Theses

Permanent URI for this collectionhttps://uwspace.uwaterloo.ca/handle/10012/6

The theses in UWSpace are publicly accessible unless restricted due to publication or patent pending.

This collection includes a subset of theses submitted by graduates of the University of Waterloo as a partial requirement of a degree program at the Master's or PhD level. It includes all electronically submitted theses. (Electronic submission was optional from 1996 through 2006. Electronic submission became the default submission format in October 2006.)

This collection also includes a subset of UW theses that were scanned through the Theses Canada program. (The subset includes UW PhD theses from 1998 - 2002.)

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Multistroke Character Recognition Using Orthogonal Polynomial Representations
(University of Waterloo, 2026-06-30) Cheriakara Joseph, Arun
This thesis studies stroke grouping for online word-level handwriting recognition of Latin letters and digits using orthogonal polynomial representations of pen strokes. A word arrives as an ordered sequence of pen-down strokes, and the system has to decide which strokes belong to which character before it can decide what each character is. At the word level the problem is harder than for isolated characters: the right grouping of strokes depends on what the characters turn out to be, and the right characters depend on how the strokes are grouped. Most existing systems commit to one segmentation and use whatever that segmentation outputs, which can lead to wrong results. The difficulty is sharpened by characters drawn with multiple strokes, by variation in stroke order between writers, and by several letter pairs and letter/digit pairs that share the same shape. This thesis describes an online word-level recognition pipeline built on orthogonal polynomial representations of multistroke characters. Each pen stroke is re-parameterized by arc length, and its coefficients are projected onto an orthogonal Legendre basis of degree eleven, giving a fixed-length coefficient vector per stroke. For multistroke characters, the per-stroke vectors are concatenated into a single feature vector. Because all strokes in a character are normalized together against a shared bounding box, this block-concatenated representation captures the relative position and scale of the strokes within the character, but it does not directly encode every pairwise relationship between strokes. A probabilistic gap model generates up to six candidate groupings per word, and each candidate character group is normalized in a common bounding box before projection. The resulting vectors are matched against a reference database of 76{,}428 samples across 62 character labels, organized into 3{,}237 classes. Classification runs in two stages: a centroid-and-radius heuristic prunes the candidate pool to fifty classes, and a label-pooled $k$-nearest-neighbour stage then ranks the seven closest samples per label by distance to the convex hull of those samples. The pipeline is evaluated on the UniPen word collection drawn from the 62-character Latin-plus-digits alphabet.
Risk Sharing with Distortion Risk Measures Beyond Risk Aversion
(University of Waterloo, 2026-06-30) Ren, Qinghua
This thesis studies optimal risk sharing among multiple agents whose preferences are represented by distortion risk measures, equivalently Yaari dual utilities. The central question is how to characterize Pareto-optimal risk allocations when agents may have heterogeneous risk preferences, including risk-averse, risk-seeking, and behavioral attitudes toward risk. Particular attention is paid to the dependence structure of optimal allocations and to the geometry of the Pareto frontier. The first part of the thesis develops counter-monotonic risk sharing as a counterpart to the classical comonotonic theory. Comonotonicity represents positive dependence and is fundamental in risk sharing among risk-averse agents, while counter-monotonicity represents an extreme form of negative dependence and arises naturally for risk-seeking agents. Chapters 2 and 3 analyze this counter-monotonic structure for distortion risk measures, moving from a homogeneous setting with a common distortion function to a heterogeneous setting with different distortion functions. Inf-convolution is an important tool for studying Pareto optimality. Using this tool, Chapters 2 and 3 compare the usual formulation with variants that restrict allocations to be comonotonic or counter-monotonic, derive explicit formulas for risk-seeking agents, and illustrate the formulas through a portfolio manager’s problem. Chapter 4 studies markets with mixed risk attitudes, where risk-averse and risk-seeking agents coexist. This setting is more challenging because neither the usual comonotonic arguments for risk-averse agents nor the counter-monotonic arguments for risk-seeking agents apply directly to the whole market. The chapter establishes a reduction theorem showing that the general multi-agent problem can be reduced to a two-agent problem between representative risk-averse and risk-seeking agents. Based on this reduction, the chapter further studies the existence of optimal allocations, identifies cases in which the inf-convolution is unbounded, and derives explicit solutions for piecewise linear distortion functions and Bernoulli-type aggregate risks. Chapter 5 studies Pareto optimality beyond universal risk aversion, with emphasis on constrained allocation problems. Feasibility constraints, such as nonnegative allocations, are natural in insurance and reinsurance, but they change the geometry of the risk-sharing problem and may limit the applicability of weighted-sum methods. The chapter reduces the two-agent Pareto problem to a one-parameter family of constrained optimization problems. For Bernoulli aggregate risks, the Pareto frontier admits a convex-envelope characterization and can be attained by three-atom allocations. For two-point aggregate risks, finite-atom structural results are developed, showing that efficient allocations can be represented by a bounded number of payment levels. These results provide both theoretical insight into non-risk-averse risk sharing and a tractable framework for numerical computation.
LLM-Based Frameworks for Information Retrieval Evaluation
(University of Waterloo, 2026-06-29) Upadhyay, Shivani Jayantkumar
Evaluating information retrieval (IR) systems requires a reference that captures what correct or relevant output looks like, as well as a mechanism for determining whether a system’s output matches that reference. For lexical retrieval systems, both requirements are relatively straightforward. Systems rank documents by term overlap, pooling produces a judgment file that covers most documents any system is likely to return, and determining relevance reduces to a simple membership test against that file. This evaluation paradigm relies on the assumption that relevance can be detected through surface-form overlap. When retrieval moves beyond that assumption, the framework begins to break down. Retrieval-augmented generation (RAG) systems strain this setup by synthesising free-form natural language responses from retrieved evidence. A gold answer set constructed before system execution cannot anticipate every correct phrasing, so even semantically correct outputs can fail under lexical matching. Dense retrieval systems encode queries and documents as vectors, retrieving relevant documents that might not share vocabulary with the query. Under pooling-based evaluation, these documents never receive human judgments and are instead assigned a default relevance grade of zero. Together, these failures highlight the limits of surface-form evaluation and point to the need for judgment mechanisms that reason directly about meaning. This thesis investigates whether large language models (LLMs) can fill this gap by contributing three frameworks across successive layers of the evaluation pipeline. The first contribution is an open-source QA evaluation framework that combines chain-of-thought (CoT) prompting with self-consistency decoding using instruction-tuned LLMs. When evaluated across 12 systems on NQ-open, it matches zero-shot GPT‑4 in rank correlation with human judgments while using a model more than an order of magnitude smaller, demonstrating that prompting strategy can matter as much as scale. The second contribution is a framework for patching incomplete relevance judgment sets by assigning four-level TREC-style labels to unjudged query-passage pairs via few-shot prompting. When evaluated across five TREC Deep Learning Track collections at removal rates varying from 10 to 90%, it substantially improves system ranking fidelity over the standard practice of treating unjudged documents as non-relevant. The third contribution is UMBRELA, which is a fully automated open-source relevance assessment framework deployed in the TREC 2024 RAG Track across 301 topics, achieving run-level Kendall's tau >= 0.86 against fully manual assessment. All frameworks are released as open-source tools to support reproducible and scalable IR evaluation.
Investigation of Thermal Behavior in Combustion of Al/Fe3O4 Nanothermite Film
(University of Waterloo, 2026-06-29) Liu, Jingtian
Aluminum-based nanothermites are promising energetic materials for rapid heat release and microscale combustion applications, but the mechanisms governing their combustion morphology and flame propagation remain incompletely understood. This thesis investigates Al/Fe3O4 nanothermite thin films, focusing on two controlling factors: equivalence ratio (ER) and particle morphology. Al/Fe3O4 thin films provide a suitable nanothermite platform for studying the transition between destructive multiphase burning and near-gasless condensed-phase propagation due to their high energy release and relatively limited gas production. Specifically, the work examines how ER drives the transition between destructive particle-ejection combustion and structurally preserved near-gasless propagation, and how core–shell (CS) and physically mixed (PM) architectures modify the combustion event. Polymer-assisted Al/Fe3O4 thin films were fabricated with controlled ER values and particle morphologies. Their combustion behavior was characterized using synchronized high-speed optical imaging and infrared thermography, combined with post-combustion SEM/EDS analysis and inverse thermal modeling. For PM films, ER < 3 produced destructive combustion with particle ejection, localized hot spots, and partial removal of the energetic layer. In contrast, ER ≥ 3 led to preserved combustion, where the reacted layer remained attached to the substrate and formed a measurable cooling zone. This transition indicates that increasing ER improves condensed-phase continuity and shifts the combustion mechanism from reactive sintering to diffusion-driven reaction. Particle morphology also strongly affected flame propagation. At ER = 3, CS films propagated faster than PM films, with velocities of 11.93 cm/s and 6.11 cm/s, respectively. The inverse-modeled reaction rate of CS films was also more than twice that of PM films, indicating stronger reaction–transport coupling due to improved fuel–oxidizer contact and shorter transport distances. Cooling-zone analysis showed that preserved reacted layers act as thermal reservoirs, redistributing heat toward the reaction and preheating zones and contributing to flame-front stability. Overall, this thesis demonstrates that both ER and particle morphology can be used to tune combustion mechanism, propagation behavior, and post-combustion structure in Al/Fe3O4 nanothermite thin films.
A STUDY OF THE CLASSIFICATION AND QUANTIFICATION OF MICROPLASTICS THROUGH RAMAN SPECTROSCOPY AND MACHINE LEARNING
(University of Waterloo, 2026-06-29) Hogan, Úna Elizabeth
Synthetic polymers, or ‘plastics’, have become an unavoidable, necessary and ubiquitous part of modern human life. Their low cost, tunable properties, durability and ease of manufacture have led to plastics use in almost every part of day-to-day life including food packaging, car manufacturing and single-use sterile medical equipment. The durability of these plastics, while an advantage in their operational life, results in substantial longevity upon their disposal. After they have been discarded, many plastic particles can exist for up to 1000 years in the environment before their eventual breakdown. Their continued use and disposal as the global population increases have led to a large accumulation of discarded plastics throughout the world. A substantial amount of these exist in sizes of 5 mm or less, and are classified as ‘microplastics’, small pervasive pollutants that have been detected in food, drink, and inside human bodies. It is necessary for researchers to determine suitable ways of characterising and quantifying microplastic particles to increase understanding of their behavior and makeup. Expanding knowledge of the sources, abundance and variety of these particles within the environment can lead to a more comprehensive understanding of the issue of microplastics pollution. The use of Raman spectroscopy as an analytical technique for classification of microplastics particles has emerged as an efficient and accurate tool for characterisation. Traditional validation of Raman spectra using library searches and comparison to reference spectra, however, is often inadequate when the plastics have faced significant environmental degradation, which can alter their Raman spectrum. Alternative validation methods such as machine learning are becoming more widely used as superior techniques to traditional library searches, and have proven fast, effective and cheap ways to identify microplastic particles from their Raman spectra. This thesis describes iterative development of machine learning based methods to semi-automate identification of microplastic particles using Raman spectroscopy.
Benthic macroinvertebrate assemblages and freshwater food webs of beaver-impounded streams in the eastern Canadian Arctic
(University of Waterloo, 2026-06-29) Gao, Katelyn
As circumpolar warming facilitates the shrubification of Arctic landscapes, the distribution of North American beavers (Castor canadensis) in Canada has been expanding northward, raising concern in Inuit communities. Though ecosystem engineering by beavers in temperate regions is well-documented, there is limited research that examines the effects of beaver impoundments in the tundra. Freshwater streams in the Arctic support subsistence fish populations and it is currently unclear how flow attenuation by dams will affect the habitat quality or prey resources of resident species. This research assesses differences in the benthic macroinvertebrate diversity and trophic structure of beaver-impounded streams above and below the treeline in Nunavik. Invertebrates and consumer stable isotopes were compared downstream and upstream of dams to characterize changes in assemblage composition, basal resource reliance, and Layman’s food web metrics. Shannon-Weiner diversity and the percentage of lotic invertebrates were lower upstream of beaver dams. Filter-feeders and EPT taxa (Ephemeroptera, Plectoptera, Trichoptera) decreased with variables associated with lentic conditions, such as reduced stream velocity, increased depth, and finer substrates. Geomorphic-driven differences in assemblage composition, without exhibiting changes in richness or abundance, suggest restructuring in response to upstream habitat transformation. In subarctic forest sites, reliance on terrestrially derived carbon in consumer diets was greater upstream of beaver dams but no effect was observed in shrub tundra sites. Additionally, upstream averages of consumer carbon were more enriched and similar to riparian vegetation than epilithic algae. Although a resource shift was observed, overall food web metrics were not affected by beaver dams. Collectively, the findings presented in this study demonstrate that beaver dam effects below the treeline generally resemble the lotic taxa replacement and dietary shifts reported within their historical range, while recently colonised streams above the tree line appear to be marginally less affected.
Powder Bed Fusion of Difficult-to-Print Ni-Based Superalloys: Microstructural Evolution and Cracking Behavior
(University of Waterloo, 2026-06-26) Aghajani, Hamidreza
IN738 is a precipitation-strengthened nickel-based superalloy that is widely valued in industry due to its excellent creep resistance and good corrosion performance. However, it exhibits poor manufacturability, primarily due to its complex alloy chemistry and the challenges associated with solidification during processing. In this study, a mechanistic process–microstructure–cracking relationship was first established for LPBF-processed IN738LC. It was observed that melt pool geometry plays a critical role in crack formation, with an optimal width-to-depth ratio governing crack susceptibility. Reducing hatch spacing or increasing laser power resulted in grain coarsening, with the effect being more pronounced for hatch spacing reduction. Nevertheless, crack density was significantly reduced with decreasing hatch spacing, which is attributed to improved part densification and a more favorable melt pool geometry. The as-built microstructure was found to be highly non-equilibrium due to the rapid cooling rates inherent to LPBF. It consisted of a γ matrix with cellular/dendritic solidification substructures, submicron carbides located at interdendritic regions, and dispersed oxide particles with Al-rich cores. No γ′ precipitates were detected in the as-built condition. Elemental segregation and oxide formation, combined with thermal stresses, contributed to reduced ductility and promoted crack initiation during processing. In the second stage of the study, heat treatment was employed to develop a high-temperature-capable microstructure, particularly aiming for a controlled distribution and size of γ′ precipitates with spherical, cuboidal, and irregular morphologies. A series of heat treatments with varying solutionizing (S) and ageing (A) temperatures were performed to promote crack healing and to develop an optimized microstructure, particularly γ′ precipitates with desirable size, distribution, and morphology for high-temperature applications. The heat treatment conditions mainly included solutionizing at different temperatures (S), solutionizing followed by low-temperature ageing at 845 °C (SLA), solutionizing followed by high-temperature ageing at 1120 °C (SHA), solutionizing followed by double-ageing at high and low temperatures (SDA), and the industry-recommended standard heat treatment for this material (ST: S1120-A845). It was observed that high-temperature solutionizing promotes a more homogeneous microstructure, whereas at lower temperatures (around or below 1120 °C), homogenization is only partial. It was observed that varying the solutionizing and ageing conditions led to the development of diverse γ′ precipitate size distributions, ranging from unimodal to bimodal and multimodal. Unimodal distributions were dominated by fine secondary γ′ precipitates, while multimodal structures consisted of fine secondary γ′ in conjunction with coarse primary γ′. It was further demonstrated that high-temperature ageing (≈1120 °C) facilitates γ′ coarsening. In contrast, low-temperature ageing (≈850 °C) stabilizes the secondary γ′, resulting in a fine, well-defined, and coherent γ′ distribution within the γ matrix. In addition to γ′ precipitation, other secondary phases were identified. Carbide precipitates, including those enriched in alloying elements such as Ti, were predominantly located along grain boundaries. Moreover, Cr-rich phases were observed to preferentially form at grain boundaries. These Cr-rich precipitates were shown to develop during low-temperature ageing (≈850 °C) and may contribute to the degradation of tensile properties. Solutionizing was identified as the primary factor governing recrystallization. At elevated temperatures, the microstructure underwent full recrystallization, resulting in pronounced grain coarsening. In contrast, at lower temperatures (e.g., ~1120 °C), the grain structure remained largely similar to the as-built condition with minor modifications. Additionally, crack healing was observed at higher solutionizing temperatures and was directly associated with the recrystallization of the material. A solid-state crack healing mechanism was proposed, whereby the high-energy state of the as-built microstructure—characterized by cracks, free surfaces, and high grain boundary density—provides a strong thermodynamic driving force for energy reduction. Upon heating above a critical temperature, this driving force promotes recrystallization and crack closure, leading to a more stable microstructure. In the subsequent phase, mechanical performance was systematically evaluated through room-temperature tensile testing of both as-built and heat-treated samples. Tensile tests at room temperature were performed along the vertical direction (i.e., loading direction parallel to the build direction). The as-built condition exhibited the lowest yield strength, while the standard heat-treated sample showed the highest. Overall, the tensile properties at room temperature were found to be governed by a combination of factors, including dislocation density, LAGB structures, grain size, γ′ precipitation (precipitation strengthening), anisotropy, residual cracking, crack healing, recrystallization, grain coarsening, and the presence of detrimental grain boundary phases. In the as-built state, the relatively lower strength and higher ductility were primarily attributed to strengthening mechanisms dominated by high dislocation density and low-angle grain boundary (LAGB) networks. The superior strength of the standard heat-treated sample in the vertical direction resulted from the synergistic effect of γ′ precipitation strengthening and the retained as-built microstructural characteristics (e.g., columnar grain structure and high LAGB density). In the other heat-treated conditions, strengthening was mainly controlled by γ′ precipitation together with crack healing during high-temperature solution treatment. The higher yield strength of the SLA sample relative to the other highly solutionized conditions was primarily attributed to the finer γ′ precipitates and reduced interparticle spacing. Furthermore, Cr-rich grain boundary phases formed during ageing at 845 °C contributed to intergranular embrittlement and fracture, which was confirmed by EDS analysis. This was consistent with the lower ductility and reduced UTS values observed in the SLA and SDA conditions relative to the SHA condition. The modified heat treatment strategies developed in this study produced a crack-free and nearly isotropic microstructure while providing improved room-temperature mechanical properties compared with the as-built condition. The combination of recrystallization and complete crack healing highlights their potential for high-temperature service, making these heat treatment routes promising alternatives to the conventional industrial heat treatment. In another case study, CM247LC, a non-weldable Ni-base superalloy, was fabricated by electron beam powder bed fusion (EB-PBF) at a wide range of energy levels. For this purpose, variable process parameters were adjusted to investigate their effect on microstructure and crack formation. Samples fabricated at both low and high area energies exhibited pronounced crack susceptibility. At very low energy densities, lack of fusion (LoF) and porosities were observed, while higher energy densities produced denser samples. Adjustments to energy density and process parameters resulted in a grain structure transition from fine-columnar to coarse-columnar and near-single crystal morphologies. Despite these changes, the cracking issue persisted, with micro-cracks observed in low-energy samples and macro-scale cracks, several millimeters long, forming at higher energy densities, highlighting the material’s high sensitivity to crack formation. Both solidification and liquation cracking were identified— the former showing dendritic crack surfaces, and the latter associated with eutectic phases and grain boundary precipitates. Severe recrystallization around cracks was observed at high energy densities, characterized by elevated dislocation densities. EDS analysis revealed hafnium- and silicon-rich precipitates in interdendritic regions and near cracks, contributing to severe hot cracking in the material.
Deployment Concerns in Machine Learning Systems: Unintended Interactions and Accountability
(University of Waterloo, 2026-06-26) Duddu, Vasisht
Machine learning (ML) models are increasingly being deployed for client-facing services (e.g., chatbots, search engines, and browsers), high-stakes decision-making applications (e.g., healthcare and criminal justice), and as part of larger systems (e.g., autonomous vehicles and operating systems). However, to deploy ML models for a particular application, practitioners need to address various deployment concerns including (i) infrastructure issues (e.g., latency, throughput, interoperability, scalability), (ii) model design (e.g., high utility and generalization, minimal overfitting, hyperparameter tuning, data processing), (iii) environmental impact (e.g., reducing carbon emissions, water and power consumption by data centers), (iv) adversarial and societal risks (e.g., security, privacy, safety, unfairness, poor transparency, misalignment, misinformation, and cyberattacks), and (v) enabling governance (e.g., verifying claims by practitioners, and regulatory compliance). I focus on two deployment concerns: adversarial and societal risks, and enabling governance, and address unintended interactions and accountability within these respective concerns. I present them as two parts of the thesis. (Part-1) Unintended Interactions in ML: Substantial prior work explores the design of defenses against individual risks to security, privacy, fairness, transparency, and safety. I argue that this is not sufficient for real-world ML models that must protect against multiple risks simultaneously. Practitioners need to address additional challenges that emerge when doing so, including unintended interactions. A systematic understanding of such interactions is lacking, and I study three unintended interactions: (a) a defense against one risk may increase or decrease other unrelated risks; (b) conflicts among defenses can decrease their effectiveness when combined; and (c) potential for collusion among adversaries can enable executing an attack to amplify others. I propose frameworks to identify factors underlying such interactions, and present guidelines to conjecture about unexplored ones. (Part-2) Accountability in ML Pipelines: Practitioners' claims about executing various ML operations needs verification by a verifier (e.g., regulator). This includes demonstrating ML properties covering the model, its training process, its training data, as well as deploying defenses and accounting for unintended interactions from Part-1. Such claims are currently communicated via ML property cards (e.g., model, data, and inference cards). I propose ML property attestation mechanisms that allow provers (e.g., model trainers) to demonstrate ML properties to verifiers, while ensuring model and data confidentiality. I show that existing software-based mechanisms are either inefficient (e.g., cryptographic mechanism), or ineffective and easily evaded (e.g., ML-based mechanism). I then identify hardware-based mechanisms using trusted execution environments as an efficient and effective alternative for providing ML property attestations. These attestations can then be used for verifiable ML property cards, to ensure accountability for practitioners' claims.
Cognitive and Epigenetic Predictors of Healthcare Utilization in the Canadian Longitudinal Study on Aging: Factor- and Indicator-level Examinations
(University of Waterloo, 2026-06-25) Wang, Elizabeth
Background: Healthcare utilization among older adults is a valuable indicator of health status and provides a mechanism linking illness to healthcare costs. Cognitive function and epigenetic age—as indicators of nervous system and cellular integrity at a biological level—are both correlated with age and may be important predictors of healthcare utilization. It is unclear how strongly the two predictor categories are associated, however, and it is not known whether they are best understood as correlated processes under a general “systemic resilience” (SR) construct, or as separate factors with independent influence on healthcare utilization. In the present research study, I examine indicator-level and factor-level predictors of healthcare utilization, with a focus on cognition and epigenetics. In doing so, the factor-level and indicator-level associations with healthcare utilization are evaluated, with an eye toward testing the validity of a superordinate SR construct. Objectives and hypotheses: This study examined the factor structure of SR as a higher-order construct, encompassing epigenetic age and cognitive function. It compared factor-level associations with healthcare utilization (emergency department [ED] visits and hospitalizations) to those observed at the indicator-level, while evaluating age and sex as potential moderators. A priori hypotheses were that a higher-order model would best fit the data, the associations would be stronger for women (vs. men) and older (vs. middle-aged) adults, and significant pathways would emerge at both the factor and indicator levels. Methods: Data were drawn from the Canadian Longitudinal Study on Aging (CLSA) Comprehensive Cohort (n=30,097; age range=45-85 at enrolment), focusing on the subsample who completed epigenetic assays (n=1,478). Structural equation modelling (SEM) was used to evaluate competing structural configurations and predict healthcare utilization. Age and sex moderation were examined using multi-group analysis. Logistic regressions were used to examine indicator-level associations. Results: A correlated two-factor model representing epigenetic age and cognitive function as distinct but related constructs, rather than as components of a higher-order SR model or a single unified factor was selected as the most appropriate model based on fit indices and parsimony. Cognitive function emerged as a predictor of hospitalizations at the factor-level (b -0.254; 95% CI; -0.461, -0.046). Supplemental analyses suggested no significant sex moderation, while evidence for age moderation was inconclusive. At the indicator-level, analyses suggested the mental alternation test (MAT) and intrinsic epigenetic age acceleration (IEAA) were reliable predictors of ED visits. Similarly, the animal fluency test (AFT) was a predictor of hospitalizations. Conclusions: Cognitive function and epigenetic age may be best considered as correlated, but fundamentally independent processes in older adults. Among factor-level predictors of healthcare utilization outcomes, cognitive function was reliable but not epigenetic age. At the indicator level, mental flexibility and cell-intrinsic aging were predictive of select facets of healthcare utilization. These findings suggest that both cognitive and epigenetic markers have some value in predicting future healthcare costs among older adults, but that systemic resilience may be less useful at the whole organism level.
Engineering Development and Signal Processing Advancements in OCT Angiography: From Custom System Integration to Temporal Domain Denoising
(University of Waterloo, 2026-06-24) Perez Paredes, Andrei Felipe
Optical Coherence Tomography Angiography (OCTA) positions itself as a highly effective, non-invasive technique that provides depth-resolved visualization of vascular structure and function. With a continuously emerging need to transition from static angiography to functional, time-resolved imaging, researchers have identified interconnected challenges. This thesis fundamentally explores two of these challenges: speckle noise and processing latency. Typically, spatial filters used to suppress speckle and denoise images are computationally expensive and act as temporal low-pass filters, destroying the dynamic physiological signals they intend to isolate. This thesis presents the design, implementation, and in vivo validation of a streaming-compatible swept-source OCTA (SS-OCTA) architecture relying on a hardware/software co-design to overcome these limitations. Rather than relying on isolated downstream algorithms, the system described in this research establishes a validated quality baseline starting at the hardware level. The custom 1060 nm MEMS-VCSEL SS-OCT platform developed in this thesis, leverages an adaptive software flyback filter to assess fast-axis position derivatives, actively isolating and discarding corrupted scans prior to contrast processing. Building upon this stationary signal foundation, the thesis introduces Temporal Subband Decomposition and Amplification (TSDA). TSDA operates as a dual-rate infinite impulse response (IIR) filter along the per-pixel temporal axis, decomposing the signal into structural, flow, and high frequency speckle bands. This continuous formulation reduces computational complexity to O(1), bypassing the buffering requirements of discrete Fourier methods and aiming to isolate physiologically driven flow from coherent noise. The integrated hardware/software stack was validated against a microfluidic phantom and an in vivo 14-day-old chorioallantoic membrane (CAM) preparation. An ablation study reported here confirms the TSDA architecture achieves a processing latency within the 10 ms budget. Furthermore, the complete pipeline delivered a Peak Signal-to-Noise Ratio (PSNR) of 27.8 dB against a multi-frame average reference, while yielding statistically significant improvements in Vessel Contrast-to-Noise Ratio (VCNR). By replacing spatial averaging with targeted temporal band isolation, the integrated platform extracts OCTA contrast while preserving the temporal flow signal within the filter passband.
Huge Operators in Holography: BPS Sectors, Matrix Models, and Black Holes
(University of Waterloo, 2026-06-24) Murali, Harish
This thesis explores quantum gravity by studying large-N gauge theories and matrix models. In particular, it focuses on operators whose charges scale as N^2, which we dub huge operators, so that they are heavy enough to backreact on the dual bulk geometry. In the first part, we study protected sectors of N = 4 super Yang-Mills theory, where supersymmetry gives enough control to ask finite-N questions beyond the planar limit. We analyze huge 1/2-BPS operators and show that their exact combinatorics reorganizes, at large N, into matrix models and integrable HCIZ fluid flows. We also study the 1/16-BPS sector relevant for supersymmetric black holes, emphasizing the role of finite-N trace relations and analytic continuation in the number of colors. In the second part, we turn to simpler matrix models as laboratories for holographic ideas such as universality, and commutativity. We show that huge deformations can produce universal eigenvalue densities in strong-coupling regimes, and we clarify the role of fermions in ensuring commutativity at strong coupling. Together, these results give concrete boundary descriptions of backreacted geometries, finite-N effects, and strong coupling dynamics.
Digital divide and financialization in Canada’s rental housing sector: An epistemological critique of technology in urban space
(University of Waterloo, 2026-06-24) St-Hilaire, Cloé
Housing is one of the most physical, tangible components of everyday life, and yet as rentiers occupy more and more space in the economy and society, housing is transformed into abstract financial products and digitally rendered into platforms, apps, and data. The increasing presence of platforms in urban life, primarily under corporate ownership, is reshaping how we view, research, understand, and experience housing. For tenants, it has translated into the digitization of the rental housing experience, from apartment search, tenant screening, to monthly payments. For landlords, it has meant increasing means for extracting value from tenants, derived from insights produced by data. For researchers and activists, it manifests as opaque information landscapes, leaving key questions unanswered and hindering housing justice efforts. For policymakers, it remains a display of fragmented data infrastructures. As housing continues to embody its contradictory nature between home (use value) and profit (exchange value), the digitization of rental housing warrants further scrutiny into how it contributes to the speculative conditions of housing under rentier capitalism. This thesis offers an epistemological investigation of the rise of data and digital technologies in Canada’s rental housing sector. It argues that the deployment of technology in rental housing, and the data that is produced as a result, (re)produces uneven epistemic outcomes that benefit capital and hampers social justice. This digital turn in housing has been led by rentiers who use platforms, apps, algorithms, and data for the production of housing information. By controlling the data pipelines, rentiers are able to dictate what gets measured (and what does not), frame housing digitization under deterministic discourses of progress and efficiency, and limit other’s capacities to know via the corporate gatekeeping of information. This leads to epistemic injustices against those who become targets, objects, and test subjects of housing datafication, and who are at the same time prevented from meaningfully understanding how this datafication affects them. From an urban governance perspective, the city under digitization remains governed through veils of opacity marked by inadequate data infrastructure, also creating epistemic injustices. This analysis combines a qualitative document and media overview of proptech and finance in Canada, a spatial analysis of proptech adoption in the build-to-rent sector of four cities, select Canadian case studies, key informant interviews, and a large-scale analysis of housing data infrastructures. The findings are separated into four empirical chapters pertaining to proptech and/or ownership data. The first article critically examines the socio-technical imaginaries of proptech–efficiency, lifestyle, sustainability, and democratization–as carried through by the industry and the media, and how these imaginaries are examples of technological determinism. The second chapter analyzes the adoption of proptech in Canada’s build-to-rent housing submarkets in Vancouver, Calgary, Toronto, and Montreal, the major adopters of rental proptech, and the characteristics of buildings with high proptech adoption. The third chapter presents the theoretical concept of epistemic engulfment to help make sense of the implications of the rise of proptech propelled by finance on the epistemic regimes of housing, and its implications for housing and urban justice. The fourth chapter analyzes the ownership data infrastructures of 31 cities across North America and Europe to determine why ownership data remains opaque despite increasing digitizing efforts from states and cities, and how ownership data opacity prevents the answering of key urban questions. In its entirety, this thesis offers an epistemological critique of the rise of digital technologies in rental housing under financialization through an analysis of its discourses and data infrastructures. It illustrates how the production of housing data under the control of private actors contributes to the already uneven power relationship between landlords and tenants through injustices that are epistemic in nature. It shows how urban governance contributes to the making of the conditions that allow us to know about urban issues, or remain in the dark. This thesis inserts itself in larger discussions about viewing housing data as a political subject and urges planning scholars to endeavour in critical reflections about urban information.
Groundwater and vegetation influences on alpine wetland evapotranspiration
(University of Waterloo, 2026-06-24) Murray, Eric
Wetlands are increasingly recognized for their ecological significance and hydrological function, particularly in snowmelt-dominated mountain regions experiencing climate change. This thesis investigates evapotranspiration (ET) and groundwater-surface water interactions within the Burstall Wetland, a mineral wetland located on the eastern slopes of the Canadian Rockies. The study aims to (1) examine seasonal wetland-scale ET fluxes and the relative contribution of snowmelt versus growing-season processes, and (2) identify the sub-surface and vegetation controls on spatial ET variability during the snow-free period. Data collection was conducted during the 2022 growing season using eddy covariance (EC) to measure wetland-scale energy and carbon fluxes and a closed dynamic chamber system to capture microsite ET across dominant vegetation communities (sedge, willow, moss, and litter). Groundwater levels were monitored through a network of groundwater wells, and volumetric water content (VWC), soil temperature, and meteorological variables were recorded to support ET estimation and spatial analysis. By integrating site-scale flux observations with chamber-based measurements, this study characterizes the spatial heterogeneity of ET and evaluates the contribution of groundwater to seasonal loss. The findings provide insight into the ecohydrological processes governing alpine wetland function and offer a baseline for assessing wetland sensitivity to future climatic and hydrological shifts in mountain environments.
Towards Trustworthy Federated Learning: Security, Privacy, and Verifiability
(University of Waterloo, 2026-06-24) Deressa, Biniyam
Federated learning enables collaborative model training across institutions that cannot share raw data, but practical deployments rely on trust assumptions that do not hold in adversarial environments. Malicious clients may omit or falsify computation, inject poisoned updates, or free-ride on collective training with negligible detection risk. Existing defenses address security, privacy, and verifiability in isolation: privacy mechanisms obscure the signals required for robustness, while general-purpose zero-knowledge proof systems incur costs that scale with circuit size and are impractical for neural network workloads. The result is a structural \emph{trust deficit} that no single existing mechanism resolves. This thesis argues that the security--privacy--verifiability tension in federated learning is \emph{architectural rather than fundamental}. By decomposing trust into \emph{four separable research problems}, namely, adversarial client selection, privacy-compatible robust aggregation, cryptographic training verification, and compositional architecture, and by exploiting the algebraic structure of learning workloads, each property can be enforced by a mechanism with explicit assumptions and well-defined interfaces. These mechanisms are independently deployable and compose via defined interfaces without requiring cross-mechanism security re-analysis, yielding a \emph{modular trust architecture} for trustworthy federated learning. \textsc{TrustBandit} addresses the security dimension by formulating client selection as an adversarial multi-armed bandit under partial observability. Importance-weighted reputation estimation with adaptive exploration achieves a provable regret bound $O(\sqrt{T N \ln N})$, where $T$ is the number of training rounds and $N$ is the number of clients, and, in evaluation, identifies trustworthy clients with $94$--$99\%$ success in low-adversary settings (up to $20\%$ adversaries) and maintains $67$--$69\%$ selection success under $50\%$ adversarial participation, while sustaining $70.97\%$ test accuracy at $50\%$ adversarial participation and improving robustness by up to $5.5\times$ over standard selection baselines. \textsc{PROFILE} addresses the privacy--robustness tension through architectural separation rather than algorithmic compromise: anomaly detection is relocated from centralized plaintext inspection to server-side predictive detection over bucket-wise homomorphically encrypted aggregates with semantic client assignment. The framework enforces IND-CPA computational privacy for individual updates under Ring-LWE hardness, with LDP-protected metadata, while preserving Byzantine robustness under poisoning and backdoor attacks; empirically it achieves accuracy within 2--3\,pp of the best plaintext baseline (FLTrust) while operating under full RLWE encryption, with detection rates from $0.87$ to $0.99$ across all datasets and non-adaptive attack types; adaptive adversaries that suppress per-round statistical signals fall outside this bound, as characterised by the leakage--detectability frontier. \textsc{zkMaP} and \textsc{zkExp} address verifiability by specializing to the dominant computational kernels in training. \textsc{zkMaP} gives succinct verification for matrix multiplication via polynomial identities over pairing groups, achieving $O(n^2)$ prover complexity for matrix dimension $n$, constant-size proofs (320 bytes), and constant-time verification (3.68\,ms), yielding up to $19.07\times$ verification speedup over prior specialized matrix multiplication protocols at comparable security. \textsc{zkExp} provides a succinct proof system for exponentiation with constant-time verification and constant-size proofs (160 bytes for single proofs; 256 bytes in batched mode), with low amortized batch overhead (1.35$\times$). \textsc{RIV} composes these primitives into an end-to-end proof-of-training protocol. Training transcripts are committed prior to challenge selection, preventing selective honest computation. Stochastic Interval Commitments certify native IEEE-754 floating-point computation within backward-error-derived bounds while preserving cryptographic binding. The resulting protocol provides parameterized detection guarantees: for an adversary corrupting a $q_{\mathrm{adv}}$-fraction of challenged layers, the per-round acceptance probability is bounded by $(1-q_{\mathrm{adv}})^k + k\varepsilon_{\mathrm{crypto}} + \delta_{\mathrm{fp}}$ (where $\varepsilon_{\mathrm{crypto}} \le 2m/|\mathbb{F}_p| + \mathsf{negl} \lambda)$ per challenged layer), yielding explicit trade-offs between challenge rate, overhead, and adversarial detectability (e.g., $>99.99\%$ cumulative detection at $k=3$ over 50 rounds). Collectively, these results demonstrate that cryptographically grounded trust in federated learning is achievable through specialized, composable mechanisms rather than monolithic designs.
Sustainability Management in Private Capital Markets: Important and Distinct, yet Underexplored-Institutional Pressures, Legitimacy, and ESG Disclosure
(University of Waterloo, 2026-06-24) Mirza, Majid
The rapid expansion of sustainable finance has intensified demands for consistent sustainability disclosure across global capital markets. While public market actors and listed corporations have received significant scholarly attention, private capital markets, particularly private equity, remain comparatively underexamined despite their growing influence in global investment flows. This dissertation investigates how sustainability management and disclosure practices are emerging within private capital markets and how private equity actors respond to evolving institutional pressures shaping environmental, social, and governance (ESG) reporting. Drawing on institutional theory, legitimacy theory, and sustainability management literature, the dissertation explores three interconnected dimensions of sustainability integration in private capital. The first paper presents a systematic literature review of sustainability research within private capital investing, identifying a substantial gap between the rapid growth of ESG practices in industry and the limited academic attention devoted to sustainability within private equity and venture capital research. The review reveals that sustainability-related scholarship constitutes a very small proportion of the broader private capital literature and highlights several emerging thematic areas requiring further investigation. Building on this foundation, the second paper examines ESG reporting practices among leading global private equity firms through a comparative analysis of ESG reports and Sustainable Development Goal (SDG) integration strategies. The findings suggest that while sustainability commitments are increasingly communicated within ESG disclosures, much of the integration appears to function as legitimacy signaling rather than deeply embedded investment decision-making processes. The third paper extends the analysis to the evolving institutional landscape of global sustainability disclosure by examining comment letters submitted by financial institutions and private equity firms in response to the International Sustainability Standards Board’s (ISSB) consultation on agenda priorities. Using a mixed-method approach combining thematic interpretation and structured content analysis, the study identifies patterns of institutional isomorphism and dissonance within the consultation process. While financial institutions and private equity actors demonstrate convergence around biodiversity and climate–nature disclosure priorities, significant divergence emerges regarding the role of integration in sustainability reporting, reflecting distinct institutional logics within the financial sector. Taken together, the findings illustrate how private capital actors both conform to and shape emerging sustainability management frameworks in the form of selective institutionalization. The dissertation contributes to scholarship by expanding understanding of sustainability integration within private capital markets, highlighting the role of institutional pressures in shaping ESG disclosure practices, and introducing private equity as an important and distinct, yet underexplored actor in global sustainability reporting debates.
Quantifying Structural Uncertainty in Hydrologic Models
(University of Waterloo, 2026-06-23) Arabzadeh, Rezgar
Hydrologic models are essential tools for understanding watershed processes and supporting water resource management. However, their predictions are inherently uncertain due to imperfect model structures (structural uncertainty), parameter estimation challenges (parameter uncertainty), and limitations in observational data and model forcings (input uncertainty). Bayesian inference has become a widely used framework for quantifying these uncertainties because it enables probabilistic parameter estimation and prediction while formally incorporating prior information and observational evidence. Despite these advantages, the application of Bayesian methods to complex hydrologic models remains computationally demanding, and the resulting predictive uncertainty often represents a combination of multiple uncertainty sources (including input, parameter, and structural uncertainties) that are difficult to interpret individually. These limitations reduce the effectiveness of Bayesian uncertainty analysis as a diagnostic tool for improving hydrologic models. This thesis develops methodological advances to improve the efficiency and interpretability of Bayesian uncertainty quantification in hydrologic modeling. The research focuses on two challenges: improving the computational feasibility of Bayesian inference for complex models and separating the sources of uncertainty represented within Bayesian predictive distributions. To address these challenges, new methods are developed and evaluated using both regional and continental-scale hydrologic datasets. 1. A machine learning–assisted framework is developed to improve the efficiency of Bayesian joint inference for hydrologic models. The proposed approach integrates machine learning techniques with Bayesian calibration to facilitate exploration of complex posterior parameter distributions and reduce the computational burden associated with traditional sampling methods. The framework is evaluated using twelve watersheds from the MOPEX dataset and demonstrates improved inference performance while maintaining reliable uncertainty quantification. 2. A variance decomposition methodology is introduced to identify and quantify the sources of uncertainty embedded within Bayesian predictions. While Bayesian calibration provides probabilistic estimates of model outputs, it does not directly attribute predictive uncertainty to individual components of the modeling framework. The proposed method decomposes posterior predictive uncertainty into interpretable components, enabling a clearer understanding of how different aspects of the modeling process contribute to overall uncertainty. 3. The proposed uncertainty decomposition framework is applied to a large-scale hydrologic analysis across approximately 3,000 watersheds in North America. This continental-scale application enables the systematic evaluation of spatial patterns in hydrologic model uncertainty and reveals how dominant uncertainty sources vary across hydroclimatic and physiographic regions. Together, the contributions of this thesis improve both the computational efficiency and the interpretability of Bayesian uncertainty estimates in hydrologic modeling. The proposed approaches provide tools for diagnosing uncertainty sources and evaluating model reliability, which can support more transparent hydrological predictions across a range of environmental and water resource applications.
Contributions to the model theory of algebraic differential equations
(University of Waterloo, 2026-06-23) Eagles, Christine
This thesis deals with semiminimal analyses of finite rank types, primarily in the stable theory of differentially closed fields of characteristic zero (DCF0). The two main themes considered in this thesis are determining when a type is minimal or semiminimal, and understanding what invariants of finite rank types are captured by a semiminimal analysis. In DCF0, a central concern of this thesis is determining when a type is almost internal to the field of constants. Partially generalising a result of Rosenlicht, algebraic criteria are provided in two different contexts: rational vector fields on affine n-space, and pullbacks under the logarithmic derivative of certain types which are internal to the constants. The criteria in the former case answers a question posed by Freitag, Jaoui, Marker and Nagloo about when the Poizat equations are internal to the constants. In both cases, the theory of binding groups in stable theories plays a significant role. Results of Duan and Nagloo are improved upon to completely classify when the generic types of Lotka-Volterra systems are minimal. In the minimal case, a characterization of the possible relations that may exist between solutions of distinct Lotka-Volterra systems is given. In the general setting of a totally transcendental theory, it is shown that the multiplicity with which a minimal type arises in a semiminimal analysis of a finite rank type is invariant, i.e., it is independent of the semiminimal analysis. A conjecture is proposed for the possible ways for two semiminimal analyses of the same finite rank type to differ. Along the way, the connection between semiminimal analyses and domination decompositions, is clarified.
Parameter Inference and Model Selection for Differential Equation Models with Applications
(University of Waterloo, 2026-06-23) Zhao, Yuxuan
Dynamic systems are commonly modelled by differential equations (DEs) in epidemiology and biology, among other fields. The parameters in the DEs are often of scientific interest and required for estimation, given a set of noisy observations. The first and oldest general class of methods for the parameter inference problem in DEs is based on numerical solvers. As a preliminary study, we conduct a comparative study of compartmental models for COVID-19 transmission using such numerical solver-based methods. However, this class of methods can be computationally intensive and may only converge to the local optima due to the sensitivity of the numerical solution to the parameters and initial conditions. This thesis begins by presenting this study, which highlights these limitations and motivates the methodological developments that follow. To address these challenges, Gaussian process-based methods serve as an alternative that bypass the need for numerical solvers. In particular, the recent manifold-constrained Gaussian process inference (MAGI) method demonstrated accurate estimation and fast computational speed. However, the original MAGI method is limited to ordinary differential equations (ODEs), which are inadequate for some dynamic systems, calling for more complex or flexible structures in the specification of the DE model. Motivated by this, this thesis extends the framework of MAGI to facilitate inference for three common but challenging contexts, including (i) delayed differential equations, where system components exhibit time delays in their responses, (ii) mixed-effects ODEs, where experimental data consist of time-course observations on multiple subjects from a population, and (iii) selection of the most appropriate ODE model from a set of candidate models, where there is no true underlying model. The complex structures of these DEs introduce inferential and computational burdens and we address them in this thesis, along with computational and theoretical justifications. We illustrate the efficacy of our methodologies through simulated and real-world applications.
Content-Aware Pixel Art Rendering on Pixels of Multiple Shapes
(University of Waterloo, 2026-06-23) Wang, Zane Z.
Pixel art is a well studied art form that arose from technical limitations on computing hardware in the early 1980s. Although the discipline itself is often associated with video games, standalone character and landscape portraits in the pixel art style are also popular. Characterized by a deliberately limited resolution and colour palette, pixel art is as an artistic exercise in the conveyance of visual information with a limited number of samples, while avoiding certain unpleasant visual artifacts. In this thesis, we present a first solution to a novel problem in computer graphics: how do we render images in the pixel art style on other tilings of the plane besides the usual squares, all while respecting image features? We formulate the non-square (or "any-shape") pixel art rendering task as an energy minimization problem over tile-shaped filter supports, given a conventional raster image and geometric tiling data as input. We compute tile energy gradients via rasterization of the tiling geometry; using this information, we evolve an optimal filter support shape while imposing geometric constraints to balance between distortion and feature clarity. We then demonstrate that our method produces images with superior qualitative and quantitative properties in comparison with naive methods. Our program can compute finished images in seconds, and allows the user to watch the pixel art evolve in real time. We also provide some basic stylization and interaction features for artists, such as k-means colour quantization, colour palette generation in a perceptually uniform colour space, and brush-based vertex manipulation to adjust the shapes of the filter supports. This method has the potential to be useful in several artistic contexts, such as the creation of highly stylized portraiture and landscapes, and authoring of image and video for real hardware displays that use non-square pixels.
Exploring Inuvialuit youth food security experiences and supports in the Inuvialuit Settlement Region
(University of Waterloo, 2026-06-23) Ramirez Prieto, Maria
Background: The Inuvialuit Settlement Region (ISR) is in the Northwest Territories, Canada, and is the westernmost of the four Canadian Inuit regions. The ISR covers 906,430 km2 and includes six communities: Aklavik, Inuvik, Paulatuk, Sachs Harbour, Tuktoyaktuk, and Ulukhaktok. Today, Inuvialuit in the ISR are closely connected to and dependent on-the-land for physical, mental, spiritual, and emotional nourishment, which supports food security and well-being. Yet Inuvialuit youth face numerous barriers to participating in subsistence harvesting, and a growing body of literature documents a dietary shift away from country foods (CF) among younger generations. This is especially concerning as 69% of individuals aged 15 years or older in the ISR experience food insecurity. However, few studies have examined how Inuvialuit, including Inuvialuit youth, engage with CF and their relationships within the food web in relation to food security and well-being. While youth are the focus of the overall dissertation, Elders and families are also participants in this dissertation. Objectives: The purpose of this dissertation is to explore the web of relationships and experiences that shape the participation of Inuvialuit youth in the CF system, including the connection to the natural world, culture, and others, in order to generate community-driven evidence that fills gaps in the literature. Moreover, this dissertation aims to centre Inuvialuit knowledge holders through the co-production of knowledge, and use community based participatory action research (CBPAR) to conduct equitable and community-driven research. Methods: Using CBPAR, the studies in this dissertation employ diverse qualitative methods to conduct research with community members, including Community Research Leads (CRLs). Using a CBPAR approach provides an opportunity for research to move away from research on Indigenous communities to research with and for Indigenous communities and aligns itself with the National Inuit Strategy on Research. Photovoice, talking circles, and semi-structured interviews were used with purposive and snowball sampling of 11 Inuvialuit youth across all six ISR communities, 19 Elders in Aklavik, Paulatuk, Tuktoyaktuk, and Ulukhaktok, and nine families in Aklavik, Tuktoyaktuk and Ulukhaktok. Reflexive thematic analysis, using co-analysis methods, was used for all three studies. Results: Study 1 (Chapter 2) employed photovoice methodology, working with 11 youth participants who captured photographs of their CF experiences and shared ~5 photographs during semi-structured interviews. Through reflexive thematic analysis, our research team co-created five themes from the data: 1) CF supports Inuvialuit youth well-being; 2) preference for CF despite varied consumption and activity frequencies; 3) network of CF within communities; 4) strong foundational cultural knowledge and skills; and 5) cultural continuity. Study 2 (Chapter 3) brought together 10 youth from Study 1 and 19 Elders through talking circles to explore the relationship between youth, Elders, and intergenerational Inuvialuit knowledge (IK) transmission in relation to CF, food security, and well-being. In addition to semi-structured questions, photo-elicitation was used to initiate conversation between Elders and youth about the photograph’s subject matter and to invite storytelling (e.g., caribou harvest, goose roast for dinner). Our research team co-created four themes from the data: 1) fostering cultural connection and knowledge transmission through CF and family time; 2) emphasizing oral teachings as essential for well‑being; 3) recognizing the true cost of store‑bought food and goods; and 4) working together for community food security In Studies 1 and 2, family was identified as a crucial aspect of youth connection to CF and IK, in turn, supporting food security and well-being. As such, in Study 3 (Chapter 4), nine families (n = 28 participants) from Aklavik, Tuktoyaktuk, and Ulukhaktok were interviewed through semi-structured group interviews to explore the role of CF and family in the transmission of IK to support youth food security and well-being in the ISR. Our research team co-created four themes from the data: 1) learning on-the-land through experiences; 2) nourished by the land; 3) navigating barriers; and 4) the guiding principles for present and future generations’ well-being. Conclusion: Together, these studies examine Inuvialuit youth, Elders, and families’ experiences in the CF system, including identifying facilitators and barriers to accessing CF and IK. These studies make substantive contributions to the literature by documenting what Inuvialuit have long known – that CF is essential for youth, family, and community food security and well-being. Concurrently, these studies offer critical qualitative evidence that broadens the predominantly quantitative and store-bought-food-centered literature in the ISR. Adding to a growing body of literature, this research highlights that CF, along with the relationships it fosters with people, the land, culture, and community, supports food security while also nourishing the mind, body, and soul. This research employed a CBPAR approach, engaging community members at all stages of the research process and aligning with Inuit Tapiriit Kanatami’s National Inuit Strategy on Research and the Inuvialuit Regional Corporation’s ISR Research Data Strategy to ensure that Inuvialuit are included and respected as knowledge holders, thereby fostering respectful and beneficial research for Inuvialuit communities.

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