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Recent Submissions

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    Laser Directed Energy Deposition Repair of Laser Powder Bed Fusion Maraging Steel Components
    (University of Waterloo, 2026-04-17) Orchard, Benjamin Ferid
    High-pressure die casting (HPDC) tooling inserts produced via laser powder bed fusion (LPBF) improve thermal balance and extend tool life, but severe thermomechanical cycling inevitably causes surface degradation. While conventional welding repairs introduce excessive heat, laser directed energy deposition (LDED) offers near-net-shape restoration with superior thermal control. However, the mechanical integrity of the LDED deposit-substrate interface remains a critical concern. This thesis investigates strategies to optimize metallurgical bonding at the repair interface of LPBF-produced HPDC inserts. Simulated repair coupons with varying sidewall inclination angles were evaluated using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) to assess defect removal geometries and toolpath parameters. In addition, peak-aged LPBF substrates repaired via LDED were tested for tensile performance and microhardness response to post-repair heat treatment. Results demonstrate that groove sidewall inclination angles of 45–50° produce acceptable bonding under baseline parameters. Steeper angles require contour pass assistance, with power-based energy modification assessed to be effective in promoting sidewall fusion and suppressing geometric deformation. Tensile testing of as-repaired coupons showed intermediate performance (ultimate tensile strength (UTS) = 1482.5 MPa) with consistent interfacial failure. Post-repair peak aging caused interfacial degradation (UTS = 679.80 MPa), attributed to wire electrical discharge machining (EDM) recast layer contamination and Ti-Al oxide inclusions at the interface; heat treatment successfully restored deposit hardness to 53.8 HRC. Ultimately, this work demonstrates that optimizing groove geometry and contour pass energy is vital for LDED repair efficacy, and it highlights that rigorous substrate surface preparation is a prerequisite to achieving mechanically sound repairs in the peak-aged condition.
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    Advancing microbial risk management through strategic integration of risk assessment and water supply operations
    (University of Waterloo, 2026-04-17) de Brito Cruz, Dafne
    Controlling risks posed by microbial contaminants is a key focus for the provision of safe drinking water. Increasing threats to water supply systems—such as rapid water quality changes associated with climate-shocks (e.g., wildfires, heavy precipitation, etc) as well as infrastructure failures and treatment deficiencies—increasingly challenge microbial risk management and underscore the need for resilience in safe water supply. The goal of this research was to advance drinking water microbial risk management through integration of risk assessment approaches and real-world water supply treatment operations scenarios. Quantitative and qualitative risk-based frameworks were developed to address gaps in knowledge and practice related to monitoring, treatment decision-making, risk assessment and treatment operations. Routine pathogen monitoring programs and the use of monitoring data in risk assessment for treatment decision making were addressed using data from a full-scale water treatment plant. Practical guidance was provided about monitoring and associated risk-based treatment requirements including methods for choosing sampling locations and frequency. Policy contributions involved definition of treatment sufficiency compliance rules and evidence for inclusion of Giardia in protozoa monitoring programs. This work underscored that data collection should be tailored to best represent water entering treatment plants so their use in risk assessment can best inform treatment decisions and maximize return on investment. While this may seem obvious, how to best represent system-specific attributes in monitoring design and implementation is often far from obvious. To shift from routine operations to extreme events, a scenario-based quantitative microbial risk assessment (QMRA) framework was applied in a case study involving a wastewater spill upstream of a drinking water intake, in which urgent risk-informed treatment decisions were needed. Conducting comprehensive and mathematically complex QMRA is not practical when time-sensitive decisions are needed. Thus, the simplified framework developed here demonstrated how preliminary risk assessment can be conducted for short-term, rapid source water quality degradation events and used to inform operational decisions and improve drinking water system response to adverse events. Filtration plays a central role in microbial risk management during drinking water treatment. Although filtration plants include multiple filters, risk is typically evaluated based on individual filter performance. A model was developed to evaluate how dynamic, concurrent operation of multiple filters affects plant-scale filtration performance and thus microbial risk. It was demonstrated that operational response strategies should prioritize improving performance when it is “low” rather than maximizing performance when it is already “high” because microbial treatment performance is typically expressed on a logarithmic scale. Practical implications of several design or operational decisions for microbial risk management such as the number of filters in operation, backwash staggering schedules and filter-to-waste operation were presented. Plant-scale risk assessment is essential to microbial risk management because risk is driven by the collective and dynamic operation of multiple treatment units. Yet, current practice and regulatory guidance do not always acknowledge plant scale considerations. Linking several aspects of water supply integrally related to risk, the effect of dependence between model inputs in drinking water QMRA was then evaluated. Hypothetical scenarios and data from a full-scale water treatment plant were used to demonstrate that ignoring dependence can over- or underestimate risk, sometimes substantially (e.g., more than an order of magnitude), and mislead decision-making. A framework was developed for characterizing the potential for biased interpretation of risk when assumed independence that is typical in QMRA practice is not valid. Finally, the themes and insights described above were connected to more broadly highlight the inextricable linkage between microbial risk assessment and resilience in safe drinking water supply. The concept of operational resilience was defined as a water supply system’s capacity to limit the risk consequences of insufficient treatment performance when adverse events perturb it. Then, hypothetical case studies were used to demonstrate that resilience description must explicitly reflect risk to be an actionable tool. These foundations can be used to develop tools to operationalize resilience in the future. In addition to contributions to advancing risk and resilience sciences and drinking water policies, this research provides actionable knowledge to water utilities for improving decision-making during challenging conditions, assessing microbial risks more accurately and increasing the resilience of treatment operations.
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    DSCode Comparator: An Interactive Interface for Comparing Models and Evaluating Code for Data Science Tasks
    (University of Waterloo, 2026-04-17) Yu, Xinxin
    Code-generating models are increasingly used to support data science tasks. However, reviewing and validating their outputs remains largely manual and time-consuming, requiring users to understand how generated code works and to assess its quality and correctness. Rather than eliminating effort, these models often shift user work from writing code to verifying it. This challenge is further compounded by the fact that different models frequently produce diverse solutions with varying levels of effectiveness, making systematic comparison and evaluation difficult. To address these challenges, this thesis presents DSCode Comparator, an interactive system designed to support code understanding, evaluation, refinement, and comparison in data science workflows. The system enables users to examine code at multiple levels of granularity, ranging from individual lines of code to complete solutions across different prompts and tasks. DSCode Comparator incorporates an automated annotation pipeline that analyzes generated code and provides structured, line-level explanations to facilitate rapid comprehension. In addition, the system evaluates code quality along multiple functional and pragmatic dimensions, including efficiency, readability, usability, and resource usage. Beyond individual code inspection, DSCode Comparator supports comparative analysis across models by aggregating annotations and evaluation results into compact summaries that highlight key differences in behavior and performance. Through a combination of empirical evaluation and user studies with data science practitioners, this thesis demonstrates that the proposed approach improves users’ ability to understand, compare, and refine code generated by large language models, reducing verification effort while supporting more informed decision-making in model-assisted programming.
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    High-Dimensional Maximum Likelihood Estimation of Multi-Spiked Tensor PCA
    (University of Waterloo, 2026-04-17) Seebach, Lily
    We study the maximum likelihood estimation of the multi-spiked tensor PCA problem. In particular, the tensor of interest is the sum of a low-rank tensor and a tensor whose entries are independent and identically distributed standard Gaussian random variables. The low-rank tensor is a linear combination of rank-one tensors scaled by signal-to-noise ratios. The recovery of the signal vectors (which determine the rank-one tensors) is known as the multi-spiked tensor PCA problem. We prove a variational formula for the high-dimensional limit of the maximum likelihood estimation of the planted signals. This formula is achieved using conjectured results regarding the constrained ground state energy of the spherical mixed vector p-spin model from statistical physics. In this setting, we show that the high-dimensional limit is equivalent to the maximization of an infimum problem with additional penalty terms. This limit acts as a basis for the analysis of the maximum likelihood estimators performance and the investigation of the necessary conditions for their success.
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    Higher Education Sustainability Governance: The Bidirectional Flow of Ideas, Influence, and Power in University Sustainability Efforts
    (University of Waterloo, 2026-04-17) Dickson, Brandon
    Given our present sustainability crisis, there is a need for actors across levels to become actively involved in sustainability action if meaningful changes are to be made. Past work has noted that universities recognize their important societal role as sustainability actors and research has also begun to emerge which investigates the role and impact of universities in sustainability. While the literature is evolving in this area, research on the role of universities and the international organizations which govern them in the broader context of global sustainability governance remains a relatively unexplored area of study. As such, this dissertation explores the two-way exchange of global sustainability governance agendas between international groups and universities. It asks: Across various levels of governance, how and why do universities engage with global sustainability agendas and regimes and what are the implications for universities’ priorities and actions in sustainability? This dissertation adopts a multiple manuscript approach which includes three empirical articles, each of which answers a piece of the above question. Both individually and taken together, these chapters make a substantial contribution to the field of higher education sustainability governance. The first manuscript combines a thematic policy analysis with executive interviews to investigate the sustainability priorities and drivers of these priorities in universities across Canada. The second manuscript offers a quantitative evaluation of the submission patterns of 1,960 universities to the largest global higher education ranking organization, the Times Higher Education Impact ranking. The third combines a discourse analysis of sustainability reports and surveys to investigate discursive transfer between universities and the largest global university sustainability rating program the Association for the Advancement of Sustainability in Higher Education’s (AASHE) Sustainability Tracking Assessment and Rating System (STARS). Together the findings from these manuscripts point to two major contributions to the higher education sustainability governance literature: 1) International organizations and directives have an impact on universities’ sustainability action through soft governance; and 2) Global higher education sustainability governance is not solely determined by one organization, and priority areas and action in sustainability are not just top down, but also reflect universities’ input, interpretation and responses to global higher education sustainability governance. This dissertation provides important context to the way that global governance is carried out in the higher education sustainability governance context and also explores the unique two-way and contested flow of norms and rules between actors and global sustainability agendas. This work will be of interest to scholars of global governance, higher education and sustainability, as well as practitioners and policy makers in the higher education and sustainability spaces.