What co-design has taught us about transformative practice and academic development

This chapter explores lessons about co-design in transformative practice and academic development. Co-design, defined as a purposeful, collaborative, and participatory design process involving multiple stakeholders, has emerged as a pivotal strategy in addressing the challenges of higher education. The Light Touch Program focuses on transformative course design and curriculum development. Through a three-year journey, this study shares insights on the program’s role in enhancing teaching and learning environments, drawing on experiences from educational developers, learning designers, and other stakeholders. The findings underscore three transformative lessons: (1) intentional flexibility is required to meet academics’ diverse needs, (2) co-design facilitates capacity building, and (3) customisable resources accelerate sharing and adoption. These lessons highlight co-design’s potential to foster innovation, adaptability, and inclusive practices in academic settings, contributing to more dynamic and collaborative design processes in higher education

Medical Image Analysis with Less and Noisy Labels

Deep learning has revolutionized medical image analysis, but its success heavily relies on supervised training with large, clean-labeled datasets. Acquiring such datasets is both costly and labor-intensive, often requiring expert annotations from medical professionals. To address this challenge, Medical image analysis with Less and Noisy labels (Med-LN) has emerged as a promising solution, enabling deep learning on less or noisy labeled datasets. However, the challenges posed in the medical imaging domain remain under-explored. In this thesis, we first address the issue of less labeled datasets by leveraging both labeled and unlabeled data. Then, we explore the common issue of noisy labels in medical datasets. Specifically, we focus on three key tasks in medical image analysis under Med-LN conditions: medical image enhancement, medical image segmentation, and medical visual question answering

Twenty years of PMI’s Pulse of the Profession (2006–2025): A review

This review examines two decades of the Project Management Institute’s Pulse of the Profession series (2006–2025), the flagship global survey of project, program, and portfolio management. Forty reports were analysed, comprising 14 annual global editions, 23 thematic studies, and three practitioner-focused outputs. The findings show that Pulse has served both as an industry barometer and as an advocacy instrument. While the central message across all editions is consistent, poor project management wastes resources, the framing of this message has shifted over time: from cost-and-control narratives to capability-driven emphases on agility, digital fluency, power skills, and business acumen. Using text mining (Voyant Tools) and qualitative coding (ATLAS.ti), the study identifies five clusters of project management approaches, governance, process, adaptive, people-centred, and purpose-driven, and traces how PMI’s discourse has repositioned project management as a strategic, human-centred discipline with societal impact. The analysis underscores the value of Pulse as a directional indicator of industry priorities, while also highlighting its limitations as empirical evidence due to shifting metrics, selective transparency, and advocacy framing. For scholarship, this review offers the first comprehensive synthesis of the Pulse series. For practice, it reinforces the importance of governance, agility, and people skills in sustaining performance. For doctoral research, it provides both a typology and a conceptual scaffold for examining how project management approaches contribute to the sustainability and scalability of public health programs

Preclinical research on novel therapies and neurochemistry in intractable epilepsy

Epilepsy is a condition involving underlying hyperexcitability of neurons in the brain leading to various seizure-related phenotypes. However, there remains unmet clinical need in epilepsy for around 30% of patients that remain resistant to treatment. This requires new therapeutic targets to be explored, through the characterization of novel neurochemical pathways that respond to seizures and testing novel candidate molecules for anti-seizure effects in state-of-the-art preclinical models of seizures. Cannabidiol (CBD), a non-psychoactive cannabinoid derived from Cannabis sativa, displays promising anti-seizure effects in patients of Dravet syndrome and Lennox-Gastaut syndrome, two types of epilepsy that are resistant to current medications. The chemical structure of CBD provides a scaffold for developing novel anti-seizure agents. Additionally, this has led to further investigations into the role of the endogenous cannabinoid system and other lipid-related signaling pathways in epilepsy and whether these systems could be leveraged to develop new anti-seizure drugs. The present thesis then aimed to further advance this field of research by identifying novel cannabinoid-related anti-seizure agents and investigating the impact of seizures on the novel lipid signaling mediators called the specialized pro-resolving mediators which assist in brain repair

Multidimensional profiling of cellular interactions in heart failure (HF) patients.

Heart failure (HF) remains a major global health burden, with its subcellular pathophysiology still not fully understood. This thesis establishes an optimised Imaging Mass Cytometry (IMC) platform tailored to cardiac tissue, introducing a novel methodology combining imaging with time-of-flight mass cytometry into cardiac research. The aim is to deepen understanding of the HF tissue landscape through single-cell profiling and spatial analysis. We recruited patients with ischaemic cardiomyopathy (ICM, n=8) and dilated cardiomyopathy (DCM, n=8), collecting apical left ventricular (LV) tissue during LV assist device implantation. A 37-marker metal-conjugated antibody panel specific to cardiac tissue was developed, alongside optimised tissue processing and staining protocols. An automated pre-processing and deep learning cell segmentation pipeline was established to identify cardiac cell types. Using unbiased clustering, we characterised macrophage, T cell, fibroblast, and cardiomyocyte subpopulations, along with other immune and neuronal cells. Spatial interaction data were extracted from fibrotic regions. The IMC platform enabled high-resolution, multiplexed imaging across all markers, offering detailed insight into HF tissue composition. Notably, we identified a previously unreported cardiomyocyte population expressing ACTN2 and podoplanin in ICM patients. DCM hearts showed greater infiltration of pro-inflammatory cells (e.g. M1 macrophages, cytotoxic T cells) and increased lymphatic vessel formation, while ICM tissues had more heterogeneous fibroblast populations. A myofibroblast-like population correlated with scar proportion, suggesting potential as a fibrosis predictor. We also defined distinct cellular niches and interactions. This work demonstrates a powerful IMC approach for single-cell, spatially resolved cardiac profiling. Our findings offer novel insight into HF pathogenesis and highlight cell populations with potential for aetiology-specific therapies

The Enchanted Loom: numinous-like-auras explained through painting

This thesis was motivated by my lived experience of Temporal Lobe Epilepsy (TLE), a condition where enhanced affective associations within the brain result in numinous-like-auras. Through the following question, How through art, can one explore a neurological condition? a heuristic practice-based inquiry developed. The Enchanted Loom: numinous-like-auras explained through painting, alludes to a weaving metaphor that traverses philosophy, paraphysics and science, including pseudoscience, to synthesise diverse, informed and interwoven parts. It proposes that by acknowledging the biological factors of a numinous-like-aura, the discourse in neurology then intersects with that of the numinous. The binding of the fragmentary, subjective experience of TLE is grounded in Rudolf Otto’s supra-rational theoretical structures of the ‘non-rational’ numinous. Within this framework, new ground is proposed that regards the universal characteristics of the origins of the numinous and theories of electromagnetism as being inherent to the numinous dimension. Within this intricate but definable domain of congruent encounters, seminal practice in historical and contemporary painting is contextualised. The studio component of the thesis links and reinterprets material culture of the numinous to neuroscientific concepts concerned with the numinous and TLE. The outcomes traverse the neuro-numinous through a definitive focus on the synthesis of ideograms in relation to neurocognitive structures. Within this realm my paintings are categorised as ‘templates for perception.’ The research contributes to new understandings in the field by establishing that neuroscience enables an appraisal of the numinous where aesthetic reflection disrupts rational structures of thought and forms of art. Thus, the neuro-numinous succeeds by converting cognitive failure into confirmation wherein the mind discovers an unspeakable and wholly other inner domain

A model for neuronal processes underlying colour vision

To provide a physiologically plausible computational model for the development of chromatically selective cells in macaque primary visual cortex. The model comprises of cones, horizontal, bipolar, ganglion, lateral geniculate nucleus and primary visual cortex input layer cells. Each cell was represented by a differential equation and all equations were solved simultaneously to provide time courses of membrane potential and, for spiking cells, impulse rate. Chapter 3 describes the construction of ganglion cell receptive fields. Negative feedback from horizontal cells to cones was built into the model and two notable results emerge. First, the full model can be reduced to a ratio-of-Gaussians model which corresponds more closely with the anatomy and has a temporal component. Second, the ratio-of-Gaussians model shows how centre and surround radii can be calculated from the radii of the optical point spread function, horizontal cell receptive field, and ganglion cell dendritic field. Chapter 4 concentrates on the development of cortical circuits before the onset of vision. Stimulation of the model in this first development phase depends on spontaneous waves of retinal activity. I assumed that the geniculocortical synapse was plastic and Hebbian: positive correlations of lateral geniculate nucleus and cortical responses strengthened the intervening synapse, and negative correlations weakened it. This led to stronger cortical responses, orientation selectivity, and mapping of orientation preference across the visual field. Chapter 5 is devoted to the second phase of development, in which stimuli were scenes from the natural world. The development here was again determined by a Hebbian mechanism, and two types of cortical cell emerged. The first, luminance cells, were best driven by the stimulus component varying in luminance. Luminance/colour cells, by contrast, produced a relatively strong response to equiluminant stimuli

Learning Ability of Deep ReLU Networks: Pairwise Tasks and Gradient Descent Methods

Deep neural networks (DNNs) have become central to modern machine learning due to their strong empirical performance. However, their theoretical understanding—especially regarding generalization—remains limited. This thesis advances the theory of deep ReLU networks through two lenses: pairwise learning tasks and gradient descent methods. For pairwise learning, we study generalization in non-parametric estimation without relying on restrictive convexity or VC-class assumptions. We establish sharp oracle inequalities for empirical minimizers under general hypothesis spaces and Lipschitz pairwise losses. Applied to pairwise least squares regression, our bounds match known minimax rates up to logarithmic terms. A key innovation is constructing a structured deep ReLU network approximating the true predictor, forming a target hypothesis space with controlled complexity. This framework successfully handles problems beyond the reach of existing theories. For metric and similarity learning, we exploit the structure of the true metric. By deriving its form under hinge loss, we approximate it using structured deep ReLU networks and analyze the excess generalization error by bounding the approximation and the estimation errors. An optimal excess risk rate is achieved, marking the first known such analysis for metric/similarity learning. We also explore extensions to general losses. For gradient descent methods, we study GD and SGD for overparameterized deep ReLU networks in the NTK regime. Prior work mainly covers shallow networks; we fill this gap by establishing the first minimax-optimal generalization rates for GD/SGD with deep architectures. Under polynomial width scaling, our results show these methods can match the generalization performance of kernel approaches

Investigation of the Security Impact of Internet Centralization

This thesis investigates how Internet centralization impacts the security of online services, focusing on three problems: digital divides in Australian government services, DNS dependencies of government domains, and privacy in Federated Learning (FL), where each addresses distinct but interconnected aspects of centralization. First, we analyze the DNS dependencies of Australian government domains, identifying potential disparities in service availability between the general population and indigenous communities. By categorizing DNS providers into leading, non-leading, and government-hosted groups, we expose how the digital divide contributes to service unavailability for indigenous domains and increases their vulnerability to outages and attacks. We construct a dataset of Australian government domains to retrieve their DNS providers. Second, we map direct and indirect dependencies through dependency graphs and provide the IP geolocation of DNS providers. We then introduce attacker models by categorizing the attackers' resources and intentions to analyze the implications of DNS dependencies on the vulnerability of different domain groups. Lastly, we address privacy concerns in FL systems, where centralized model aggregation leads to model inversion attacks. We propose ACCESS-FL, a secure aggregation protocol with communication and computation costs as O(1). Experimental results on benchmark datasets (MNIST, FMNIST, and CIFAR) demonstrate that ACCESS-FL significantly reduces computation and communication overhead compared to state-of-the-art methods (Google's SecAgg and SecAgg+) while maintaining comparable model accuracy in honest-but-curious scenarios. This makes ACCESS-FL particularly suitable for large-scale, stable FL environments, such as healthcare systems. In conclusion, this thesis analyses the security consequences of centralization across DNS infrastructure and FL systems to enhance the availability and privacy of online services

Deep Learning-Based Synthesis Algorithms for Positron Emission Tomography (PET) Imaging

Medical imaging plays a vital role in healthcare by enabling non-invasive visualization of anatomical and physiological processes. Among modalities, Positron Emission Tomography (PET) provides metabolic insights crucial for diagnosing cancers such as lung cancer and lymphoma. However, PET’s reliance on radioactive tracers poses radiation risks, particularly during sequential scans used to monitor treatment response. While low-dose PET reduces exposure, it compromises image quality and diagnostic accuracy. This thesis addresses the challenge of synthesizing high-quality PET images from low-dose scans, known as low-to-high PET synthesis. Existing methods enhance image quality but often lack generalizability across dose levels and neglect the longitudinal nature of clinical imaging. Moreover, current CNN-based methods capture local features well but struggle with global dependencies essential for structural consistency. To overcome these limitations, this thesis proposes three novel deep learning-based PET synthesis approaches: SS-AEGAN introduces a self-supervised, adaptive residual estimation GAN to improve generalizability across varying low-dose levels, achieving robust synthesis performance on public datasets. Trans-synGAN leverages baseline PET/CT to guide follow-up PET synthesis, explicitly modeling spatial and metabolic transformations across time points to address sequential scan inconsistencies. Hybrid-CMLP integrates CNNs for local detail extraction with MLPs for global structure modeling, offering improved fidelity and contextual coherence with low computational overhead. Validated on three datasets—including real low-dose PET—the proposed methods consistently outperform state-of-the-art techniques in both image quality and generalizability, providing more reliable, radiation-efficient molecular imaging