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Recent Submissions

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    Sequential Design and Metamodeling for Computer Experiments
    (University of Waterloo, 2026-09-25) Huang, Yuying
    Computer simulation models are increasingly used to study complex engineering systems, but their high computational cost often limits the number of simulations that can be performed. This thesis develops sequential design strategies for efficient metamodeling of expensive computer simulations, with a particular focus on stochastic simulations exhibiting input-dependent noise. The proposed methods aim to improve predictive accuracy under limited simulation budgets while providing reliable uncertainty quantification. The thesis begins with a motivating application in performance-based seismic design of wood-frame podium buildings. A Kriging metamodel with an adaptive sampling strategy is developed to efficiently approximate the relationship between structural design parameters and seismic response. The resulting surrogate is used to identify reliable design regions in which simplified seismic analysis procedures can be applied with high confidence. Building on this application, the thesis develops a fully Bayesian sequential design framework for heteroscedastic stochastic simulation models. The proposed approach employs dual Gaussian process surrogates to jointly model the mean response and input-dependent noise, and introduces an expected Bayesian integrated mean squared prediction error (BIMSPE) criterion for sequential point selection. By fully propagating uncertainty in model parameters and latent noise through posterior inference and sequential importance sampling, the proposed method achieves improved predictive accuracy, noise estimation, and uncertainty quantification compared with existing empirical Bayes and variational approaches. Finally, the thesis extends the fully Bayesian framework from one-at-a-time sequential design over a discrete candidate set to batch-sequential design over continuous input spaces. A computationally efficient two-step strategy is proposed that first generates promising candidate batches through continuous optimization of an analytical batch IMSPE reduction criterion and then performs a fully Bayesian reranking using an approximation to the expected BIMSPE criterion. This extension enables practical batch selection while preserving the advantages of fully Bayesian uncertainty quantification and leveraging parallel computing resources. Extensive numerical studies on synthetic examples and a seismic engineering application demonstrate that the proposed methods consistently improve surrogate accuracy and uncertainty quantification while remaining computationally tractable. Collectively, this thesis advances Bayesian sequential design methodology for global metamodeling of expensive stochastic simulations and provides practical algorithms for modern simulation-based engineering applications.
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    Data-driven Fault and Performance Management of Virtualized Networks
    (University of Waterloo, 2026-09-25) Johari, Seyed Soheil
    The increasing adoption of Network Functions Virtualization (NFV), cloud-native architectures, and microservice-based designs has transformed modern communication networks into highly dynamic and complex software-driven systems. While these advancements enable flexibility, scalability, and efficient resource utilization, they also introduce significant challenges for network fault and performance management due to increased system complexity, dynamic behavior, and limited observability. In such environments, faults often originate in different layers of the system and propagate across tightly coupled components, manifesting as correlated anomalies in high-dimensional telemetry data. Traditional rule-based and threshold-driven management approaches are no longer sufficient to capture these complex dependencies or reliably diagnose faults, motivating the adoption of data-driven and machine learning (ML)-based techniques for automated network management. This thesis develops data-driven methods for fault detection, diagnosis, and root cause analysis in virtualized network systems, with a focus on NFV-based infrastructures and cloud-native mobile networks. The central goal is to design learning-based solutions that remain robust under limited supervision, data contamination, evolving environments, and incomplete observability, while providing interpretable and actionable insights for real-world network operations. To this end, the proposed approaches combine unsupervised learning, active learning, domain adaptation, and causal reasoning to address fundamental challenges in modern network management. Specifically, the thesis includes four main contributions. First, an unsupervised anomaly detection and localization framework for NFV systems that remains robust under contaminated training data by leveraging a teacher–student learning paradigm. Second, an active learning approach for Transformer-based fault diagnosis that reduces labeling costs by selecting informative and diverse samples based on attention-derived dependency structures. Third, a few-shot domain adaptation framework that mitigates data drift through causal feature separation, enabling robust cross-domain deployment without the need for frequent retraining. Fourth, a causal root cause analysis framework that incorporates structural priors learned from normal-operation telemetry and enables the identification of latent root causes under partial observability. Extensive evaluations on realistic datasets collected from NFV testbeds, cloud-native microservice applications, and 5G network environments demonstrate that the proposed methods significantly improve anomaly detection and localization accuracy, reduce labeling requirements, enhance cross-domain robustness, and provide more accurate and interpretable root cause analysis compared to existing approaches.
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    DNATree: Dynamic Tree Adaptation for Multi-Tenant In-Network Aggregation
    (University of Waterloo, 2026-09-25) Yan, Qi Fan
    In-network aggregation (INA) reduces distributed-training traffic by aggregating gradient updates in programmable switches. However, in multi-tier, multi-tenant clusters, heterogeneous workloads and background traffic continually alter the aggregation state and link bandwidth available across aggregation trees. Existing systems either optimize resource use within a fixed tree or reconsider tree assignments only at job admission or minute-scale intervals, limiting their ability to exploit changing resource availability within and across aggregation trees. We present DNATree, a multi-tenant INA service that adapts at millisecond timescales by combining flexible aggregation (i.e., Exploitation) with dynamic tree adaptation (i.e., Exploration). Exploitation progressively uses available switch memory and link bandwidth along the installed tree without fixed per-job allocations. This decouples fine-grained resource allocation from tree selection, enabling Exploration to identify and validate candidate trees using job-local observations. Safe tree transitions steer new work toward better-resourced trees without disrupting in-flight aggregation. On a Tofino~1 testbed, DNATree achieves up to 74.3% higher system goodput over fixed-partition progressive aggregation under constrained aggregation capacity. Packet-level simulations show a 25% average improvement over the best-performing aggregation-tree adaptation baseline across fat-tree and leaf-spine datacenter topologies.
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    Negotiated Spaces: Spatial Adaptations of Informal Practices in the Toronto Chinese Diaspora
    (University of Waterloo, 2026-09-25) Leung, Valarie
    Since the 1980s, gradual migration has transformed Chinese diasporic landscapes across Toronto’s Chinatown and suburbanized Greater Toronto Area (GTA), producing ethnoburbs and new forms of social and spatial communities. While contemporary urban and suburban environments are constantly reshaped by migration and globalization, the small business practices and networks that underpin these communities remain less visible and largely outside of formal recognition and social acceptance. Existing scholarly research has examined Toronto Chinatown and its surrounding suburbs for its internal social organization, and patterns of ethnoburban expansion. However, less attention has been given to everyday spatial practices and architectural reconfigurations of small informal businesses. These practices materially express how communities adapt and modify spaces to sustain themselves. These spaces remain understudied and continue to exclude the personal stories and everyday lived experiences of its community members. This thesis aims to document and address the spatial transformations of microcosmic environments shaped by informal businesses and practices within the Chinese diaspora communities of Toronto and its suburbia periphery across the GTA. It will identify spatial conditions along a spectrum of visibility, legality, temporality, and across the private-public threshold to reveal how communities subvert and reappropriate spaces in ways that reflect alternative systems of governance and practices outside of formal structures. This study takes on ethnographic surveying through quantitative and qualitative approach of observing everyday workspace conditions and conducting one-on-one oral interviews with community members of their stories and lived experiences. This information was then spatially analyzed through sketches and architectural drawings, to derive a series of speculative design interventions that experiment with ongoing tensions of ‘negotiating spaces’ across prototypical sites that resist and contest against conventional orders. This thesis argues that speculative design reveals and reimagines alternative spatial possibilities of potential for informal practices to coexist, bringing together these often fragmented and underrepresented logics through architectural tactics across a gradient of interventions.
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    Simulation of Millimeter-Wave Optomechanical Torque Sensors and RF Resonators
    (University of Waterloo, 2026-09-25) Kim, John
    Superconducting resonators provide a promising platform for sensing because of their low microwave dissipation and ability to support high quality factor electromagnetic resonances. This thesis investigates the design and characterization of superconducting resonator systems for sensitive microwave measurements, with particular emphasis on a millimeter-wave optomechanical torque sensor and superconducting spiral resonators coupled to coplanar waveguides. The first part of this thesis begins with examples of quantum-enabled and optomechanical torque sensors, followed by the theory of optomechanical torque sensing and the design and finite element analysis of a superconducting optomechanical torque sensor operating at an electromagnetic resonant frequency of 40 GHz. The device couples a torsional mechanical resonator to a superconducting electromagnetic resonator through an electric-field interaction confined within mechanically compliant capacitors. The mechanical resonator operates at frequencies ranging from the sub-MHz regime to a few MHz. The effects of device geometry on the optomechanical coupling strength, standard quantum limit photon number, and torque sensitivity are systematically investigated using the finite element method. These three quantities are critical figures of merit for evaluating the performance of the sensor and are explained in detail in this thesis. Based on this numerical analysis, the sensor geometry was optimized with respect to the minimum torque sensitivity, or more intuitively, the smallest detectable torque, and the standard quantum limit photon number, which depends on the coupling strength. The minimum torque sensitivity was determined to be 2.587 yNm/√Hz, while the minimum standard quantum limit photon number was found to be 9.206 × 10^−3. Not only were the optimized numerical figures of merit revealed by this analysis, but a suite of sensor designs with optimized figures of merit was also developed based on the geometric analysis. The second part of this thesis investigates superconducting spiral resonators inductively coupled to a coplanar waveguide as a simplified platform for studying microwave loss mechanisms relevant to superconducting circuits. Electromagnetic finite element method simulations were used to examine how the spiral geometry and resonator–waveguide separation determine the resonant frequency and external coupling rate within the 4–8 GHz band. The number of turns of the spiral resonator was varied to determine the designs corresponding to EM resonant frequencies of 4, 6, and 8 GHz, which were found to require 24.9, 18.8, and 16.24 turns, respectively. Additionally, the separation distances required to achieve external coupling rates of 1, 10, and 100 kHz were determined from the simulations for each of the three resonant frequencies. These coupling rates were selected because they correspond to desired external quality factors, which are discussed in greater detail in this thesis. Together, these studies demonstrate how geometric design can be used to engineer the coupling and performance of superconducting resonator systems. The results provide practical design guidelines for future quantum sensing applications and for studying microwave loss mechanisms in superconducting circuits.