Please collect your name tag and enjoy some refreshments.

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.

Arpit is a DARE PhD candidate at the School of Mathematics and Statistics at UNSW, 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.

Arpit received his undergraduate degree in Computer Science and Engineering in 2019. His recent research has focused on Bayesian Neural Networks, evolutionary and tempered-MCMC methods and their applications in time-series modelling and pattern classification problems for climate and atmospheric events.

Samudra graduated from the University of Peradeniya, Sri Lanka with First Class Honours from a Bachelor of Science in Statistics and Operations Research. She also completed her Master of Science in Industrial Mathematics in 2021 at the University of Sri Jayewardenepura, Sri Lanka.

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 at UNSW, her research interest is on time series analysis, Bayesian Inference, hierarchical modelling, and data visualisation.

Sam completed his integrated Master’s degree at the University of Manchester in 2019, where he studied for a dual Honours in Mathematics and Physics. In addition to his studies, Sam has worked for the European Space Agency, Satellite Applications Catapult and as a Software Developer for Apadmi Ltd.

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

Sally Cripps is an internationally recognised scholar and leader in Bayesian Machine Learning and Artificial Intelligence. 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 was cofounder and co-director of the University of Sydney’s Centre for Translation Data Science (CTDS) and was founder and inaugural Director of the DARE Centre. Most recently she was Research Director of Analytics and Decision Science and Science Director of the Next Gen AI Training Programme in CSIRO’s Data61.

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, 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.

Tea and coffee will be provided at the back of the auditorium.

Sam is a DARE PhD candidate at UNSW having worked in the 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.

Sam’s master’s thesis focused on tree canopy research using Nearmap’s visible spectrum imagery and AI datasets, which triggered an interest in ecology and environmental science. His PhD centres on developing robust spatio-temporal species distribution models to assess the impact of climate change on species diversity and distributions.

Yiyi graduated from Imperial College London with a Bachelor of Science in Mathematics in 2020 and a Master of Science in Statistics (Data Science) in 2021. She explored different statistical models and machine learning methods through various student statistical projects in fields including politics, finance, cyber security and more.

As a DARE PhD candidate at UNSW, Yiyi 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.

Maria Clara Lopes Paula has a Master’s degree in Applied Geophysics (2019) and a Bachelor’s degree in Geophysics from the University of Brasília, Brazil. From 2015, she has been using geophysical methods to mitigate deforestation in Brazilian biomes, and in 2018, Maria started to apply statistical methods to assess their uses in geophysical interpretation.

As a DARE PhD candidate at UWA, Maria is applying machine learning techniques to help inform decisions about the coexistence of natural resource exploration and subterranean fauna in the Pilbara region of Western Australia.

Fabian completed a Master’s degree in Operations Research at the University of Chile, where his research consisted of applying mathematical programming to spatial conservation planning, a branch of ecology. As a DARE PhD candidate at UWA, he sought to continue applying his mathematical background to resources and environments.

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.

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. He has worked in post-doctoral positions at the Business School, UNSW, and the Department of Probability and Statistics, Sheffield University, and as a statistical consultant at The Australian Institute of Marine Science, Townsville.

Currently, he is a senior lecturer in the Department of Mathematics and Statistics at UWA. 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.

Thank you for joining our Symposium! Please join us for lunch and networking.