Architectural Design Across Spaces and Cultures: Technology and Language. This project addresses two significant productivity barriers facing Australia’s architectural practices; designing in spatially distributed and culturally diverse teams.
While design practices are central to sustaining Australia’s creative export sector, growing concerns associated with online, multilingual design teams have been identified.
Directly responding to Australia’s COVID-19 recovery plans, this research seeks ....Architectural Design Across Spaces and Cultures: Technology and Language. This project addresses two significant productivity barriers facing Australia’s architectural practices; designing in spatially distributed and culturally diverse teams.
While design practices are central to sustaining Australia’s creative export sector, growing concerns associated with online, multilingual design teams have been identified.
Directly responding to Australia’s COVID-19 recovery plans, this research seeks to develop new knowledge about the cognitive, social and technical factors that shape the effectiveness of online international design teamwork. Its goal is to leverage the opportunities provided through technological advances and multicultural practices, to remove barriers to design productivity and enhance creativity.Read moreRead less
Inverse problems with partial data. This project aims to use mathematics, in particular the theory of micro-local analysis, to determine the amount of measurements one needs in order to reconstruct an image by some of the tomography methods commonly used in medical imaging. Expected outcomes of this project include showing that an arbitrarily small set of boundary measurements is sufficient to reconstruct the coefficients of various important partial differential equations such as Schrodinger eq ....Inverse problems with partial data. This project aims to use mathematics, in particular the theory of micro-local analysis, to determine the amount of measurements one needs in order to reconstruct an image by some of the tomography methods commonly used in medical imaging. Expected outcomes of this project include showing that an arbitrarily small set of boundary measurements is sufficient to reconstruct the coefficients of various important partial differential equations such as Schrodinger equation, Dirac operators, and Maxwell equations. In addition to providing a theoretical foundation upon which one can build numerical algorithms, this project will also provide the missing link between inverse problems and unique continuation theory. The downstream impact of this research will lead to more efficient and accurate tomography methods which can be implemented in a range of imaging applications.Read moreRead less
An optimisation-based framework for non-classical Chebyshev approximation. This project aims to solve open mathematical problems in multivariate and piecewise polynomial approximations, two directions that correspond to fundamental obstacles to extending classical approximation results. Through an innovative combination of optimisation and algebraic technique, the project intends to develop foundations for new results in approximation theory, and new insights into other areas of mathematics, mos ....An optimisation-based framework for non-classical Chebyshev approximation. This project aims to solve open mathematical problems in multivariate and piecewise polynomial approximations, two directions that correspond to fundamental obstacles to extending classical approximation results. Through an innovative combination of optimisation and algebraic technique, the project intends to develop foundations for new results in approximation theory, and new insights into other areas of mathematics, most notably optimisation. The techniques and methods developed should also have significant benefits in the many disciplines where approximation problems appear, such as engineering, physics or data mining. The research outputs resulting from this project will be used in a wide range of fields to help implement programs, policies and improve decision making.Read moreRead less
A new asymptotic toolbox for nonlinear discrete systems and particle chains. This project aims to pioneer a mathematical toolbox of new asymptotic techniques for discrete systems driven by vanishingly small influences. The purpose of these techniques is to permit the asymptotic study of discrete problems in which significant effects originate due to subtle causes that are invisible to existing asymptotic methods. Discrete systems play a significant role in modern applied mathematics, and it is v ....A new asymptotic toolbox for nonlinear discrete systems and particle chains. This project aims to pioneer a mathematical toolbox of new asymptotic techniques for discrete systems driven by vanishingly small influences. The purpose of these techniques is to permit the asymptotic study of discrete problems in which significant effects originate due to subtle causes that are invisible to existing asymptotic methods. Discrete systems play a significant role in modern applied mathematics, and it is vital that mathematical tools be designed in order to explore their behaviour. The aim of this project is to open new pathways for resolving open scientific problems, providing benefits such as understanding the energy dissipation of particle chains and granular lattices contained in small-scale technological components.Read moreRead less
How electric fields can facilitate reversible protein binding to surfaces. The aim of this project is to develop the first biosensors that prevent nonspecific protein adsorption and allow reversible protein binding. The project expects to achieve this using a combination of novel surface chemistry and pulsed electric fields that dynamically change a sensing interface. The impact of electric fields on the binding of proteins to this interface will be followed using a novel single molecule fluores ....How electric fields can facilitate reversible protein binding to surfaces. The aim of this project is to develop the first biosensors that prevent nonspecific protein adsorption and allow reversible protein binding. The project expects to achieve this using a combination of novel surface chemistry and pulsed electric fields that dynamically change a sensing interface. The impact of electric fields on the binding of proteins to this interface will be followed using a novel single molecule fluorescence microscope previously developed that can locate the position of proteins with 2 nanometer resolution. The expected outcomes of this project is a class of biosensor that can continuously monitor protein biomarkers for wearable sensors that provide information on a user’s wellness and nutrition.Read moreRead less
Creating Hybrid Exponential Asymptotics for use with Computational Data. Asymptotic analysis is a vital tool for studying small influences with critical effects. This project aims to create an innovative fully-automated asymptotic framework for studying phenomena which are invisible to classical approximation methods, using new ideas from asymptotics and numerical complex analysis. The outcome will be the first framework that can be used on data from numerical simulations or real-life measuremen ....Creating Hybrid Exponential Asymptotics for use with Computational Data. Asymptotic analysis is a vital tool for studying small influences with critical effects. This project aims to create an innovative fully-automated asymptotic framework for studying phenomena which are invisible to classical approximation methods, using new ideas from asymptotics and numerical complex analysis. The outcome will be the first framework that can be used on data from numerical simulations or real-life measurements, and which can be applied automatically without hands-on expert input. It will be used to design submerged structures and efficient vessels with minimal energy loss from surface waves. Expected benefits include making powerful methods accessible to scientists, and new paths for energy-efficient industrial design.Read moreRead less
Nanoarchitectured multifunctional porous superparamagnetic nanoparticles. This project aims to develop a method for the direct detection of biomarkers based on a new class of highly porous superparamagnetic nanoparticles with peroxidase-like activity. The particles will be used as dispersible capture agents for isolating specific targets in biological samples, and electrocatalytic nanozymes for naked-eye evaluation and electrochemical detection. The project is expected to develop simple, low-cos ....Nanoarchitectured multifunctional porous superparamagnetic nanoparticles. This project aims to develop a method for the direct detection of biomarkers based on a new class of highly porous superparamagnetic nanoparticles with peroxidase-like activity. The particles will be used as dispersible capture agents for isolating specific targets in biological samples, and electrocatalytic nanozymes for naked-eye evaluation and electrochemical detection. The project is expected to develop simple, low-cost, portable devices for the analysis of exosomes and exosomal miRNA in biological samples. The future development of this technology into diagnostic devices will improve patient outcomes by enabling earlier disease diagnosis and improved monitoring of treatment.Read moreRead less
High Dimensional Approximation, Learning, and Uncertainty. This project aims to develop next-generation computational methods for complex problems in science and engineering that have many uncertain parameters, using advanced high-dimensional strategies and deep learning to enhance computational speed. The significance of the project is that these methods will help address important applications that at present are not feasible or at the edge of feasibility. The expected outcomes are powerful me ....High Dimensional Approximation, Learning, and Uncertainty. This project aims to develop next-generation computational methods for complex problems in science and engineering that have many uncertain parameters, using advanced high-dimensional strategies and deep learning to enhance computational speed. The significance of the project is that these methods will help address important applications that at present are not feasible or at the edge of feasibility. The expected outcomes are powerful methods that will be mathematically rigorous and suitable for a wide variety of applications. The benefits are that the project will boost Australia’s position as a leader in innovation, and contribute to future developments over a wide area, from aerospace engineering to personalised computational oncology.Read moreRead less
Large dynamic time-varying models for structural macroeconomic inference. This project aims to broaden the range of macroeconomic models that have an integrated capacity for both greater realism and efficiency in analysis. This approach will be applied to two contexts at the forefront of current macroeconomic research, the effects of noisy productivity signals on business cycles and the effects of fiscal policy shocks. Flexible macro-econometric models underpin accurate inference by economists ....Large dynamic time-varying models for structural macroeconomic inference. This project aims to broaden the range of macroeconomic models that have an integrated capacity for both greater realism and efficiency in analysis. This approach will be applied to two contexts at the forefront of current macroeconomic research, the effects of noisy productivity signals on business cycles and the effects of fiscal policy shocks. Flexible macro-econometric models underpin accurate inference by economists and policymakers and the project outputs should provide widespread and significant benefits by improving policy and boosting Australia’s comparative advantage.Read moreRead less
Deep learning based time series modeling and financial forecasting. 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 ....Deep learning based time series modeling and financial forecasting. 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.Read moreRead less