ARC Awards Discovery Projects to ACEMS Researchers

The Australian Research Council announced its first round of 2021 Discovery Projects. A total of 11 ACEMS researchers are named in eight of the projects. The projects are listed below. ACEMS member names are highlighted.

Bayesian inference for psychological theories with intractable likelihood

Professor Robert Kohn, Professor Scott Brown, Dr Minh-Ngoc Tran, Dr David Gunawan (UNSW)

This project pursues breakthroughs which allow important questions of basic and applied science to be addressed using mathematical theories from cognitive psychology. Advances are made through an interdisciplinary effort, combining recent developments in econometric and statistical methods and cognitive science. The outcomes will advance knowledge and open up new avenues for applied research in important aspects of psychology. This research will result in new methods available to the wider scientific community which open up new horizons for understanding basic cognition, and human behavior in many domains. $378,500.00

Mathematical modelling of information flow in social networks 

Assoc Prof Lewis Mitchell, Professor Matthew Roughan (Adelaide)

This proposal aims to develop new mathematical and statistical methods to understand information flow in social networks. By using novel information theoretic techniques, it will create new methods to characterise social information flow in social networks. These tools will allow derivation of fundamental limits of predictability for AI methods applied to digital data. New mathematics of information flow will produce insights into social influence in online social networks. Benefits include: better understanding of how echo chambers may form in social networks, predictive models for how misinformation can spread online such as during an emergency, and a framework for intercomparison of AI methods applied to digital data on individuals. $390,000.00

Expanding and linking random matrix theory

Professor Peter Forrester, Dr Mario Kieburg (Melbourne)

Fundamental to random matrix theory are certain universality laws, holding in scaling limits to infinite matrix size. A basic question is to quantify the rate of convergence to the universal laws. The analysis of data for the Riemann zeros from prime number theory, and of the spectral form factor probe of chaos in black hole physics, are immediate applications. An analysis involving integrable structures holding for finite matrix size and their asymptotics is proposed, allowing the rate to be quantified for a large class of model ensembles, and providing predictions in the various applied settings. The broad project is to be networked with researchers in the Asia-Oceania region, with the aim of establishing leadership status for Australia. $507,648.00

Demographic and evolutionary inferences from large, whole-genome datasets 

Professor David Balding, Dr Yao-ban Chan (Melbourne)

A new data structure for genome-wide datasets has allowed great improvements in the efficiency of genomic data storage and in population genomics simulations, which are crucial to developing and testing mathematical models of population history and species evolution. We will take these advances in new directions, using efficient data structures to dramatically improve inferences about: the demographic histories of populations, rates of genome change, and phylogenetic networks, and we will develop the first inference methods for the multispecies coalescent with recombination. Outcomes will include advances in understanding the evolutionary histories of humans and other species, including pathogens of importance for global health. $405,816.00

Fast flexible feature selection for high dimensional challenging data 

Associate Professor John Ormerod, Dr Garth Tarr, Professor Samuel Muller (Sydney)

The project aims to provide new frameworks for fast flexible feature selection and appropriate modelling of heterogeneous data through structural varying-coefficient regression models. The outcomes will be a series of new statistical methods and concepts enabling more powerful modelling of complex bioscience data. The project will create the science for building reliable statistical models taking model uncertainty into account, impacting how results will be interpreted, and with accompanying software. This will be a significant improvement in the assessment of model confidence in the food and health research priority areas including areas such as meat science, Huntington’s disease, and kidney transplantation.

Interface-aware numerical methods for stochastic inverse problems 

Associate Professor Jerome Droniou, Dr Tiangang Cui, Professor Santiago Badia (Monash)

This project aims to design novel high-performance numerical tools for solving large-scale forward and inverse problems dominated by stochastic interfaces and quantifying associated uncertainties. In real-world applications such as groundwater, these tools are instrumental for assimilating big datasets into mathematical models for providing reliable predictions. By advancing and integrating high-order polytopal schemes, multilevel methods, transport maps, and dimension reduction, this project's anticipated outcomes are highly accurate and cost-efficient numerical schemes, certified by rigorous mathematical analysis. This should provide data-centric simulation tools with enhanced reliability, for engineering and scientific applications. $475,000.00

Enabling Situated Immersive Science Collaboration with Remote Sensing Data 

Dr Selen Turkay, Dr Ross Brown, Dr David Flannery (QUT) 

This project aims to help scientists communicate and collaborate in immersive environments. Fieldwork is more valuable to scientists than looking at abstract remote data, but expense, danger, or inaccessible locations often stand in the way. This project will address this issue by researching and designing immersive environments that combine remote data with visualisations and new interaction tools for science teams to make sense of spatial and temporal aspects of data. Outcomes will include new presentation and interaction methods, an evaluation with geoscientists, and a framework for designing interactive systems that enable situated interactions. Benefits will include helping Australian scientists overcome distance in their research.  $501,000

Statistical Analysis of State-Dependent Government Spending Multipliers

Dr Seojeong Lee, Dr Bonsoo Koo (UNSW)

This project aims to provide a new statistical analysis of the government spending multiplier by acknowledging that government spending is the sum of sectoral spending which has heterogeneous effects on the economy. An added complication is that the multiplier can also be state-dependent, meaning that its magnitude can differ across recessions and expansions. Expected outcomes of this project include a better understanding of the components of the multiplier by novel decomposition and the development of a new statistical test for the state-dependency of the multiplier. This should provide significant benefits to researchers by bringing in new tools and insights and to policymakers by providing timely guidance on fiscal policies. $336,939.00

  • For the complete list of all the funded Discovery Projects, head to the ARC Website.