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Recent Submissions

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    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.
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    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.
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    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.
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    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.
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    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.