UWSpace

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Recent Submissions

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    Counterfactual Data Augmentation for Regression
    (University of Waterloo, 2026-01-23) Mohebbi, Hossein
    Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. While data augmentation has revolutionized fields such as computer vision and natural language processing by leveraging domain-specific symmetries, effective techniques for tabular regression remain elusive. Existing approaches, ranging from geometric interpolation to deep generative models, often fail to preserve the underlying noise structure of the data, leading to the generation of unrealistic samples that can degrade predictive performance. This thesis proposes a novel framework called Counterfactual Residual Data Augmentation (CRDA). Our method is founded on the theoretical principle of Residual Invariance, which posits that once a regressor has modeled the systematic component of the data, the remaining residual noise often remains stable under small perturbations of carefully selected features. We exploit this invariance to synthesize valid counterfactual samples, which are data points with perturbed features but preserved residual noise. We formalize this process through the lens of structural causal models, establishing conditions under which the residual is conditionally independent of specific feature subsets. We provide a practical, model-agnostic algorithm that integrates feature selection heuristics and statistical safety checks to ensure augmentation is applied only when empirically beneficial. Through extensive evaluation across diverse benchmark datasets, we demonstrate that CRDA consistently reduces test error in data-scarce regimes. Specifically, our method reduces the Mean Squared Error (MSE) of Multi-Layer Perceptrons by an average of 22.9% and XGBoost regressors by 6.4%. Furthermore, comparisons against state-of-the-art baselines, including Mixup variants and diffusion-based generative models, reveal that CRDA offers a more robust and statistically grounded remedy for noise-prone, small-sample regression tasks. Finally, we provide a production-ready, open-source implementation of our framework to encourage applications in real-world tabular regression tasks.
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    A Staggered Grid-Based Variational Approach for Modeling Elastic Deformation
    (University of Waterloo, 2026-01-23) Dolny, Brooke
    Elastic deformation is often simulated in computer graphics using the Finite Element Method on tetrahedral meshes. However, generating a tetrahedral mesh can be complicated and expensive. When a hierarchy of meshes is needed (for example, with a progressive or multigrid method), generating this set of hierarchical meshes is a time-consuming process. However, in other areas of physics simulation, such as fluid simulation, the use of staggered grids and finite differences is much more common. The application of adaptive or multigrid methods to grid-based simulations is trivial in comparison. By applying the variational method of Batty et al. from fluid simulation to the staggered grid-based elasticity simulation method of Zhu et al., we produce a method that accurately solves the linear elasticity partial differential equations with free boundary conditions solved implicitly. In this work, we derive a variational formulation for the linear elasticity partial differential equations on a staggered grid. We derive both the static and dynamic forms of the minimization problem, and their subsequent discretizations. Our method only requires an indicator function that specifies the interior and exterior of the solid to be simulated, and does not require any information about the normals of the object’s surface. Furthermore, our method retains the simplicity and sparsity of the basic staggered grid finite difference scheme, but supports non-axis-aligned boundaries without the need for boundary-conforming meshing. We apply our method to several examples of uniform and nonuniform objects under different deformations, demonstrated with both static and quasi-static simulations. We also compare our results with analytical solutions to the linear elasticity partial differential equations to show the accuracy of our method and that our method converges well with grid refinement.
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    Rewriting the City: Disruption as Cultural and Spatial Practice in Amman, Jordan
    (University of Waterloo, 2026-01-23) Hijazi, Nadine
    “Rewriting the City” examines how everyday informal spatial practices in Amman—such as street vending, ad-hoc construction, and mobile economies—are framed as disruptions by state-led planning and municipal authorities, while functioning as strategies of livelihood for those who depend on them. Operating outside formal architectural authorship, these practices remain integral to how the city is inhabited, serviced, and sustained. The thesis investigates how such practices come to be labelled as “disruptive” within planning and regulatory discourse, and how this designation frames them as problems of order rather than as intentional spatial practices. It argues that disruption is not an inherent quality of these acts, but a relational one: they are deemed disruptive only within systems that prioritize legibility, permanence, and control. Therefore, what is disrupted is not urban order, but dominant frameworks that define architecture, legality, and spatial authorship. Focusing on markets, sidewalks, streets, and mobile economic infrastructures across East and West Amman, the research analyses how informal practices operate within, alongside, and against municipal planning and governance. The East-West divide is approached as a spatial condition produced by uneven infrastructure investment, zoning practices, and histories of migration and displacement, revealing how regulation itself produces uneven access, visibility, and legitimacy. The thesis reframes disruption as persistence: the repeated, adaptive occupation of space that sustains livelihood under constraint. These practices actively shape public space, organize social relations, and maintain systems of interdependence through deliberate responses to uneven development, economic precarity, and regulatory constraint. Methodologically, the thesis employs ethnographic research, drawing on observation, storytelling, and spatial documentation to examine how space is transformed through temporality, mobility, material decisions, bodily labour, and repeated use. By reading these practices as architectural, the thesis expands the discipline’s scope and responsibility. It challenges the association of architecture with permanence, capital, and professional authorship, and instead positions architecture as a contingent and relational practice embedded in agency, labour, and lived experience.
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    Content-Based Course Recommendation
    (University of Waterloo, 2026-01-23) Elfayoumi, Doaa
    Choosing university courses can be overwhelming for students, especially in large institutions where hundreds of options are offered each term. Motivated by these observations, I envisioned developing a system that could help students identify educational pathways aligned with their goals and aspirations. By leveraging the University of Waterloo’s course and student data, this research aims to design a recommendation system that suggests courses based on students’ interests and desired career outcomes. To address several challenges, including the lack of aggregate historical enrollment data, this thesis presents a new AI-based course recommendation system designed specifically for the University of Waterloo. The system combines a knowledge graph, semantic similarity techniques, and a custom \textit{interestingness} score to recommend courses that are not only eligible but also meaningful and relevant to the student. The knowledge graph models relationships between courses, programs, prerequisites, terms, and subjects, allowing the system to reason about eligibility in a transparent way. To capture course similarity, I compare two embedding models, sBERT and OpenAI embeddings, and evaluate them by examining their similarity matrices and observing how subjects naturally cluster together. Based on these results, sBERT was selected for the final system due to its strong performance and ability to run locally without API costs. The system also integrates a personalized scoring method that considers prerequisite depth, program requirements, and students’ enrollment history, helping surface courses students might not otherwise discover. This thesis also explores how Large Language Models (LLMs), such as ChatGPT, can support recommendation tasks. Recent studies show that LLMs can enhance existing recommenders through feature extraction, prompt-based ranking, explanation generation, and improved handling of cold-start cases. In this project, LLMs are used as a complementary tool, primarily for generating code from text that represents course prerequisites. The resulting system embodies a hybrid AI approach that blends LLM-driven semantic understanding with knowledge-graph-based reasoning to deliver more intelligent, context-aware recommendations. Overall, this work demonstrates a practical and scalable approach to course recommendation. By combining structured academic data with semantic similarity and AI-driven reasoning, this research shows how natural language processing, large language models, and knowledge-graph reasoning can come together to create an adaptable and effective framework for educational recommendation systems.
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    Municipal Interpretations of Indigenous-Settler Reconciliation in Planning for Urban Redevelopment and Regeneration
    (University of Waterloo, 2026-01-23) Ellis-Young, Margaret
    Municipalities in settler-colonial countries such as Canada, Australia, and New Zealand are placing new emphasis on improving Indigenous-settler relations and addressing colonial injustices in the city, in discourse if not in practice. In Canada, municipalities increasingly identify comprehensive planning projects that define future change and (re)development in the city as a space through which to advance these ‘reconciliation’ objectives. However, such projects are also intertwined with gentrification outcomes, outcomes that include Indigenous displacement, dispossession, and erasure. While a growing body of scholarship underlines these settler-colonial dimensions, it is unclear if such connections are made in practice as municipal planners turn their attention to both advancing Indigenous-settler reconciliation and mitigating gentrification-induced displacement. This dissertation deepens emerging dialogue between gentrification scholarship and literature on settler-colonial urbanism and Indigenous recognition as it examines tensions between gentrification, reconciliation, and displacement mitigation within municipal comprehensive planning. To identify the continuity and/or disruption of colonial-capitalist relations therein, I interrogate 1) how reconciliation discourses are translated into area redevelopment plans, 2) how municipal planners represent reconciliatory planning practice, and 3) how planning responses to gentrification concerns address the colonial dimensions of displacement. The research looks at comprehensive planning projects in cities across Canada, with a particular focus on Vancouver and Montréal. I draw on critical discourse analyses of both project documents and interviews with municipal planning staff and other relevant actors. The findings reveal that municipal planners negotiate multiple colonial-capitalist ‘boundaries’ at the nexus of redevelopment and reconciliation: those of Indigenous recognition, existing planning structures, and status quo regeneration objectives. While these boundaries are often reproduced as planners look to advance reconciliation and mitigate displacement within their constraints, more transformative policies, approaches, and mentalities are also beginning to emerge. The research expands on the (im)possibilities of state-led reconciliation through a planning lens, nuances the dynamics of Indigenous recognition in planning within a new context, and provides insight into discursive and policy shifts regarding gentrification and displacement, including limitations therein. It also underlines the importance of building planners’ motivations and capacities to disrupt colonial-capitalist planning relations.