The development of vaccines and better treatments for HIV-AIDS and Hepatitis C are urgent global health priorities. This Program will undertake studies to better understand effective immunity against HIV and hepatitis C, allowing the rational design and testing of novel vaccines and treatments. The Program brings together a team of researchers with skills in basic virology and immunology with those providing expertise in translating findings in the laboratory into human clinical trials.
Multistage Vaccines For The Prevention Of Tuberculosis
Funder
National Health and Medical Research Council
Funding Amount
$884,290.00
Summary
Almost two million people die from tuberculosis (TB) each year. The current vaccine, BCG, is ineffective at controlling TB and the type of immune response needed to protect against the disease is poorly understood. We have discovered new antigens of the TB bacterium, and we will combine them with novel delivery strategies to develop new TB vaccines. We will also determine the type of immune response needed to protect against TB, which will aid progression of vaccines into clinical trials.
The Male Partner Contribution To Pregnancy Immune Tolerance Deficit In Women
Funder
National Health and Medical Research Council
Funding Amount
$1,462,925.00
Summary
A complication-free pregnancy and birth of a healthy infant depends on adequate preparation of the mother's immune system to tolerate the 'foreign' fetus, Both the mother and the father contribute to establishing optimal immune tolerance. This project will determine the links between specific agents in male seminal fluid and the female immune response, and will make progress towards new diagnostic tests and treatment options for unexplained subfertility and gestational disorders.
Immunomodulatory Vaccines In The Treatment Of Peanut Allergy
Funder
National Health and Medical Research Council
Funding Amount
$678,899.00
Summary
Peanut allergy is the most common cause of food-induced anaphylactic reactions in Australia and is a major burden to our healthcare system. Current clinical practice advice dietary avoidance to prevent fatal anaphylactic responses. We propose the use of an immunomodulatory vaccine to re-write the immune response to peanut antigens, from an allergic to a tolerant phenotype. This study will provide novel insights into rational approaches for manipulating immune memory to food allergens.
ARC Communications Research Network. Building on a strong platform of existing research excellence, the Aim of the Network is to facilitate nation-wide collaborative research, promoting four intersecting research Themes: Mobile and Wireless Communications, Rural Communications, Broadband and Optical Networks, and Fundamentals of Emerging Media. Each Theme is formulated to drive multidisciplinary, innovative research as well as inspire new collaborative initiatives. Four Programs encapsulate the ....ARC Communications Research Network. Building on a strong platform of existing research excellence, the Aim of the Network is to facilitate nation-wide collaborative research, promoting four intersecting research Themes: Mobile and Wireless Communications, Rural Communications, Broadband and Optical Networks, and Fundamentals of Emerging Media. Each Theme is formulated to drive multidisciplinary, innovative research as well as inspire new collaborative initiatives. Four Programs encapsulate the core activities of the Network: Researcher Mobility, Workshops and Conferences, Postgraduate Education, and Knowledge Management Systems. The Network is expected to add significant value to pre-existing investments and raise the profile of Australian telecommunications research.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120101161
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Compressive sensing based probabilistic graphical models (PGM). The aim of the project is to develop fast, large scale probabilistic graphical models (PGM) learning and inference methods. The resulting system will be able to process large scale PGMs on a standard PC, and will be easily extendable to computer clustering for larger scale PGMs requiring higher precision.
Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop ....Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop fast and scalable data mining methods for identifying causal relationships from large and/or high dimensional data sets. The developed methods will mainly be evaluated in real world biological applications. The research outcomes will be useful in many areas for causal reasoning and decision making.Read moreRead less
Fairness aware data mining for discrimination free decision-making. This project aims to develop data mining methods to detect algorithmic discriminations and to build fair decision models. It expects to provide techniques for regulatory organisations to detect discriminations in algorithmic decisions, and for various companies and organisations to build fair decision systems. Expected outcomes are novel and accurate methods for discrimination detection, practical and versatile techniques for fa ....Fairness aware data mining for discrimination free decision-making. This project aims to develop data mining methods to detect algorithmic discriminations and to build fair decision models. It expects to provide techniques for regulatory organisations to detect discriminations in algorithmic decisions, and for various companies and organisations to build fair decision systems. Expected outcomes are novel and accurate methods for discrimination detection, practical and versatile techniques for fair decision model building, and improved understanding of the relationships between privacy preservation and discrimination prevention to enable new techniques to achieve both goals. The developed techniques enable society to tackle ethical challenges in the big data era where many decisions are analytics based. Read moreRead less
Developing novel data mining methods to reveal complex group relationships from heterogeneous data. This project aims to develop novel and effective data mining methods that will enable us to unravel the relationships between multiple, rather than individual, components of complex systems (such as genes, gene regulators and cancer), which is crucial to understanding how such systems work. Potential applications for such methods are extensive.
Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are compl ....Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.Read moreRead less