UWSpace

UWSpace is the University of Waterloo’s institutional repository for the free, secure, and long-term home of research produced by faculty, students, and staff.

Depositing Theses/Dissertations or Research to UWSpace

Are you a Graduate Student depositing your thesis to UWSpace? See our Thesis Deposit Help and UWSpace Thesis FAQ pages to learn more.

Are you a Faculty or Staff member depositing research to UWSpace? See our Waterloo Research Deposit Help and Self-Archiving pages to learn more.

Photo by Waterloo staff

Communities in UWSpace

Select a community to browse its collections.

Recent Submissions

  • Thumbnail Image
    Item type: Item ,
    An AI-Assisted, Motion-Aware Robotic Framework for Adaptive Ultrasound-Guided High-Intensity Focused Ultrasound Therapy
    (University of Waterloo, 2026-04-21) Taghipour, Alaleh
    High-intensity focused ultrasound (HIFU) is a non-invasive therapeutic modality capable of inducing localized tissue ablation through the precise delivery of focused acoustic energy. Ultrasound-guided HIFU (USgHIFU) offers real-time imaging, portability, and broad clinical accessibility; however, its effective deployment remains challenged by limited interpretability of ultrasound images, patient motion, and the difficulty of tightly integrating perception, planning, and robotic execution within a unified treatment workflow. This thesis presents an integrated, Artificial-Intelligent-assisted (AI-assisted) and robotenabled framework for adaptive USgHIFU therapy. While HIFU has broader clinical applicability across multiple solid tumors, the present work focuses specifically on breast cancer therapy and therefore leverages breast ultrasound data for framework development and evaluation. In the first stage, open-access breast ultrasound images from the Breast Ultrasound Images (BUSI) dataset are processed using a foundation model–based segmentation approach, namely Segment Anything Model 2 (SAM2), to automatically delineate tumor regions. The resulting segmentation masks are converted into geometric boundary representations, from which interior target points are generated at fixed spatial resolution and ordered using a nearest-neighbour (NN) strategy. These targets are exported as trajectory files that are continuously monitored by a robotic control system. A Franka Emika Panda robot executes the planned trajectories using a position-based Cartesian controller, while a geometric safety projection mechanism enforces boundary-aware safety constraints derived directly from the segmented tumor geometry. Motion-aware logic is incorporated into the segmentation pipeline such that detected displacements exceeding a predefined threshold trigger re-segmentation and trajectory regeneration, enabling safe interruption and resumption of robotic execution. In the second stage, the framework is extended to physics-informed ablation planning and experimental validation using a Verasonics ultrasound system. Tumor boundary information obtained from the segmentation pipeline is used as input to high-intensity therapeutic ultrasound (HITU) acoustic simulations to determine optimal focal locations, lesion dimensions, focal depths, and sonication parameters that maximize coverage while minimizing spill-out. These simulation-derived parameters are exported as structured comma-separated values (CSV) files and used to dynamically configure Verasonics sonication settings during experiments. Real-time ultrasound frames acquired during ablation are continuously monitored by the segmentation system, enabling online assessment of ablation extent relative to the tumor boundary and providing a mechanism for early termination in the presence of boundary violations or excessive spill-out. Robotic execution is synchronized with sonication through dwell-based targeting, and volumetric tumor coverage is achieved by iteratively advancing the robot across multiple planar slices. Experimental results demonstrate accurate segmentation of ultrasound tumor images, reliable geometric boundary extraction, uniform interior coverage, and high-fidelity robotic trajectory tracking under safety constraints. Quantitative analysis of coverage and spill-out shows strong agreement between simulation predictions and experimental outcomes. Collectively, this work establishes a closed-loop, motion-aware, and experimentally validated framework that tightly couples AI-based ultrasound image segmentation, robotic control, and acoustic simulation, advancing toward practical autonomous and adaptive USgHIFU therapy systems.
  • Thumbnail Image
    Item type: Item ,
    Graph Neural Network-based Approximate Bayesian Computation for Agent-based Model Calibration of Bacterial Population Growth
    (University of Waterloo, 2026-04-21) Bai, Xianglong
    Approximate Bayesian Computation (ABC) has emerged as a powerful likelihood-free inference framework for model selection and parameter inference in complex biological systems where explicit likelihood functions are intractable or computationally prohibitive. However, the effectiveness of ABC strongly depends on the choice of summary statistics and distance metrics used to compare simulated and observed data. When analyzing time-lapse observations of growing cell populations, the selection of suitable summary statistics often relies on manually designed features informed by domain expertise. Designing such statistics is challenging as they must capture complex spatial, structural, and temporal characteristics of the biological system. Consequently, handcrafted summary statistics may omit relevant information or fail to generalize across datasets. As a result, important information contained in the data may be lost, potentially leading to inefficient inference or biased posterior estimates. This motivates the use of deep learning approaches, such as Graph Neural Networks (GNNs), which can automatically learn informative representations directly from graph-structured data. To address these limitations, this thesis proposes and systematically investigates four novel strategies for integrating deep learning approaches into the Sequential Monte Carlo ABC (ABC-SMC) framework, with a focus on GNNs and Long Short-Term Memory (LSTM) models. These architectures are specifically designed to capture the relational structure of cell populations and the temporal dynamics inherent in time-lapse data. Using GNNs, we encoded spatial interactions among cells through contact edges in graph representations of the biological system. The temporal dynamics of the evolving cell population are captured in two ways. In one approach, LSTM layers are incorporated to model dependencies across successive graph observations in time-lapse sequences. In the alternative approach, we represent temporal relationships directly within the graph structure through lineage edges in a knowledge graph, which explicitly encode parent–daughter relationships between cells over time. We consider two learning paradigms for extracting informative representations from these graphs. In the first approach, graph regression models are trained using mean squared error (MSE) to directly predict model parameters from simulated data. In the second approach, graph embedding models are trained with a triplet loss to learn low-dimensional representations that preserve the similarity relationships among simulations generated from similar parameter configurations. The resulting representations serve as GNN-based summary statistics, replacing conventional handcrafted statistics within the ABC-SMC inference pipeline. Such deep learning approaches belong to the broader class of GNN-based methods for likelihood-free inference, which aim to automatically extract informative features from complex simulation outputs. We evaluate the proposed strategies against a baseline approach relying on classical summary statistics. Inference performance is assessed using two complementary metrics. One is the Kullback-Leibler (KL) divergence between the inferred posterior distributions and the ground-truth parameters. The other is the mean squared distance (MSD) between the inferred and true parameter values. Across all evaluated strategies, the GNN-based summary statistics consistently outperform conventional handcrafted summary statistics for simulation studies. They yield more accurate posterior approximations, as reflected by reduced KL divergence, and more precise parameter estimates, as reflected by lower MSD values. However, the results are less convincing on real data, likely due to model mismatch. Overall, this work demonstrates that replacing handcrafted summary statistics with GNN-based ones can substantially improve likelihood-free inference in complex biological systems, assuming that there is no model mismatch and no unknown noise in observations from real experiments. By integrating GNNs with the ABC-SMC framework, the proposed approach enables the automatic extraction of informative representations from graph-structured, time-evolving population data. The resulting methodology provides a principled strategy for parameter inference, bridging computational simulations and experimental observations through simulation-based model calibration. Although the biological model considered in this study serves primarily as a simple test to develop the inference pipeline, the proposed framework is designed to be readily extended to more complex cases commonly encountered in systems biology.
  • Thumbnail Image
    Item type: Item ,
    Towards Adaptable and Deployable Wildlife Detection from Aerial Imagery
    (University of Waterloo, 2026-04-21) Hsiao, Jayden
    Reliable wildlife monitoring is critical for biodiversity conservation, yet large-scale aerial surveys remain constrained by labour-intensive manual counting and limited model generalization. While camera trap–based approaches are used for species identification and behavioural monitoring at fixed locations, aerial imagery acquired from drones and aircraft enables large-area coverage and is commonly used to estimate population abundance, species distributions, and temporal trends through repeated surveys. However, these workflows remain heavily reliant on manual annotation. Although recent advances in computer vision enable automated wildlife detection, many existing approaches rely on species-specific training data, exhibit poor transferability across environments, and lack mechanisms to incorporate expert corrections into iterative model improvement. These limitations hinder scalability and long-term deployment in operational conservation workflows. We propose OpenWildlife as an open-vocabulary, multi-species wildlife detection framework designed for RGB aerial imagery captured from drones or aircraft, together with a human-in-the-loop (HITL) annotation system that supports incremental model refinement. The framework adapts a language-grounded detection architecture to allow species specification through natural language prompts, enabling flexible identification across terrestrial and marine environments without retraining for each new category. Trained on 15 publicly available wildlife datasets, the model achieves up to 0.981 mAP50 (mean Average Precision at 50% overlap, a standard object detection metric) under fine-tuning and 0.597 mAP50 on seven datasets containing novel species in zero-shot settings, demonstrating generalization to unseen species across diverse vertebrate groups, including mammals and birds. To support practical deployment, the detection model is integrated into a HITL workflow that combines automated pre-labelling, regional human correction, and incremental fine-tuning. While these components are individually well-established, the proposed system integrates them into a unified workflow tailored to aerial wildlife imagery. This design avoids exhaustive full-image annotation in dense scenes and enables expert feedback to directly improve subsequent model iterations. A case study conducted with the Arctic Eider Society on high-resolution aerial surveys of eider ducks in Arctic Canada demonstrates practical impact: the system achieves 77.6% recall with a 22.2% counting error while reducing annotation time by 87.4% compared to fully manual labelling, demonstrating its applicability for semi-automated population abundance estimation from aerial surveys. These results demonstrate that combining open-vocabulary detection with human-in-the-loop learning provides a scalable and adaptable approach for wildlife monitoring, enabling efficient and consistent large-area surveys across diverse species and environments.
  • Thumbnail Image
    Item type: Item ,
    FATal Effect: Investigating causative agents of TDP-43 palmitoylation and mislocalization in ALS
    (University of Waterloo, 2026-04-21) Perry, Cailyn
    Amyotrophic lateral sclerosis (ALS) is a lethal neurodegenerative disease characterized by the progressive loss of upper and lower motor neurons. ALS is highly heterogeneous and has no known singular cause. Of the more than 40 genes associated with ALS, TARDBP stands out, as the encoded protein TDP-43 is known to accumulate in 97% of all ALS cases, regardless of etiology. In these cases, TDP-43 mislocalizes from the nucleus to the cytoplasm resulting in a toxic loss of its native function as an RNA/DNA binding protein, and a toxic gain of cytoplasm accumulation. In neurodegenerative diseases, like ALS, protein aggregation is the most common pathological hallmark. Protein aggregation is often preceded by mislocalization and is an indicator of underlying proteostasis deficiencies. Palmitoylation, the post-translational addition of the 16-carbon fatty acid palmitate, is a reversible lipid modification that, among many things, directs protein membrane trafficking. Palmitoylation is facilitated through the actions of palmitoyl transferases (PATs). Dysregulation of palmitoylation has been linked to proteostasis deficiencies in numerous neurodegenerative diseases, including ALS. Consequently, TDP-43 mislocalization in ALS may be caused by aberrant palmitoylation. This project aims to characterize how TDP-43 localization and palmitoylation are regulated by PATS within the context of ALS. This study confirms that TDP-43 and ALS linked proteins are palmitoylated, and that mutations in TDP-43 result in altered palmitoylation. Understanding how palmitoylation of TDP-43 impacts cellular stress response and recovery within a variety of models will be integral to identifying targets for rescuing proteostasis deficiencies in neurodegenerative disease.
  • Thumbnail Image
    Item type: Item ,
    Pore-Scale Modeling of Platinum Degradation in PEM Fuel Cells
    (University of Waterloo, 2026-04-20) Agravante, Gerard
    Heavy-duty vehicles rely primarily on diesel engines and produce disproportionately higher emissions in the transportation sector. They also lack feasible zero-emission alternatives due to their demanding range and load requirements. Polymer electrolyte membrane (PEM) fuel cells can offer a promising solution due to their long range, low weight, and fast refueling times. However, their application for heavy-duty vehicles is limited by durability issues which limit the fuel cell lifetime. In particular, platinum catalyst degradation in the cathode catalyst layer (CCL) is a critical degradation mechanism. Understanding the mechanisms of platinum degradation through physics-based modeling is essential for developing more durable fuel cells. This thesis first presents an in-depth literature review of physics-based modeling approaches for platinum degradation, classifying the most widely used modeling frameworks and examining how recent studies have utilized and expanded upon them. The review then identified a key gap: the lack of pore-scale models capable of investigating the effects of the CCL microstructure and spatial properties on platinum degradation. To address this gap, a transient pore-scale model of platinum degradation is first developed using pore network modeling. The model couples a performance solver (which simulates the oxygen reduction reaction) with a degradation solver (which simulates the degradation mechanisms of platinum dissolution, oxidation, and mass loss to the membrane). The model is able to reproduce key degradation trends including spatial variations in surface area loss and transient changes in platinum oxide coverage and ion concentration. Importantly, the pore-scale resolution reveals new microstructural insights, such as how constrictions between agglomerates lead to localized degradation and how the length of transport path correlates with platinum loss. Building on the model developed, microstructural and spatial design strategies were systematically investigated. Strategies through modifications in the porosity, agglomerate size, and platinum placement were evaluated for their effects on performance and degradation. It was found that: (1) increasing porosity uniformly or near the membrane reduces platinum loss but worsens performance, although mid-CCL constrictions can minimize this trade-off, (2) reducing agglomerate size enhances performance and has negligible impact on degradation, and (3) increasing platinum loading near the membrane through larger particle sizes promotes more uniform degradation. A combined case study integrating these strategies demonstrates simultaneous improvements in initial performance, total degradation, and degradation uniformity. These results indicate that while individual strategies have distinct trade-offs, combining complementary strategies can retain their advantages while minimizing the drawbacks. Overall, the works presented in this thesis demonstrate the utility of pore-scale modeling for uncovering microstructural insights and identifying mitigation strategies for platinum degradation through the CCL design, which can ultimately contribute to the advancement of PEM fuel cells as a viable clean energy technology for heavy-duty vehicles.