New statistical methods for identifying micro-ribonucleic acid (miRNA) regulatory networks. Understanding gene regulatory networks is critical in the understanding of fundamental biological systems. These networks have important implications for the discovery of fundamental mechanisms relating to the diagnosis and management of many illnesses. This research will provide new statistical methods to identify regulatory micro-ribonucleic acid modules and to understand their relationship in gene regu ....New statistical methods for identifying micro-ribonucleic acid (miRNA) regulatory networks. Understanding gene regulatory networks is critical in the understanding of fundamental biological systems. These networks have important implications for the discovery of fundamental mechanisms relating to the diagnosis and management of many illnesses. This research will provide new statistical methods to identify regulatory micro-ribonucleic acid modules and to understand their relationship in gene regulatory networks through multiple covariance estimation and multivariate classification techniques. My results should enable researchers to better understand the regulation underlying biological systems, leading to improved human health, medical and biological research outcomes.Read moreRead less
Establishing an accurate chemical volatile profile of decomposition for use in victim recovery in mass disaster and forensic investigations. This project will advance forensic science by identifying a complete chemical profile of human decomposition scent. It will result in the development of a more accurate training scent aid to enhance the response of cadaver detection dogs deployed to scenes of mass disasters involving human remains.
Scalable and Robust Bayesian Inference for Implicit Statistical Models. This project aims to develop the next generation of efficient methods for fitting complex simulation-based statistical models to data. Practitioners and scientists are interested in such implicit models to enable discoveries, produce accurate predictions and inform decisions under uncertainty. However, the associated computational cost has restricted researchers to implicit models that must have a small number of parameters ....Scalable and Robust Bayesian Inference for Implicit Statistical Models. This project aims to develop the next generation of efficient methods for fitting complex simulation-based statistical models to data. Practitioners and scientists are interested in such implicit models to enable discoveries, produce accurate predictions and inform decisions under uncertainty. However, the associated computational cost has restricted researchers to implicit models that must have a small number of parameters and be well specified, impeding scientific progress. This project will develop new computational methods and algorithms for implicit models that scale to high dimensions and are robust to misspecification. Benefits will arise from the more routine use of implicit models in epidemiology, biology, ecology and other fields.Read moreRead less
Frontiers in Data Science: Analysing Distributions as Data. This project aims to develop the statistical foundations of a new approach to analysing large and complex data, based on building distributional approximations of the data, which can then be analysed by standard statistical methods. The need to analyse very large and complex datasets has become a vital part of everyday life, particularly in the analysis of national problems in public health, environmental pollution, computer network sec ....Frontiers in Data Science: Analysing Distributions as Data. This project aims to develop the statistical foundations of a new approach to analysing large and complex data, based on building distributional approximations of the data, which can then be analysed by standard statistical methods. The need to analyse very large and complex datasets has become a vital part of everyday life, particularly in the analysis of national problems in public health, environmental pollution, computer network security and climate extremes. The project expects to change our way of thinking in how to be smarter about what data we use (and collect) for analysis, rather than relying on brute force analysis of large datasets. The project is expected to transform the knowledge base of the discipline, and the resulting techniques will enable across-the-board research advances for many industries and disciplines.Read moreRead less
New nonparametric statistical methods for imperfectly observed data. Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutriti ....New nonparametric statistical methods for imperfectly observed data. Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutrition, this project aims to develop novel and highly effective statistical methodology for solving contemporary problems involving new types of imperfectly observed data. The expected outcomes will solve frontier problems, where information can only be accessed through sophisticated computer intensive methods.Read moreRead less
DNA and the missing: ancient DNA and advanced forensic identification. Identifying the remains of missing persons, disaster victims and war dead is of major social and cultural importance and has significant implications for national and international justice systems. This project will apply expertise in analysis of ancient DNA to build capacity and expertise within Australia to identify highly degraded human remains.
Faces in context: A new ecological paradigm for person identification. Accurate face recognition is critical to normal social functioning of individuals and identity management processes that underpin a secure and fair Australia. Current understanding is based on tests that do not capture the rich context surrounding person identification in daily life. This project aims to introduce new methods for observing person identification in daily life and real-world tasks that are critical to border se ....Faces in context: A new ecological paradigm for person identification. Accurate face recognition is critical to normal social functioning of individuals and identity management processes that underpin a secure and fair Australia. Current understanding is based on tests that do not capture the rich context surrounding person identification in daily life. This project aims to introduce new methods for observing person identification in daily life and real-world tasks that are critical to border security, criminal investigations and the justice system. Expected outcomes include an integrated framework for person identification describing the cognitive mechanisms that link faces to surrounding visual context and the viewer’s background knowledge. Benefits in forensic, security and legal settings are expected.Read moreRead less
Statistical Modelling in the Era of Data Science: Theory and Practice. This project aims to develop innovative statistical methodology that is interpretable, theoretically justified, and scalable to today's growing complex data. With the influx of data being collected in both the public and private sectors, making sense of this data is a fundamental task. Through a rigorous modelling framework, this project intends to facilitate the discovery of knowledge by developing powerful new tools to extr ....Statistical Modelling in the Era of Data Science: Theory and Practice. This project aims to develop innovative statistical methodology that is interpretable, theoretically justified, and scalable to today's growing complex data. With the influx of data being collected in both the public and private sectors, making sense of this data is a fundamental task. Through a rigorous modelling framework, this project intends to facilitate the discovery of knowledge by developing powerful new tools to extract insight from these complex datasets. The outcomes of this project will benefit society by providing techniques to enable research advances and inform decision-making for a broad base of disciplines, including applications to network security, energy forecasting, environmental monitoring, and public health. Read moreRead less
Advancing tools for the analysis of high-dimensional data in ecology. This project will accelerate the development of advanced tools for answering fundamental questions concerning the potential impact of climate change on ecological communities. These advanced methodologies, more powerful than currently used methods, will fit easy-to-interpret models which can handle all common data types.
Dissecting the shared genetic architecture of psychiatric and psychological traits with application to prediction of genetic risk. Identification of the early phase of psychiatric disorders is considered critical for early intervention which is the essence of prevention. At present, the main obstacle to targeted early intervention strategies in psychiatric disorders is the non-specific nature of early stage symptoms. Many psychiatric disorders present with symptoms of depressed mood and anxiety ....Dissecting the shared genetic architecture of psychiatric and psychological traits with application to prediction of genetic risk. Identification of the early phase of psychiatric disorders is considered critical for early intervention which is the essence of prevention. At present, the main obstacle to targeted early intervention strategies in psychiatric disorders is the non-specific nature of early stage symptoms. Many psychiatric disorders present with symptoms of depressed mood and anxiety in the early stages, yet best intervention treatments are dependent on the final (unknown) diagnosed disorder. Prediction of genetic risk is likely to make an important contribution for identification of individuals at risk of specific psychiatric disorders so that the best early intervention treatment can be administered. Read moreRead less