Seed Funding Award Announcement
We sought collaborative interdisciplinary projects that join data science with expertise from other domains, you sent in a large number of strong expressions of interest!
A big thank you to all of you who submitted an application and told us about your intriguing research projects. Our inaugural round of Seed Funding has now closed and we are pleased to tell you about the projects that will receive support.
AI at the forefront of the eye: an artificial intelligence platform to transform the analysis of in vivo confocal microscopy images of the living human cornea
Congratulations to Chief Investigator A/Prof Laura Downie and Co-Investigators Prof Karin Verspoor, Dr Holly Chinnery and Dr Vlada Rozova.
This is a new interdisciplinary collaboration with expertise spanning clinical ocular imaging, neuroimmunology, AI for disease detection and cellular image analysis plus the team spans all academic levels. The project proposes to advance data science by preparing a high-quality data set for analysis and experimenting with methods supporting the first multi-stage deep learning framework to facilitate quality assessment and streamline analysis of human corneal confocal microscopy images. This work will improve the rigor and efficiency of corneal anatomical imaging and lead to better identification and monitoring of the immunological and neural effects of ocular and systemic disease.
Gendered algorithms: rethinking discrimination in automated recruitment predictions
Congratulations to Chief Investigator A/Prof Leah Ruppanner and Co-Investigators Dr Marc Cheong, Dr Lea Frermann, and Ms Sheilla Njoto.
This project brings together an interdisciplinary team of experts and incorporates knowledge of social science experts into systematic use ofmachine learning and NLP to promote a more ethical and informed use of algorithms. It advances algorithmic analysis to rationalise the outputs of hiring algorithms (including black-box analysis, NLP and data analytics); sociological analysis to examine the extent to which algorithmic prediction based on gender is considered a ‘discrimination’; and the study of public policy to respond to the issue beyond direct and indirect discrimination using a comprehensive mitigation plan that can be systematically applied to algorithmic governance.
The Victorian Twin Cohort Study: harnessing the power of linked data for twin studies to improve health for all
Congratulations to Chief Investigator Dr Jesse Young and Co-Investigators Prof John Hopper, A/Prof Douglas Boyle, Dr Lucas Calais-Ferreira, Dr Sue Malta, Ms Ximena Camacho, Dr Miriam Mosing and Dr Adrian Bickerstaffe.
This project will develop an algorithm to accurately identify twins from routinely-linked administrative data in Victoria, generating Australia's first population-wide cohort of twins. Using novel statistical modelling to investigate and account for familial risk factors, the team will build a stronger evidence-base on the causes of major chronic conditions in the Australian population from birth to old age. Beyond Victoria, the team can collaborate with all other linkage nodes in Australia to adopt this algorithm and establish a national database of Australian Twins. Therefore, through innovative data science, this project will drive evidence based public health change ensuring that future generations of Australians benefit from the powerful insights generated from twin and family designs.