Congratulations to the following ACEMS members for their success in getting a Discovery Project Grant from the Australian Research Council.
Computational methods for population-size-dependent branching processes.
Dr Sophie Hautphenne, Dr Giang Nguyen, Dr Melanie Massaro
Branching processes are the primary mathematical tool used to model populations that evolve randomly in time. Most key results in the theory are derived under the simplifying assumption that individuals reproduce and die independently of each other. However, this assumption fails in most real-life situations, in particular when the environment has limited resources or when the habitat has a restricted capacity. This project aims to develop novel and effective algorithmic techniques and statistical methods for a class of branching processes with dependences. We will use these results to study significant problems in the conservation of endangered island bird populations in Oceania, and to help inform their conservation management. ($380,000)
Loss-based Bayesian Prediction.
Professor Gael Martin, Dr David Frazier, Professor Robin Hyndman, Assoc Prof Worapree Maneesoonthorn
This project proposes a new paradigm for prediction. Using state-of-the-art computational methods, the project aims to produce accurate, fit for purpose, predictions which, by design, reduce the loss incurred when the prediction is inaccurate. Theoretical validation of the new predictive method, without reliance on knowledge of the correct statistical model, is an expected outcome, as is an extensive numerical assessment of its performance in empirical settings. The new paradigm should produce significant benefits for all fields in which the consequences of predictive inaccuracy are severe. Problems that lead to substantial economic, financial or environmental loss if predictions are incorrect will be given particular attention. ($393,000)
Optimising progress towards elimination of malaria.
Dr Jennifer Flegg, Assoc Professor Jonathan Keith, Professor Kate Smith-Miles, Assoc Professor Jonathan "Jack" Richards
The project aims to advance mathematical knowledge by developing novel tools appropriate for modelling disease elimination. We will apply these new mathematical tools to the significant problem of malaria elimination in Vietnam. The expected outcomes are new tools for modelling disease elimination on a fine spatial resolution with heterogeneities in individual patient characteristics, calibrating models to household level data on disease transmission and designing intervention strategies for maximum effect on disease transmission. The innovative combination of modelling, inference and optimisation ensures that the mathematical methods developed will be broadly applicable to modelling elimination strategies for other infectious diseases. ($520,000)
Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries.
Professor Dirk Kroese, Professor Jerzy Filar, Dr Nan Ye, Dr Hanna Kurniawati, Dr Martin Bohner
Partially Observable Markov Decision Processes (POMDPs) provide a general mathematical framework for sequential decision making under uncertainty. However, solving POMDPs effectively under realistic assumptions remains a challenging problem. This project aims to develop new efficient Monte Carlo algorithms to significantly advance the application of POMDPs to real-world decision problems involving complex action spaces and system dynamics. Both theoretical and algorithmic approaches will be applied to sustainable fishery management --- an important problem for Australia and an ideal context for POMDPs. The project will advance research in artificial intelligence, dynamical systems, and fishery operations, and benefit the national economy. ($365,000)
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)
Deep learning based time series modeling and financial forecasting.
Dr Minh-Ngoc Tran, Professor Junbin Gao, Professor Richard Gerlach
This project pursues breakthroughs in time series modelling and develops novel statistical models and inference techniques, with a focus on modelling of financial time series data. The advances will be achieved through interdisciplinary research, combining recent advances in machine learning, Bayesian computation, financial econometrics and the increasing availability of Big Data. The outcomes will provide a new range of proven and powerful approaches for analysing time series and understanding time effects. The methodologies developed will lead to a greater accuracy in financial forecasting and risk management, and open up new horizons for the wider scientific community to analyse time series data. ($280,000)
Engineering the Next Generation of Broadband Terahertz Technologies.
Professor Aleksandar Rakic, Dr Thomas Taimre, Dr Yah Leng Lim, Professor Edmund Linfield, Assoc Professor Dragan Indjin, Dr Paul Dean
This project proposes a new broadband, high-power, laser technology for THz sensing. This semiconductor laser based THz technology is crucial for a wide range of applications requiring the acquisition of THz spectral signatures of materials and high-frame rate hyper-spectral THz imaging. We propose two pathways to engineer this novel THz technology: using a tuneable, coupled-cavity quantum cascade semiconductor laser and by creating the broad emission spectra through active mode locking in a THz semiconductor laser. The THz laser coupled with the self-detection technique is the key to realising this, and will be explored both in model and experiment. ($440,000)
Augmented Sociality: Enabling a Socialised Experience of Augmented Reality.
Dr Alessandro Soro, Professor Peta Wyeth, Dr Ross Brown, Dr Selen Turkay
This project will explore new socialised uses of Augmented Reality (AR) that expand creativity, social relations, and participation. We seek to better understand how AR content can be leveraged by people to create their own new ways of learning, collaborating, and relating with each other. To do so we will study and prototype new tools and platforms to allow non-experts to create their own AR media. We aim to enable people of all ages, education, and background, to imagine and create, and not just passively consume, AR contents, services, and applications. We will generate new applications of AR, a new platform to collaboratively create these applications, and a new theory of 'Augmented Sociality' to guide AR design. ($509,000)