Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inf ....Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inferential quantities in an incremental manner. The proposed stochastic algorithms encompass and extend upon a wide variety of current algorithmic frameworks for fitting statistical and machine learning models, and can be used to produce feasible and practical algorithms for complex models, both current and future.
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Learning the meso-scale organization of complex networks. This project aims to model and learn the organization of online social networks. We will combine mathematical models, inference, and domain knowledge from computational social sciences to obtain interpretable descriptions of the role groups of users play in the network. The expected outcomes are new mathematical models and computational methods that learn from data how to best decompose a complex network into building blocks and their int ....Learning the meso-scale organization of complex networks. This project aims to model and learn the organization of online social networks. We will combine mathematical models, inference, and domain knowledge from computational social sciences to obtain interpretable descriptions of the role groups of users play in the network. The expected outcomes are new mathematical models and computational methods that learn from data how to best decompose a complex network into building blocks and their interactions, linking connectivity to function. This should provide benefits to industries and policy makers interested in how information spreads in social media, including the critical questions of understanding the mechanisms contributing to political polarization and fragmentation.Read moreRead less
Ultra-sensitive 3D molecular assays using total body PET and deep learning. Recent advances in biomedical engineering have led to the development of Total Body Positron Emission Tomography (TB-PET), the most sensitive imaging device to date. Despite these impressive engineering advances, computational methods lag far behind and model-based approaches cannot deal with the complexity or volume of data these systems produce. We will develop new computational methods based on deep learning and stati ....Ultra-sensitive 3D molecular assays using total body PET and deep learning. Recent advances in biomedical engineering have led to the development of Total Body Positron Emission Tomography (TB-PET), the most sensitive imaging device to date. Despite these impressive engineering advances, computational methods lag far behind and model-based approaches cannot deal with the complexity or volume of data these systems produce. We will develop new computational methods based on deep learning and statistical methods that fully exploit the richness and complexity of the data generated by TB-PET, enabling 3D quantitative assays of molecular processes throughout the entire human body with unparalleled sensitivity. The technology we create will open up new capability for the study of complex physiological systems.Read moreRead less
Optimising disease surveillance to support decision-making. COVID-19 has demonstrated the critical role of epidemic data and analytics in guiding government response to pandemic threats, reducing disease and saving lives. The demand for epidemic analytics for response to threats of national significance will only grow. The goals of this project are to 1) determine the combination(s) of surveillance methods that provide the most useful data for epidemic analysis and 2) translate these findings in ....Optimising disease surveillance to support decision-making. COVID-19 has demonstrated the critical role of epidemic data and analytics in guiding government response to pandemic threats, reducing disease and saving lives. The demand for epidemic analytics for response to threats of national significance will only grow. The goals of this project are to 1) determine the combination(s) of surveillance methods that provide the most useful data for epidemic analysis and 2) translate these findings into the blueprint for a next-generation infectious disease surveillance system for Australia. We will use a simulation-evaluation approach, coupling methods from infectious disease modelling with those from information theory optimal design. Outcomes will enable more tailored and effective pandemic response.Read moreRead less