Evaluating the Network Neuroscience of Human Cognition to Improve AI. This project will translate the brain’s inherent complexity into a set of explorable networks that will test the network theory of intelligence, and also be used to drive advances in next generation artificial neural networks. Our approach will catalyse new knowledge regarding how the complexity of the brain gives rise to cognition using innovative analyses inspired by physics and engineering. This fresh perspective on cogniti ....Evaluating the Network Neuroscience of Human Cognition to Improve AI. This project will translate the brain’s inherent complexity into a set of explorable networks that will test the network theory of intelligence, and also be used to drive advances in next generation artificial neural networks. Our approach will catalyse new knowledge regarding how the complexity of the brain gives rise to cognition using innovative analyses inspired by physics and engineering. This fresh perspective on cognition will accelerate understanding of normal cognitive function and also advance the development of advances in artificial neural network performance. Expected outcomes include methods to describe the computational signature of how cognition emerges from dynamic brain network activity and novel AI algorithms. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101129
Funder
Australian Research Council
Funding Amount
$442,000.00
Summary
Synergy between future 21-cm experiments and physical cosmology. The nature of dark matter and formation of the first galaxies are both unsolved mysteries. During the first 500 million years, our universe was filled with hydrogen atoms illuminated by the first galaxies. The 21-cm radiation from this gas encodes properties of unseen galaxies and dark matter during this so-called cosmic dawn. This project aims to build an innovative framework to leverage future 21-cm experiments using The Square K ....Synergy between future 21-cm experiments and physical cosmology. The nature of dark matter and formation of the first galaxies are both unsolved mysteries. During the first 500 million years, our universe was filled with hydrogen atoms illuminated by the first galaxies. The 21-cm radiation from this gas encodes properties of unseen galaxies and dark matter during this so-called cosmic dawn. This project aims to build an innovative framework to leverage future 21-cm experiments using The Square Kilometre Array to observe cosmic dawn, and to forecast the optimal constraints on dark matter physics. Additional outcomes include the largest cosmological simulation of the first galaxies powered by neural networks and improved knowledge of their properties using Bayes' theorem and The James Webb Space Telescope.Read moreRead less
Causal Knowledge-Empowered Adaptive Federated Learning. Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data he ....Causal Knowledge-Empowered Adaptive Federated Learning. Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data heterogeneity across devices and 2) address the real-world challenges when only a subset of devices have labelled data. Expected outcomes and benefits include the theoretical underpinnings and algorithms of causality-based collaborative training of ML models while better preserving the users’ data privacy.Read moreRead less