Asset pricing with social interactions, adaptive learning, and differences in opinion. This project seeks to understand how social interactions and adaptive learning of investors affect asset prices in highly competitive and adaptive financial markets. It will develop an evolutionary asset pricing theory, novel empirical hypotheses and tests of financial market characteristics and provide implications for policy and market regulation.
ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this ....ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this Centre is to create innovative mathematical and statistical models that can uncover the knowledge concealed within the size and complexity of these big data sets, with a focus on using the models to deliver insight into problems vital to the Centre's Collaborative Domains: Healthy People, Sustainable Environments and Prosperous Societies.Read moreRead less
The mathematics of novel magnetic memory materials. Magnetic memories are the world’s principal device for storing information. The next generation will have greatly increased access speed and data-storage capacity. This project will develop the mathematical theory of these new magnetic memory materials, a crucial first step in understanding and being able to fine-tune their properties.
Software-defined provisioning of Internet of Things applications in fog computing systems. This project aims to investigate and provide solutions for the realisation of a seemingly integrated Fog Computing (FC) paradigm with cloud environments, networking devices and Internet of Things devices. Fog Computing (FC) is an emerging paradigm with great promises for advancing Information and Communications Technologies. Using interdisciplinary approaches, the project expects to generate new knowledge ....Software-defined provisioning of Internet of Things applications in fog computing systems. This project aims to investigate and provide solutions for the realisation of a seemingly integrated Fog Computing (FC) paradigm with cloud environments, networking devices and Internet of Things devices. Fog Computing (FC) is an emerging paradigm with great promises for advancing Information and Communications Technologies. Using interdisciplinary approaches, the project expects to generate new knowledge for optimising both hardware and software resources of a FC system. Outcomes of this project include practical solutions through building novel mathematical frameworks and optimisation objectives. The project is expected to provide efficient monitoring and control of intelligent spaces, management of urban and rural environments and will have applications in the areas of energy, security, transport and public health.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100741
Funder
Australian Research Council
Funding Amount
$382,274.00
Summary
Tractable Bayesian algorithms for intractable Bayesian problems. This project seeks to develop computationally efficient and scalable Bayesian algorithms to estimate the parameters of complex models and ensure inferences drawn from the models can be trusted. Bayesian parameter estimation and model validation procedures are currently computationally intractable for many complex models of interest in science and technology. These include biological processes such as the efficacy of heart disease, ....Tractable Bayesian algorithms for intractable Bayesian problems. This project seeks to develop computationally efficient and scalable Bayesian algorithms to estimate the parameters of complex models and ensure inferences drawn from the models can be trusted. Bayesian parameter estimation and model validation procedures are currently computationally intractable for many complex models of interest in science and technology. These include biological processes such as the efficacy of heart disease, wound healing and skin cancer treatments. Potential outcomes of the project include new algorithms to significantly economise computations and improved understanding of the mechanisms of experimental data generation. Improved models of wound healing, skin cancer growth and heart physiology supported by these algorithms could improve population health.Read moreRead less
Random network models with applications in biology. Complex biological systems consist of a large number of interacting agents or components, and so can be studied using mathematical random network models. We aim to gain deeper insights into the laws emerging as the random networks evolve in time. This can help us to deal with dangerous disease epidemics and better understand the human brain.
Retirement income product innovation. This project aims to develop and assess comprehensive retirement income products to support sustainable retirement income streams for the Australian superannuation system. It will provide a framework to develop flexible structured retirement income products, taking into account the fair and effective allocation of costs and risks. Actuarial and financial analysis will highlight savings in Age Pension and aged care costs arising from more effective design of ....Retirement income product innovation. This project aims to develop and assess comprehensive retirement income products to support sustainable retirement income streams for the Australian superannuation system. It will provide a framework to develop flexible structured retirement income products, taking into account the fair and effective allocation of costs and risks. Actuarial and financial analysis will highlight savings in Age Pension and aged care costs arising from more effective design of retirement income products incorporating investment and longevity risk. It intends to develop risk sharing retirement products, risk management strategies, and longevity index-based hedging contracts to share and mitigate financial and longevity risk.Read moreRead less
Stochastic modelling of genetic regulatory networks with burst process. This project will develop the next generation of stochastic modelling to study the fundamental principles of genetic regulation. Simulations will yield deeper insight into the origin of bistability and oscillation in gene networks.
Overseeing the internet: new paradigms of network measurement. Like the electricity network, the internet is a core infrastructure, and so must be reliable and efficient. A gap in bandwidth supply is like a blackout in terms of lost business and productivity. This project will provide the measurement breakthroughs to ensure that network behaviour can be accurately and comprehensively monitored.
Discovery Early Career Researcher Award - Grant ID: DE140100993
Funder
Australian Research Council
Funding Amount
$293,520.00
Summary
Mathematics of importance: The optimal importance sampling algorithm for estimating the probability of a black swan event. Rare event simulation and modelling is critical to our understanding of high-cost hard-to-predict events such as nuclear accidents, natural disasters, and financial crises. Quantitative analysis of such high-impact events demands the accurate estimation of the probability of occurrence of such rare events. In realistic models this probability is very difficult to estimate, ....Mathematics of importance: The optimal importance sampling algorithm for estimating the probability of a black swan event. Rare event simulation and modelling is critical to our understanding of high-cost hard-to-predict events such as nuclear accidents, natural disasters, and financial crises. Quantitative analysis of such high-impact events demands the accurate estimation of the probability of occurrence of such rare events. In realistic models this probability is very difficult to estimate, because exact simple analytical formulas are not available and the existing estimation methods fail spectacularly. There is an urgent need for new efficient methodology. This project develops a new Monte Carlo method that will be able to estimate reliably and accurately rare-event probabilities. Read moreRead less