Estimating genotype-environment interaction using genomic information. This project aims to develop statistical methods that can explore genotype–environment interaction at the genomic level using genome-wide single nucleotide polymorphisms or sequence data. It plans to estimate how the effects of genetic variants change with changing environmental conditions and how overall genetic variance changes due to changing effects in specific gene regions. It plans to deliver statistical models and meth ....Estimating genotype-environment interaction using genomic information. This project aims to develop statistical methods that can explore genotype–environment interaction at the genomic level using genome-wide single nucleotide polymorphisms or sequence data. It plans to estimate how the effects of genetic variants change with changing environmental conditions and how overall genetic variance changes due to changing effects in specific gene regions. It plans to deliver statistical models and methods and an efficient algorithm implemented in software, which would broadly benefit the field of complex trait genetics. Methods to estimate genotype–environment interaction effects at the genomic level would help elucidate complex biological systems, including human genetic response to changing environmental factors and the potential adaptation of animals to changing environmental conditions.Read moreRead less
Whole-genome multivariate reaction norm model for complex traits. This project aims to develop a multivariate whole-genome genotype-covariate correlation and interaction model that can be applied to a wide range of existing genome-wide association study (GWAS) datasets. Genotype-covariate correlation and interaction (GCCI) are fundamental in biology but there is no standard approach to disentangle interaction from correlation in the whole-genome analyses. This project will address the key featur ....Whole-genome multivariate reaction norm model for complex traits. This project aims to develop a multivariate whole-genome genotype-covariate correlation and interaction model that can be applied to a wide range of existing genome-wide association study (GWAS) datasets. Genotype-covariate correlation and interaction (GCCI) are fundamental in biology but there is no standard approach to disentangle interaction from correlation in the whole-genome analyses. This project will address the key feature in biology, which relates to dissecting the complex mechanism of association and interaction. The proposed statistical model implemented in a context of a novel design based on multiple GWAS data sets is a paradigm shifting-tool with applications to multiple industries.Read moreRead less
Identifying Target Genes For Novel Anti-epileptic Therapies In The Mouse
Funder
National Health and Medical Research Council
Funding Amount
$469,802.00
Summary
Epilepsy is a disease which affects 2-4% of the population. There are a wide range of drugs available to treat the condition but there is consistently 30-40% of patients who do not respond well to any of these drugs and who continue to have seizures. The reason that there are no drugs available for these people is that most of the drugs available have been designed along the same principles. A new set of principles is needed to develop new drugs which will be able to treat those people not respo ....Epilepsy is a disease which affects 2-4% of the population. There are a wide range of drugs available to treat the condition but there is consistently 30-40% of patients who do not respond well to any of these drugs and who continue to have seizures. The reason that there are no drugs available for these people is that most of the drugs available have been designed along the same principles. A new set of principles is needed to develop new drugs which will be able to treat those people not responding to current therapy. This project is designed to identify new biologic pathways which may be interrupted with drugs to prevent seizures in people with epilepsy. This project uses a procedure to induce mutations into genes in mice and then screens for mice which do not seize when challenged with a drug which generates seizures in mice. Genetic studies will identify the mutated genes and these will be used as potential targets for new therapies or will identify new biological pathway which should expand the use of future anti-epileptic drugs.Read moreRead less
Statistical Methods of Model Fitting and Segmentation in Computer Vision. Electronic sensors such as cameras and lasers can provide a rich source of information about the position, shape, and motion of objects around us. However, to extract this information in a reliable, automatic, and accurate way requires a sophisticated statistical theory of the process. Example applications include: video surveillance (better automatic detection of moving people and vehicles and of characterising what those ....Statistical Methods of Model Fitting and Segmentation in Computer Vision. Electronic sensors such as cameras and lasers can provide a rich source of information about the position, shape, and motion of objects around us. However, to extract this information in a reliable, automatic, and accurate way requires a sophisticated statistical theory of the process. Example applications include: video surveillance (better automatic detection of moving people and vehicles and of characterising what those people and vehicles are doing), industrial prototyping and inspection (measuring the size and shape of objects), urban planning (laser scanning streetscapes to create computer models of cities), entertainment industry (movie special effects and games), etc. Read moreRead less
Learning to see in 3D. The project aims to endow machine vision with an ability we, as humans, use almost constantly: to judge 3D properties from a 2D image. This extremely useful ability will be applied to digital images to obtain 3D measurements and aid in automating tasks such as mining, surveying, medical diagnosis, and visual effects in movies.
Recognising and reconstructing objects in real time from a moving camera. This project will use a moving camera to estimate the three-dimensional shape and identity of objects and surfaces it can see. This ability, which we humans use all the time, has wide application in automation including driver assistance, exploring hazardous environments, robotics, remote collaboration, and the creation of three-dimensional models for entertainment.
Optimal Robust Fitting under the Framework of LP-Type Problems. The project aims to develop algorithms to support the development of robust and accurate computer vision systems. Real-world visual data (images, videos) is inherently noisy and outlier prone. To build computer vision systems that work reliably in the real world, it is necessary to ensure that the underlying algorithms are robust and efficient. The project aims to devise novel algorithms that can compute the best possible result giv ....Optimal Robust Fitting under the Framework of LP-Type Problems. The project aims to develop algorithms to support the development of robust and accurate computer vision systems. Real-world visual data (images, videos) is inherently noisy and outlier prone. To build computer vision systems that work reliably in the real world, it is necessary to ensure that the underlying algorithms are robust and efficient. The project aims to devise novel algorithms that can compute the best possible result given the input data in a short amount of time. The expected outcomes would support the construction of reliable and accurate computer vision-based systems, such as large-scale 3-D reconstruction from photo collections, self-driving cars and domestic robots.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL130100102
Funder
Australian Research Council
Funding Amount
$3,179,946.00
Summary
Lifelong computer vision systems. This project will create a computer vision system that can produce a detailed environmental map in real time, turning standard video cameras into sensors that 'understand' a scene with basic semantic tools. This high-level sensing will unlock a wide range of applications for autonomous systems.
Whole image understanding by convolutions on graphs. This project seeks to develop technologies that will help computer vision interpret the whole visible scene, rather than just some of the objects therein. Existing automated methods for understanding images perform well at recognising specific objects in canonical poses, but the problem of whole image interpretation is far more challenging. Convolutional neural networks (CNN) have underpinned recent progress in object recognition, but whole-im ....Whole image understanding by convolutions on graphs. This project seeks to develop technologies that will help computer vision interpret the whole visible scene, rather than just some of the objects therein. Existing automated methods for understanding images perform well at recognising specific objects in canonical poses, but the problem of whole image interpretation is far more challenging. Convolutional neural networks (CNN) have underpinned recent progress in object recognition, but whole-image understanding cannot be tackled similarly because the number of possible combinations of objects is too large. The project thus proposes a graph-based generalisation of the CNN approach which allows scene structure to be learned explicitly. This would represent an important step towards providing computers with robust vision, allowing them to interact with their environment.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170101259
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Zero-shot and few-shot learning with deep knowledge transfer. This project aims to develop few-shot and zero-shot learning, visual recognition techniques that can learn a visual concept with few or no visual examples. Visual recognition is a major component in Artificial Intelligence and used in cybernetic security, robotic vision and medical image analysis. This project will use deep learning to enable the zero/few-shot learning to use and model previously unexplored information, making zero/fe ....Zero-shot and few-shot learning with deep knowledge transfer. This project aims to develop few-shot and zero-shot learning, visual recognition techniques that can learn a visual concept with few or no visual examples. Visual recognition is a major component in Artificial Intelligence and used in cybernetic security, robotic vision and medical image analysis. This project will use deep learning to enable the zero/few-shot learning to use and model previously unexplored information, making zero/few-shot learning more practical, scalable and flexible. The project is expected to advance the applicability of visual recognition in many challenging scenarios and provide effective tools to analyse the online visual data for supporting Australia’s cybernetic security.Read moreRead less