Congratulations to the following members of our Centre leadership team for their success in getting a Discovery Project Grant from the Australian Research Council.
Advances in Sequential Monte Carlo Methods for Complex Bayesian Models.
Associate Professor Christopher Drovandi, Professor Chris Oates, Dr Anthony Lee
This project aims to develop efficient statistical algorithms for parameter estimation of complex stochastic models that currently cannot be handled. Parameter estimation is an essential component of mathematical modelling for answering scientific questions and revealing new insights. Current parameter estimation methods can be inefficient and require too much user intervention. This project will develop novel Bayesian algorithms that are optimally automated and efficient by exploiting ever-improving parallel computing devices. The new methods will allow practitioners to process realistic models, enabling new scientific discoveries in a wide range of disciplines such as biology, ecology, agriculture, hydrology and finance. ($390,000)
Precision ecology: the modern era of designed experiments in plant ecology.
Associate Professor James McGree, Associate Professor Jennifer Firn, Professor Eric Seabloom, Professor Elizabeth Borer
This project aims to develop the field of precision ecology, forging a new era of designed experiments where sampling is informed by research questions and what is known about the ecological process being studied. Through the development of novel statistical methods, new experiments globally will be designed to answer important ecological questions including what influence abiotic and biotic factors have on plant communities over time and different spatial scales. Expected outcomes include new methods and tools that will modernise how future experiments will be conducted in plant ecology. This will provide significant transdisciplinary benefits including new statistical methods that target scientific discovery in ecological studies.($360,000)
Human-Machine Teaming: Designing synergistic learning of humans and machines.
Professor Margot Brereton; Professor Andrew Bradley; Dr Laurianne Sitbon; Professor David Lovell; Dr Benoit Favre
This proposal investigates the design of systems in which humans and machines use their different abilities to learn together for mutual benefit. Machine learning has been commoditised, applied in areas such as medical image reading and autonomous vehicles, however it typically operates separately from humans, supplanting human skills and leading to deskilling. Using human-computer interaction research techniques, co-design and iterative prototyping in the domains of radiology training and environmental learning, we will devise and evaluate exemplar systems that support humans to interactively frame problems, explore and learn, while utilising and improving machine models, leading to a guiding framework for designing human-machine teaming.