embedding and analysis techniques in resource and environment management.

DARE Deputy Director, Professor Lucy Marshall (Macquarie University)

Lucy Marshall is Executive Dean of Faculty of Science and Engineering at Macquarie University in Sydney.

Lucy received her Bachelor of Civil Engineering in 2001, Master of Engineering Science in 2002 and her PhD in Civil and Environmental Engineering in 2006, all from the University of New South Wales (UNSW) in Sydney.

From 2006-2013 she was Associate Professor of Watershed Analysis at Montana State University, working at the interface of engineering and environmental science in quantifying uncertainty in hydrologic and environmental systems.

Following this she was Director of the Water Research Centre in the School of Civil and Environmental Engineering and Associate Dean of Engineering (Equity and Diversity) at UNSW.

Lucy’s technical expertise is in hydrologic modelling, model optimisation, and quantification of uncertainty in water resources analysis. Lucy is an expert in Monte Carlo methods, Bayesian inference, and associated methodologies aimed at improved uncertainty analysis of water resources modelling and the assessment of uncertainty in water resources.