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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Now showing 1 - 20 of 17185

Safety and Security of Reinforcement Learning for Autonomous Driving
(University of Waterloo, 2025-11-27) Lohrasbi, Saeedeh
In the context of autonomous driving, reinforcement learning (RL) presents a powerful paradigm: agents capable of learning to drive efficiently in unseen situations through experience. However, this promise is shadowed by a fundamental concern—how can we entrust decision-making to agents that rely on trial-and-error learning in safety-critical environments where errors may carry severe consequences? This thesis advances a step toward resolving this dilemma by integrating three foundational pillars: adversarial robustness, simulation realism, and model-based safety. We begin with a comprehensive survey of adversarial attacks and corresponding defences within the domains of deep learning (DL) and deep reinforcement learning (DRL) for autonomous vehicles. This survey reveals the porous boundary between safety and security—both natural disturbances and adversarial perturbations can destabilize learned policies. Motivated by this insight, we introduce the Optimism Induction Attack (OIA), a novel adversarial technique that manipulates an RL agent’s perception of safety, causing it to act with unwarranted confidence in hazardous situations. Evaluated in the context of an Adaptive Cruise Control (ACC) task, the OIA significantly impairs policy performance, exposing critical vulnerabilities in state-of-the-art RL algorithms. To counter the demonstrated threats, we present a systematic defence architecture. We develop REVEAL, a high-fidelity simulation framework designed to support the training and evaluation of safe RL agents under realistic vehicle dynamics, traffic scenarios, and adversarial conditions. By narrowing the gap between abstract simulation and real-world complexity, REVEAL facilitates rigorous and nuanced testing, which is essential for safety-critical applications. To enhance learning efficiency within this environment, we employ a transfer learning (TL) strategy: policies initially trained in simplified simulators (e.g., SUMO) are adapted and fine-tuned in REVEAL, leading to faster convergence and improved safety performance during both training and deployment. Central to our approach is the development of a Multi-Output Control Barrier Function (MO-CBF), which simultaneously supervises throttle and brake commands to enforce safety constraints in real time. Rather than relying on hard overrides, the MO-CBF operates cooperatively with the learning agent—gently adjusting unsafe actions and introducing corresponding penalties during training. This enables the agent not only to learn safe behaviour but also to internalize safety principles and anticipate potentially unsafe scenarios. Our empirical evaluation demonstrates the effectiveness of the proposed framework across a spectrum of disturbances, adversarial inputs, and realistic high-risk maneuvers. The results consistently show improved safety and robustness, highlighting the framework’s capacity to transform RL agents from vulnerable learners into trustworthy autonomous systems. In summary, this thesis presents a comprehensive methodology for safe and secure RL in autonomous driving. By grounding agent training in high-fidelity simulation, leveraging adversarial awareness, and embedding real-time model-based safety mechanisms, we provide a cohesive and scalable pathway toward deploying RL in the real world with confidence.
How Architectural Style, Height, and Complexity Influence Perceived Oppressiveness in Urban Spaces
(University of Waterloo, 2025-11-27) Lapietra Garcia, Thomas
The design of urban environments strongly influences psychological experience, yet research on how building form influences affective responses remains limited. This study used immersive virtual reality to examine the combined effects of architectural style (modern vs. contemporary), building height (low-, mid-, and high-rise), and façade complexity (low, medium, high) on affective perceptions of urban streetscapes. Forty-nine participants explored 18 virtual environments and rated each on oppressiveness, openness, restoration, arousal, and environmental liking. Results showed that greater building height consistently increased perceived oppressiveness and arousal while reducing openness, stress restoration, and liking. Greater façade complexity increased preference, openness, and restoration, and buffered the oppressive effects of high-rises, particularly in modern-style settings. Participants also expressed a clear preference for low- and mid-rise settings over high-rises. These findings reiterate and expand on the restorative and aesthetic benefits of architectural complexity and the value of human-scale design in supporting psychological well-being in urban dwellings.
Quantifying Endplate Deflection in Response to Cyclic Load Exposures Using a Porcine Cervical Spine Model
(University of Waterloo, 2025-11-27) Watson, Michael
The vertebral endplate is a thin layer of cartilage and bone that separates the intervertebral disc from adjacent vertebral bodies and facilitates the transmission of compressive force through the spine. Despite this essential function, it remains the weakest component of the vertebra-disc unit and is highly susceptible to mechanical failure. Endplate failure typically arises from localized tensile strains that manifest as deflection, defined as the out-of-plane displacement of the surface under load. While prior work has demonstrated inferior endplates of intervertebral joints exhibiting greater deflection and higher incidence of failure than their superior counterpart, current techniques for quantifying endplate deflection face notable limitations. Early studies using metallic markers or displacement transducers required drilling channels into the vertebral body, potentially exaggerating deformation by weakening subchondral bone support. Imaging-based approaches, particularly micro-CT, offer high spatial resolution but are limited to static or stepwise loading due to temporal constraints. These static conditions do not capture the cyclic loading patterns experienced by the spine during daily activity, where repeated deformation can cause fatigue-induced microdamage and eventual failure. Additionally, static loading promotes excess fluid loss from the nucleus pulposus, altering endplate deflections in ways that do not reflect physiological motion. Consequently, existing measurement techniques may misrepresent true endplate behavior and are unable to evaluate changes in deflection as a function of cyclic load exposure. This study addresses these limitations by developing a unique method to assess endplate deflection during cyclic loading without requiring prolonged stepwise protocols or causing damage to the vertebral bone. By comparing superior and inferior endplates across different load magnitudes and cyclic durations, this work aims to clarify the mechanisms underlying endplate vulnerability and further validate the porcine cervical spine as an experimental model for human lumbar spine deflection. Eighteen porcine cervical spine functional units (C3C4, C4C5, and C5C6; n = 6 per level) were dissected to yield 36 individual vertebrae. High-resolution laser profilometry was then used to capture the topography of the caudal endplates of C3, C4, and C5 and the cranial endplates of C4, C5, and C6. Custom indenters, designed as negative molds of the nucleus-occupying endplate region, were created from the resulting surface scans and fabricated via 3D printing. Specimens were then oriented such that the tested endplate was in a neutral position and subjected to a normalized haversine waveform, ranging from 0.3 kN to 30% of the predicted ultimate compressive strength using a servohydraulic materials testing system. The cycle-dependent changes in endplate deflection were measured at 0, 1000, 3000, and 5000 total cycles. At each time point, endplate deflection measurements were captured via the indenter’s displacement while specimens were exposed to a brief static force of 0.3 kN, 1 kN, and 3 kN, totaling 12 measurements per vertebra. Three separate linear mixed effects models were used to evaluate the impact of loading magnitude, loading cycles, endplate level and the proportion of the nucleus occupying endplate area on superior and inferior endplate deflection within each joint. A fourth linear mixed effects model was used to evaluate the impact of loading magnitude, loading cycles, and joint level on the magnitude of the differences between superior and inferior endplate deflection. Utilizing this novel methodology, this study was the first to quantify endplate deflection under cyclic loading conditions, observing greater deflection of the inferior endplate across all spinal levels, except at baseline (0.3 kN, 0 cycles). This method also enabled comparison of deflection rates between endplates, with the C4C5 and C5C6 inferior endplates showing a significantly greater rate of deflection during the first 1000 cycles. Among joints, C4C5 exhibited the largest difference in superior and inferior endplate deflection compared to C3C4 and C5C6. Endplate deflection was not influenced by the proportion of the nucleus occupying endplate area at any spinal level. Lastly, as the first study to examine endplate deflection in porcine cervical vertebrae, the observation of greater inferior endplate deflection being consistent with human cadaveric studies further supports the validity of this model. Overall, this study demonstrates the utility of a novel methodology for measuring and comparing superior and inferior endplate deflection under cyclic loading.
Zero-Knowledge Proof-Enabled SAT Co-processor for Blockchain Systems
(University of Waterloo, 2025-11-26) Yusiuk, Vladyslav
This thesis explores the possibility of building classical SAT solvers in Circom Domain Specific Language to create zero-knowledge proofs (ZKPs) usable in blockchain contexts. I implemented DPLL and Chaff as arithmetic circuits within Circom and analyze them based on constraint count, proving delay, and zk-SNARK verification layers. With this evaluation, the aim is to determine the feasibility of solvers integration into off-chain computation systems and rollup-centric architectures on Ethereum. The findings indicate that incorporating SAT solvers within zero-knowledge circuits is achievable though some degradation in efficiency occurs based on algorithm used and input representation. This research provides a thorough assessment of known SAT methods across an unconventional boundary, linking symbolic logic with blockchain technologies reliant on zk-SNARKs.
Development of High Strength Aluminum Alloys for Directed Energy Deposition Additive Manufacturing
(University of Waterloo, 2025-11-26) Waqar, Taha
Among additive manufacturing (AM) techniques, directed energy deposition (DED) is of particular interest for structural Al alloys, as it combines the faster cooling rates with the flexibility to repair or build large-scale geometries. The localized thermal cycling inherent to the DED process influences solidification behavior, grain refinement and precipitate evolution for high strength age-hardenable Al alloys such as Al 7075, which in turn governs the mechanical performance. These capabilities position DED as a promising pathway for expanding use of high strength heat-treatable aluminum alloys in aerospace and automotive applications where a good strength to weight ratio is crucial. However, Al 7075 tends to crack during solidification and possesses a limited service temperature range. The research conducted explores the tailoring of an existing Al 7075 composition and delves into the development of novel Al alloy compositions for DED-AM processes. In the initial phase of the research, laser-directed energy deposition of Al 7075 wire feedstock enhanced with TiC nanoparticles to promote grain refinement was investigated. It was found that the combination of high laser power (3400 W) along with low travel speed (400 mm/min) and low wire feed speed (400 mm/min) resulted in the reduction of lack of fusion defects and reducing cracks within the multilayer prints. However, substantial evaporation during printing led to a reduced amount of Mg and Zn bearing phases in the as-printed samples. It was shown that the direct aged sample heated for 5 hours was of comparable hardness to the T6 (solution heat treated and then artificially aged) sample (115 HV0.5), which highlights the presence of solute trapping in the as-printed material. To compare the behavior of the same Al 7075 + TiC wire feedstock under arc-based solidification conditions, the research continued to investigate the microstructural evolution and mechanical response of Al 7075 reinforced with TiC nanoparticles processed via arc-based DED, with a particular focus on aging behavior. Grain refinement was primarily attributed to heterogeneous nucleation and grain boundary pinning by TiC clusters. Moreover, TiC inoculants influenced solute redistribution, driving segregation of Mg and Cr, which in turn altered the precipitation behavior during aging. Heat-treated samples revealed the co-formation of MgZn₂ strengthening precipitates and the E-phase (Al18Mg3Cr2), with the latter contributing to the heterogeneous distribution of precipitates. These findings highlight both the benefits and challenges of TiC inoculation in tailoring microstructure and age-hardening response in arc-DED processed Al 7075 alloys. The second phase of the research presents the design and evaluation of a novel Al-Ce-Mg alloy tailored for wire arc-DED. The objective was to overcome the limitations of conventional high-strength aluminum alloys, which suffer from solidification cracking, volatile element loss, and poor thermal stability at elevated temperatures. Alloy selection was guided by CALPHAD simulations, leading to the identification of a near-eutectic Al-10Ce-9Mg composition. Thin-wall structures were fabricated, and porosity was quantified using micro-computed tomography, supported by high-speed imaging that revealed oxide-film entrapment as the dominant cause of porosity. The solidified microstructure consisted of α-Al, eutectic, and primary Al₁₁Ce₃ phases, as well as β-AlMg phase, which contributed to both strength and thermal stability. Compression testing demonstrated high room-temperature strength but brittle failure. At elevated temperatures, however, the alloy retained superior strength compared to conventional precipitation-strengthened Al 7075 alloy, even after extended thermal exposure. This observation was attributed to the stability of Al-Ce intermetallics. Incorporation of Sc into Al-Ce-Mg alloys can provide a dual strengthening and thermal stabilizing effect. Therefore, in the final phase of the conducted research, an Al-8Ce-8Mg-0.2Sc alloy was developed. Laser surface remelting was employed to replicate AM-like conditions, producing a refined bimodal grain structure and fragmenting coarse Al₁₁Ce₃ networks into discontinuous, blocky morphologies. Compared to the as-cast state, the remelted alloy exhibited increased hardness (114.5 HV1 vs 133 HV1), aided by refined grains and secondary phases such as Al11Ce3 and Mg2Si. Direct aging produced an irregular hardening response, with peak hardness achieved at 375 °C for 1 h due to the precipitation of coherent Al₃Sc nanoprecipitates. Long-term thermal exposure at 200 °C for up to 1000 hours showed negligible hardness loss and minimal coarsening of Ce-bearing intermetallics. Strengthening contributions were dominated by Al₃Sc precipitation, supported by solid-solution, grain refinement, dislocation hardening, and stable Al₁₁Ce₃ dispersoids.
Heterogeneous Decomposition of Convolutional Neural Networks Using Tucker Decomposition
(University of Waterloo, 2025-11-26) Mokadem, Frank
Convolutional Neural Network (CNN) remain the architecture of choice for computer vision tasks on compute-constrained platforms such as edge and personal devices, delivering both close to state-of-the-art performance metrics and linear inference complexity with respect to input resolution and number of channels. However, the deployment of larger and more complex CNN architectures is limited by the restrained memory offered by such platforms. This brings about a need to compress pretrained CNN into smaller models in number of parameters while controlling for degradation in performance. This thesis tackles CNN compression using low rank approximation of convolution layers using Tucker Decomposition (TD). We introduce a new heuristics-based Neural Architectural Search procedure to select low rank configurations for the convolution tensors, which we call Heterogeneous Tucker Decomposition (HTD). Standard low rank approximation using TD factorizes and approximates convolution layers using uniform ranks for all convolution tensors, then applies a few fine–tuning epochs to recover degradation in performance. An approach we show to be suboptimal against a heterogeneous selection of ranks for each convolution layer, followed by same number of fine-tuning epochs. Our primary contribution is the development and evaluation of TD, which applies layers-pecific compression rate (low rank divided by full rank) inferred from a Neural Architectural Search (NAS) process. Furthermore, we introduce a sampling heuristic to efficiently explore the search space of layer-specific compression rates, thus preserving performance while significantly reducing search time. We present a mathematical formulation for the HTD optimization problem and an NAS algorithm to find admissible solutions. We test our approach on multiple varieties of CNN architectures: AlexNet, VGG16, and ResNet18, adapted for the MNIST classification task. Our findings confirm that HTD performs better than TD on all models tested. For the same compression rate, HTD enables to recover a higher precision after fine-tuning, with gains ranging from 1.2% to 5.8%. For equivalent accuracy targets, HTD delivers 15-30% higher compression rates than TD. This thesis advances Neural Architectural Search by highlighting the efficacy of heterogeneous tensor decomposition approaches. It provides a robust framework for their implementation and evaluation, with significant implications for deploying convolutional deep learning models in resource-limited settings. Future work will explore incorporating low-rank constraints as a regularization objective during training, potentially enabling end-to-end compression-aware optimization.
Proactive Characterization of Wildfire Impacts on Drinking Water Treatability
(University of Waterloo, 2025-11-26) Bahramian, Soosan
Forested catchments are important sources of drinking water globally. They are increasingly threatened by disturbances, prominently climate shocks, including large wildfires. Wildfires alter watershed hydrology and biogeochemistry, leading to reduced infiltration, increased overland flow, and enhanced delivery of sediments, burned vegetation, and pyrogenic material into aquatic systems. Such inputs can alter drinking water source quality and challenge treatability. Ash is the residual material from wildland fuel combustion, composed of mineral particles and organic matter that can leach into water. While inorganic dissolved compounds from ash can impact water quality by, for example, increasing ionic strength and alkalinity, water-extractable organic matter (WEOM) from wildfire ash contributes to increased post-fire dissolved organic carbon (DOC) concentrations. During drinking water treatment, higher DOC concentrations increase chemical demand (e.g., coagulant, disinfectant), enhance the formation of potentially harmful disinfection by-products (DBPs), cause taste and odor issues, and promote bacterial regrowth in distribution systems. These impacts may also necessitate new infrastructure to manage changes in source water quality, ultimately increasing overall treatment costs. Although they cannot reflect all watershed processes, bench-scale evaluations provide valuable insights into wildfire impacts on drinking water treatability by isolating treatment-relevant mechanisms at controlled laboratory conditions. However, different approaches used to prepare wildfire ash-impacted waters (WAIWs) limit the inferences that can be drawn from them. Here, key factors (e.g., mixing duration and condition, ash-to-water ratio, and source water quality) that can impact organic matter leaching from wildfire ash to water were investigated. WEOM concentration increased within the first 24 hours of mixing before plateauing or declining as mixing progressed, regardless of ash type and background water source. Continuous mixing yielded higher WEOM concentrations than stagnant conditions, indicating that particle-particle interactions and surface exposure enhanced leaching. WEOM yield also decreased as ash-to-water ratios increased. Despite anecdotal suggestions, a relationship between wildfire ash color and WEOM concentration was not observed (Chapter 2). Wildfire ash collection methods may also impact inferences drawn from bench-scale drinking water treatability assessments. Unburned vegetation, rocks, or other debris may have physico-chemical properties different from those of ash deposits; thus, increasing uncertainty in treatability assessments. Dry ash homogenization methods (i.e., manual separation, sieving, and pulverization) were investigated because they may mitigate these impacts. Sieving was shown to be the most practical and reliable method for ensuring ash homogeneity. Pulverization enhanced organic matter release from large particles by increasing surface area, but it also generated aerosolized ash, complicating sample handling. In addition, pulverization altered WEOM character, potentially by increasing the availability of smaller organic matter compounds previously encapsulated within ash particles or by mechanically fragmenting larger organic molecules into smaller compounds (Chapter 3). Subsequent investigations examined the role of settleable ash solids (SAS), a previously overlooked fraction of wildfire ash. SAS substantially increased water alkalinity and make pH control for coagulation extremely difficult. Although pH adjustment enhances DOC removal from WAIW, SAS increased acid demand substantially. The removal of SAS reduced both alkalinity and acid demand; however, as ionic strength was concurrently reduced, floc formation and turbidity reduction for a given coagulant dose decreased somewhat. A limited complementary analysis was conducted to evaluate whether atmospheric ash deposition could also act as a significant driver of source water quality and treatability change. While the impact of atmospheric deposition of ash on water alkalinity depends on the surface area of water body, only exceptionally high atmospheric ash loading could meaningfully alter source water alkalinity in reservoirs that hold large volume of water (Chapter 4). Wildfire ash alters multiple aspects of water quality concurrently, including turbidity, DOC concentration and character, and alkalinity, so its overall implications for water treatment cannot be adequately assessed by examining individual mechanisms in isolation. Coagulation experiments with WAIWs demonstrated these interacting impacts. At low coagulant (i.e., alum) doses, turbidity was effectively reduced, yet DOC removal remained limited, despite pH adjustment to coagulant-specific optima. Enhanced coagulation combined with higher alum doses improved DOC removal but introduced trade-offs, as turbidity reduction declined somewhat because of reduced ionic strength associated with decreased alkalinity. The results indicated, while wildfire ash can severely deteriorate water quality by increasing turbidity, alkalinity, DOC concentration, and aromaticity, which may increase coagulant demand or necessitate more advanced treatment methods, the underlying coagulation mechanisms for WAIW remain consistent with those in natural waters. Thus, wildfire ash does not present fundamentally new challenges to coagulation; rather, the magnitude of water quality changes following wildfire can pose risk to treatment performance and operational resilience (Chapter 5). Collectively, this research demonstrates that while bench-scale studies cannot fully replicate the complexity of post-fire watershed processes and wildfire impacts on water quality, they remain essential for isolating and investigating the specific effects of wildfire ash on drinking water treatment processes. Accordingly, it is practical to adopt methods that maximize the extraction of organic matter from wildfire ash and represent worst-case treatment scenarios. These methodological insights help ensure the comparability of bench-scale investigations. This work also shows that wildfire impacts coagulation primarily by complicating pH control and deteriorating drinking water source quality, increasing the need for more intensive treatment processes. Overall, this research establishes a robust methodological foundation for reliably assessing wildfire ash impacts on water quality and for informing the development of strategies to mitigate wildfire impacts on drinking water treatability.
Design and Implementation of a Robust State of Charge Estimation Approach for a Single Battery Cell, a Hardware-in-the-Loop Test Bench, and a Battery Disconnect Unit for an Electric Vehicle Battery Pack
(University of Waterloo, 2025-11-25) Pham, Nguyen Truong Son
As transportation electrification accelerates, battery-powered vehicles, including cars, airplanes, and boats, are rapidly emerging. This thesis provides solutions and practical insights on two key topics: implementing robust machine learning algorithms on commercial Battery Management System (BMS), and building a high-performance Battery Disconnect Unit (BDU). The experience was gained during participation in the North American Battery Workforce Challenge. First, two machine learning approaches for State of Charge (SoC) estimation are introduced. The first approach is an adaptive algorithm using SoC-OCV-T (State of Charge-Open Circuit Voltage-Temperature) lookup table and Extreme Learning Machine (ELM). The experiment began at 100% SoC, with temperature ranging from -20°C to 60°C. From -20°C to 0°C, the maximum absolute error (MAE) ranged from 0.030 to 0.025. In the mid-range from 5°C to 40°C, the MAE decreased to within 0.015 to 0.020 range. Lastly, at higher temperature range of 45°C to 60°C, the MAE was below 0.013. In the second approach, advanced differential features are added to improve the accuracy of the ELM model, particularly below 0°C. Under noisy condition, both the maximum absolute error (MAE) and the root mean square error (RMSE) were reduced to below 1.5% at -20, 20, and 60°C. Both algorithms were validated on a customized Hardware-in-the-loop (HIL) test bench. The HIL platform was developed to streamline validation of algorithms such as SoC estimation. Finally, the thesis details the design and testing process for the BDU, highlighting key design considerations, test results, and engineering challenges.
Robust Nonparametric Inference on Manifold Spaces
(University of Waterloo, 2025-11-25) Mozaffari, Ahmad
We propose rank-based procedures for robust and nonparametric statistical inference on manifold spaces. Particularly, we focus on the problems of multi-sample hypothesis testing, multiple change point analysis, and statistical process monitoring when data lie on a Riemannian manifold. These methodologies provide a unified framework to deal with various types of data structures such as matrices, curves, surfaces, networks, to name a few. These types of datasets frequently appear in a broad set of applications such as communication networks, manufacturing, computer vision, autonomous systems and robotics. We evaluate the proposed methods considering various types of object data such as matrices, curves, text mining data, networks, shape data and landmarks. In Chapter 2, we develop robust and nonparametric methods for hypothesis testing when data lie on a manifold. We demonstrate that ranks generated from data depth can be used for two-sample and multiple sample hypothesis testing of change in location and scale parameters. Several important properties of these tests such as asymptotic convergence, size and power, robustness with respect to qualitative-robustness and breakdown point are developed under mild nonparametric assumptions. These tests have several advantages, they have a simple distribution under null, they are computationally cheap, and they enjoy invariance properties. We demonstrate the efficacy of these methods with a numerical simulation and a data analysis. We show that these tests are robust when data are heavy tailed or skewed, and have higher power compared to their competitors in some situations, while still maintaining a reasonable size. In Chapter 3, we propose robust and nonparametric single and multiple change point detection methods for stochastic processes defined on manifolds. These methods consider a variant of CUSUM statistic which is based on the rank of data depth. We demonstrate that changes in the rank of depth values can be used to detect change in the distribution of data lie on manifolds. To detect more than one change point, we consider binary segmentation and wild binary segmentation algorithms along with the proposed data depth rank CUSUM statistic. We demonstrate that both of these algorithms are consistent estimators of the number of change point(s) and the location of change point(s). In addition to asymptotic results, we develop nonasymptotic sharp bounds for single and multiple change point estimators. These test statistics can be applied to both intrinsic and extrinsic manifold analysis frameworks. In simulation, we compare our methods against several methods from the literature, and demonstrate that the proposed methods outperform their competitors in some situations where dataset is contaminated with outliers. We also present the application of our methods to vehicle health monitoring, traffic monitoring on highways, and mall pedestrian surveillance. In Chapter 4, we extend these methods to the setting of statistical process monitoring. We investigate statistical process monitoring scheme on general metric spaces, and propose exponentially weighted moving average, CUSUM, and Mann-Whitney moving average Shewhart control charts using rank of data depth. These methods are nonparametric and robust to outliers through the use of data depth ranks. We show that when sample size is large, our methods have simple behaviour under the null hypothesis. Since our methods are based on data depth ranks, we do not need the estimate of covariance operator which makes our method computationally cheap. Such advantages make these methods a favorable choice for online process monitoring. We demonstrate the robustness of these methods theoretically and numerically. We extract several nonparametric control charts from the literature for comparative study. Simulation results indicated that the proposed methods outperform their competitors in many situations in terms of out-of-control average run length, while keeping the in-control average run length at a reasonable level. We present the application of our methods to laser power-bed fusion additive manufacturing process. In Chapter 5, we present some possible directions for future research related to dynamic network and longitudinal data analysis on Riemannian manifolds. It is anticipated that the contributions achieved in this thesis will be applicable to a wide range of interdisciplinary research problems.
Towards Honest, Practicable and Efficient Private Learning
(University of Waterloo, 2025-11-24) Mohapatra, Shubhankar
Protecting our personal information is a major challenge in today's data-driven world. When scientists and companies analyze large datasets, they need a way to ensure our individual privacy isn't compromised. This thesis focuses on Differential Privacy, a powerful, mathematical guarantee that places a strict, verifiable limit on how much personal information can be leaked, even if an attacker has the worst-case advantage. Researchers have developed various sophisticated algorithms to accomplish useful tasks, like building machine learning models or generating realistic synthetic data, while maintaining Differential Privacy. Crucially, these operations must be conducted within a predetermined, strict limit, often referred to as the "privacy budget." This budget mathematically quantifies the total acceptable loss of privacy for the entire process, enforcing a crucial trade-off between data utility and individual protection. All routine procedures of the machine learning pipeline, including data cleaning, hyperparameter tuning, and model training, must be performed within the budget. Several tools can perform these tasks in disjunction when the dataset is non-private. However, these tools do not translate easily to differential privacy and often do not consider the cumulative privacy costs. In this thesis, we explore various pragmatic problems that a data science practitioner may face when deploying a differentially private learning framework from data collection to model training. In particular, we are interested in real-world data quality problems, such as missing data, inconsistent data, and incorrectly labeled data, as well as machine learning pipeline requirements, including hyperparameter tuning. We envision building a general-purpose private learning framework that can handle real data as input and can be used in learning tasks such as generating a highly accurate private machine learning model or creating a synthetic version of the dataset with end-to-end differential privacy guarantees. We envision our work will make differentially private learning more accessible to data science practitioners and easily deployable in day-to-day applications.
Decolonizing Disability: access without erasure
(University of Waterloo, 2025-11-24) Musa, Kenyo
This thesis rethinks disability in the Global South by turning to Nigerian open-air markets, rather than institutional settings as primary sites of inquiry. More than points of exchange, these markets are cultural and civic arenas where economic activity intersects with social connection, mutual care, and collective identity. Marketplaces often function as “third places,” sustaining relationships, preserving communal memory, and hosting the negotiation of public life alongside commerce. Within this context, disability is framed not as a fixed biological deficit but as a condition shaped by environments, social structures, and cultural narratives. Drawing on critical disability studies, African epistemologies, and the concept of relational access, the project positions design as a continual negotiation between bodies, space, and practices of care, challenging functionalist approaches that reduce access to technical compliance. A central critique advanced in this research is the co-option of accessibility language to legitimize exclusionary development. In postcolonial African cities, modernization projects often promise accessible infrastructure while simultaneously displacing those most reliant on markets for survival. Under the banner of “ultra-modern” shopping complexes, elderly traders, people with impairments, and low-income groups are frequently priced out, excluded from decision-making, and stripped of long-standing spatial and economic networks. In such cases, access becomes a rhetorical tool for privatization and displacement rather than a pathway to justice. This thesis argues that genuine access must go beyond token infrastructural features to address the deeper social, economic, and political systems that sustain participation. Methodologically, the study combines critical literature with graphical anthropology, using mapping and diagramming to interpret the spatial conditions of Nigerian markets. This approach, informed by Jos Boys’s “Having a Body” framework, highlights how non-normative bodies engage space, revealing barriers such as uneven ground, sensory overload, or disorientation, alongside supports like shared seating, mutual caregiving, and assistance from load carriers. Through this iterative method, the research develops strategies grounded in lived realities rather than abstract standards, emphasizing collective arrangements that sustain participation. The design proposal focuses on Jos Main Market, a once-celebrated hub now in disrepair after arson and neglect. The intervention introduces a spine that organizes utilities and circulation while embedding care nodes for prayer, rest, sanitation, and basic medical support. A market workshop provides space for repair, fabrication, and low-cost assistive devices, affirming resourcefulness and local skill as vital forms of access. At its center, a market plaza serves as a commons, enhancing visibility and offering social services such as collective childcare, community kitchens, thrift collectives, and meeting areas. Together, these spaces strengthen support networks and ensure vulnerable groups remain active within the civic life of the market. Ultimately, the thesis positions open-air markets as sites that resist the misuse of accessibility rhetoric by grounding access in reciprocity and collective care. Rather than treating informality as disorder to be erased, it demonstrates how markets themselves model alternative approaches to spatial justice. By centering lived experience, this project advances a decolonial vision of disability design, one where access is relational, negotiated, and inseparable from economic survival and community life.
Monitoring risk from contaminant mixtures in stormwater with water quality measurements, bioassays, and bioassessment
(University of Waterloo, 2025-11-20) Izma, Gab
Urban stormwater management ponds (SWPs) are increasingly valued not only for their role in mitigating runoff but also for the biodiversity they support in densely developed environments. However, these systems receive complex contaminant mixtures from urban runoff, including pesticides, pharmaceuticals, industrial chemicals, and metals. These pollutants can accumulate in biologically active compartments like biofilms, posing risks that are not always captured by traditional water-based monitoring. My thesis investigates the nature, accumulation, and ecological effects of contaminants in SWPs using a combination of chemical, biological, and toxicological approaches. The objectives of my research were to: (1) characterize pesticide contamination in SWPs using water, biofilm, and passive samplers; (2) quantify pesticide accumulation in biofilms and identify influencing factors; (3) assess the toxicity of contaminated biofilms through dietary exposure; (4) survey the broader suite of urban contaminants in SWPs to develop a stormwater contaminant signature; and (5) examine relationships between environmental conditions and aquatic community composition. In Chapter 2, I surveyed 21 SWPs in Brampton, Ontario for pesticide contamination. I compared three monitoring approaches across the ponds - time-integrated water sampling, biofilm cultured on artificial substrates, and organic-diffusive gradients in thin films (o-DGT) passive samplers - finding that o-DGTs had the highest pesticide detection rates. However, issues with reproducibility in passive sampler data highlighted the challenges of using them for quantitative risk assessment. Despite generally low concentrations in water and biofilm samples, the widespread detection of diverse pesticide classes across all three matrices emphasized the chronic, mixture-based exposures in these ponds and informed recommendations for future monitoring strategies. In Chapter 3, I further investigated the use of biofilms as a sensitive and ecologically relevant matrix for contaminant monitoring. Examining a wider set of pesticide analytes, I found that over half of the pesticides detected in biofilm samples were not detected in water, suggesting that conventional sampling approaches may overlook important alternative exposure routes. Calculated bioconcentration factors (BCFs) varied widely and were not well explained by pesticide properties or water quality variables, pointing to the complexity of contaminant uptake mechanisms. To test the potential toxicity of these contaminated biofilm samples, in Chapter 4 I conducted a series of dietary exposure assays with two invertebrate grazers. Mayfly nymphs (Neocloeon triangulifer) and juvenile freshwater snails (Planorbella pilsbryi) fed with contaminated biofilms from the SWPs showed reduced survival and growth compared to controls. Although the test results did not always correlate with measured pesticide levels, these results support the ecological relevance of biofilm-mediated exposure and suggest the presence of additional stressors not captured in targeted chemical analyses. I further expanded the chemical scope in Chapter 5 by analyzing over 700 unique urban contaminants across water, biofilm, and o-DGT samples. In total, 200 organic compounds were detected, including personal care products and traffic-related pollutants, as well as persistent elevated levels of fecal indicators and chloride. From these data, I developed the Urban Stormwater Contaminant Signature (USCS): a proposed list of common, environmentally relevant compounds to guide future monitoring and toxicity testing in urban aquatic systems. Finally, in Chapter 6 I examine how environmental variables shape aquatic community composition. Diatom and macroinvertebrate assemblages sampled from the SWPs were dominated by pollution-tolerant taxa, with diatoms responding primarily to water quality (e.g., nutrients, chloride, herbicides) and macroinvertebrates more sensitive to habitat features associated with pond naturalization. Landscape-scale metrics (e.g., impervious cover) calculated from buffer zones had limited predictive power, suggesting that local conditions and upstream drainage characteristics play a stronger role in shaping biological communities. This research highlights the need to expand contaminant monitoring in stormwater systems beyond conventional water sampling, incorporating matrices like biofilm and tools such as passive samplers to better reflect the complexities of exposures in urban environments. The detection of numerous unmonitored or rarely assessed compounds suggests that current regulatory frameworks may underestimate the complexity and risk of urban chemical mixtures. Recognizing stormwater ponds as both infrastructure and ecosystems calls for more ecologically grounded approaches to design, management, and risk assessment; ones that support biodiversity alongside water quality improvement and flood protection.
Development of Functional Binders and Li2S@Carbon Nanocomposites for High-Performance Lithium Sulfide Batteries
(University of Waterloo, 2025-11-20) Huang, Zhe
Lithium sulfide (Li2S) is a promising cathode material for lithium-sulfur batteries (LSBs) owing to its high theoretical capacity (1166 mA h g-1) and potential for safer, scalable battery architectures. In contrast to sulfur cathode, Li2S enables direct pairing with commercial anode materials, avoiding the safety risks of lithium metal. Despite these merits, practical application of Li2S is challenged by its hygroscopic nature, which forms insulating LiOH/Li2O surface layers that cause a large first-charge overpotential; its high melting point (~938 °C), which prevents melt infiltration into carbon frameworks; sluggish redox kinetics; severe polysulfide dissolution; poor conductivity. Addressing these challenges requires integrated advances in binder design, electrode engineering, and cathode nanostructuring. The large first-charge overpotential due to the insulating LiOH/Li2O surface layer in Li2S-LSBs hinders activation and induces irreversible side reactions. Chapter 3 proposes mitigating the activation barrier by exploiting the reaction between polyvinylidene fluoride (PVDF) binder and LiOH/Li2O through dehydrofluorination. The overpotential was successfully reduced from 3.74 V with 30 min slurry grinding to 2.75 V by extending slurry stirring to 48 h. However, PVDF was also found to react with Li2S itself, partially consuming active material and lowering discharge capacity. Overall, this study provides mechanistic insights into the origin of Li2S activation overpotential and demonstrates the dual role of conventional PVDF binders, where slurry processing with PVDF can effectively reduce the first-charge barrier, while also highlighting the limitations of PVDF as a binder for Li2S electrodes. Since PVDF proved unsuitable for Li2S electrodes, Chapter 4 investigates alternative binders capable of enhancing the electrochemical performance of Li2S-LSBs. A binder based on a zinc acetate triethanolamine (Zn(OAc)2·TEA) complex was developed, which not only provides strong polysulfide-trapping ability but also exhibits redox catalytic activity, leading to markedly improved capacity, rate capability, and cycling stability compared with PVDF. To further reinforce electrode integrity and improve dispersion stability, polyethylenimine (PEI) was incorporated to form a Zn(OAc)2·TEA/PEI hybrid binder. Electrochemical testing showed that Li2S cathodes employing Zn(OAc)2·TEA/PEI with 10 wt.% PEI achieved superior rate performance, high discharge capacity, and excellent long-term cycling stability. An additional advantage of these binders is their fluorine-free composition, which aligns with sustainability goals and complying with emerging regulations, including EU restrictions on per- and polyfluoroalkyl substances (PFAS). In Chapter 5, an efficient precursor solution infiltration-decomposition strategy was invented to synthesize Li2S@Carbon nanocomposites under mild conditions, overcoming the challenges of Li2S’s high melting point, poor solubility, and the large particle size of commercial Li2S. In this approach, Li2S was first reacted with carbon disulfide (CS2) in ethanol at ambient temperature to form a highly soluble lithium trithiocarbonate (Li2CS3) precursor, which was readily infiltrated into mesoporous Super P carbon (SP). Subsequent thermal decomposition of Li2CS3@SP at 400 °C produced Li2S@SP-400 nanocomposites with a Li2S:SP mass ratio of 60:40, containing finely dispersed Li2S particles (~11 nm) uniformly confined within the Super P matrix. Electrochemical testing demonstrated that these nanocomposites delivered a high discharge capacity of 821 mA h g-1 (Li2S) at 0.1 C, equivalent to 1190 mA h g-1 (S), and exhibited superior rate capability and cycling stability compared to commercial Li2S, non-infiltrated Li2S nanoparticles, and melt-infiltrated sulfur composites (S@SP). The thermal decomposition of Li2CS3 precursor releases a large amount of CS2 gas (~62 wt.% of the precursor), which creates internal voids and limits the in-pore Li2S loading. To address this, Chapter 6 builds upon precursor infiltration-decomposition method with a multi-cycle strategy, enabling higher Li2S content and in-pore loading. Using mesoporous Super P as the conductive host and Li2CS3 as the precursor, repeated infiltration-decomposition cycles progressively increased the pore filling factor (FF) and in-pore Li2S loading (IPL), from FF = 38% and IPL = 30% for Li2S@SP-1 (one cycle) to FF = 91% and IPL = 73% for Li2S@SP-5 (five cycles), while also raising the overall Li2S content to 70 wt.%. Direct structural evidence from XRD and SEM confirmed reduced crystallite size, suppressed external deposition, and uniform Li2S distribution in the optimized Li2S@SP-5. Electrochemical tests demonstrated that Li2S@SP-5 delivered an initial discharge capacity of 807 mA h g-1 (Li2S) at 0.1 C, 598 mA h g-1 (Li2S) in the first cycle at 1.0 C, and retained 376 mA h g-1 (Li2S) after 500 cycles at 1.0 C. To construct high-performance cathodes, the functional binder from Chapter 4 was combined with the high in-pore loading Li2S@SP from Chapter 6. This attempt failed because Zn(OAc)2·TEA/PEI-based binders exhibited limitations with highly reactive nanoscale Li2S, resulting in diminished binding effectiveness. Chapter 7 therefore introduces a series of polyethylenimine-epoxy resin (PEI-ER) binders, where high-molecular-weight PEI anchors and catalyzes polysulfides while epoxy crosslinking reinforces mechanical stability, making this strategy particularly effective for stabilizing nanoscale Li2S composites. The in-situ crosslinking method further improved processing by removing the short crosslinking time window and enabling uniform networks without altering Li2S@SP morphology. Electrochemical tests showed the optimized in-situ crosslinked PEI-ER1:1 binder achieved 928 mA h g-1 at 0.05 C, 688 mA h g-1 in the first cycle at 0.5 C and retained 325 mA h g-1 after 1000 cycles at 0.5 C with stable Coulombic efficiency. SEM confirmed its compact structure, establishing in-situ PEI-ER crosslinking as a robust binder strategy for nanoscale, high-loading Li2S cathodes. Chapter 8 serves as the culmination of these research projects, combining the optimized Li2S@Carbon cathodes from Chapter 6 and functional binders developed from Chapter 7 with commercial Si/C anodes to successfully assemble and evaluate lithium-anode-free full cells, with PVP used as a baseline comparison, thereby demonstrating their practical feasibility. The in-situ crosslinked PEI-ER1:1-based full cell batteries delivered 670 mA h g-1 at 0.1 C and retained 304 mA h g-1 after 100 cycles (~45% retention), outperforming PVP-based full cell batteries (582 to 250 mA h g-1, ~43%). At 0.5 C, the in-situ crosslinked PEI-ER1:1-based full cell batteries achieved 564 mA h g-1 after activation and maintained 377 mA h g-1 after 500 cycles (66.8% retention), while the PVP counterparts fell from 573 to 176 mA h g-1 (30.7%). These results underscore the binder’s role in stabilizing cathodes and mark the successful assembly of lithium-free-anode Li2S full cells with commercial Si/C anodes. In summary, this thesis addresses the critical challenges of Li2S cathodes, including the large first-charge overpotential, the drawback of PVDF consuming Li2S, the large particle size of commercial Li2S, the high melting point and poor solubility that hinder conventional Li2S@Carbon composite fabrication, and the limitations of binders when applied to nanoscale Li2S, each identified in the process of resolving the preceding issue. By systematically investigating these problems, this thesis advances functional binder design, exploits precursor chemistry, and engineers nanostructured composites, concluding with the successful demonstration of lithium-anode-free full cell batteries. Further improvements could be achieved by employing more efficient carbon hosts with tailored structures, developing high-loading electrodes, integrating solid-state electrolytes to mitigate polysulfide dissolution, and incorporating catalytic components to accelerate Li2S redox kinetics, thereby pushing Li2S-LSBs closer to practical, high-energy-density applications.
Validating & Measuring Influenza Vaccine Effectiveness among and against Cardiovascular Hospitalization
(University of Waterloo, 2025-11-19) Amoud, Razan
Background: Influenza is a viral respiratory infection that causes serious health outcomes such as hospitalization and death and represents an important public health burden globally. Vaccination is one of the most effective interventions to prevent influenza and its complications. Although administrative databases such as pharmacy billing claims are used to measure influenza vaccination status, little is known of the validity of these databases in Ontario. Patients with cardiovascular disease (CVD) may have an altered immune response, despite being at a higher risk of influenza complications. It is not known if the Vaccine Effectiveness (VE) in this population is comparable to the general population. Further, there is a lack of research in Canada evaluating influenza vaccine effectiveness against cardiovascular outcomes, particularly using robust study designs such as the test-negative design. Three interrelated studies were carried out. First, a validation study was completed to examine the accuracy of the combination of Ontario’s administrative data from pharmacy and physician billing claims in identifying an individual’s vaccination status. The second study assessed the influenza VE against laboratory-confirmed influenza among older adults hospitalized with CVD conditions in Ontario and examined sex and age group as potential effect modifiers in the association between influenza vaccination and laboratory-confirmed influenza. The third study measured influenza VE against acute CVD outcomes using the Test-Negative Design (TND) for the first time among older adults in Ontario who were hospitalized within three days of their influenza testing. Methods: In the first study, I validated the combined physician and pharmacy billing claims within administrative databases using the linked reference standard of self-report data from the Canadian Community Health Survey (CCHS). This study estimated sensitivity, specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV), with 95% Confidence Intervals (CI) of the estimates. In the second study, I used the TND to measure influenza VE against laboratory-confirmed influenza. The analysis included both crude and adjusted estimates accounting for potential confounders (sex, age group, neighbourhood income quintile, rurality, number of outpatient visits, beta-blocker medication use, statin medication use, receiving home care services, and influenza testing relative to the peak month of the season, and year of influenza season). In addition, I assessed effect modification of sex and age group on influenza VE by using two different methods: introducing interaction terms into the model and stratification. In the third study, I examined influenza VE against acute CVD outcomes, also using the TND. The exposure of interest was vaccination status, cases were those hospitalized for myocardial infarction, unstable angina, or stroke and testing positive for influenza, and controls were those hospitalized for a non-cardiovascular event and testing negative for influenza. Both crude and adjusted VE were estimated. Sensitivity analyses were performed to test the robustness of the findings by varying inclusion criteria, outcome definitions, and statistical adjustments. Results: In the first study, the CCHS identified 43% as vaccinated across the two survey cycles of 2013 and 2014. The sensitivity for the combined pharmacy and physician billing codes was 60.1% (95% CI 59.3%–61.0%), specificity was 98.5% (95% CI 98.3%–98.7%), PPV was 96.7% (95% CI 96.3%–97.1%) and NPV was 76.9% (95% CI 76.4%–77.5%). The second study included 1,159 patients, and almost half were vaccinated. Among vaccinated patients, 14% tested positive for influenza compared to 20% of unvaccinated patients.. Crude and adjusted VE were 32% [95% CI, 8–50%] and 43% [95% CI 20%–60%], respectively. Neither inclusion of interaction terms separately in the full model, nor stratification by sex or age group, revealed any evidence of effect modification. The third study included 33,710 hospitalized individuals who tested positive for influenza and had a CVD event (cases) or tested negative and did not have a CVD event (controls). There were 18,519 vaccinated individuals (55%). Among vaccinated patients, 0.4% tested positive for influenza and were hospitalized for a cardiovascular event, compared to 0.8% of unvaccinated patients. The adjusted influenza VE against cardiovascular outcomes was 43% [95% CI 25%–58%; p-value =0.0001]. Conclusion: Compared to past studies with only physician billing claims, the validation study provided improved performance measures of sensitivity, specificity, PPV and NPV values in the combined physician and pharmacy billing claims in identifying individual vaccination status in Ontario. The second study estimated influenza VE against laboratory confirmed infection and supports that influenza VE among older adults with CVD hospitalization is comparable to the general population. Also, no significant effect modification in VE was observed by the patient’s sex, or age. These findings suggest that the protective effect of the influenza vaccine against laboratory-confirmed influenza is consistent across key demographic and clinical subgroups within this high-risk population. The third study found that the influenza vaccine provides a significant protective effect against CVD outcomes. The global findings of this thesis emphasize the validity of administrative databases at estimating population-level vaccination rates and show the importance of influenza vaccination as an effective strategy to reduce both influenza hospitalization and CVD events. This unique research equips healthcare providers and policy makers with relevant findings to support their campaigns and recommendations on influenza VE, particularly in relation to CVD outcomes.
Turing Instability of a Closed Nutrient-Phytoplankton-Zooplankton Model with Nutrient Recycling
(University of Waterloo, 2025-11-19) Xu, Xiangye
We investigate Turing instability in a closed Nutrient–Phytoplankton–Zooplankton (NPZ) ecosystem that incorporates delayed nutrient recycling, formulated as a reaction–diffusion system. Although spatial diffusion typically enhances system stability, our study focuses on how differing diffusion rates among species can destabilize steady states and lead to the emergence of spatial patterns. To explore this, we first perform a linear stability analysis to identify the conditions under which Turing instability arises. These theoretical predictions are then validated through numerical simulations. Our study progresses systematically: beginning with a two-species model, extending to a threespecies system, and finally to a four species NPZD model. This stepwise framework provides both conceptual insight and quantitative understanding of how diffusion influences instabilities, offering a comprehensive perspective on pattern formation in multi-species plankton ecosystems.
Mitigating Hardware Trojan Risks in the Global IC Supply Chain: Pre- and Post-Silicon Detection Approaches
(University of Waterloo, 2025-11-19) Pintur, Michael
The integrity of modern systems is critically dependent on trust in the underlying hardware, yet complex Integrated Circuit (IC) supply chains introduce numerous vulnerabilities for malicious insertions. This thesis confronts the challenge of IC trust by examining two distinct detection methodologies, illuminating the fundamental trade-offs inherent in practical hardware verification under black-box conditions. The first contribution targets Trojan detection in Third Party Intellectual Property (3PIP) by adapting power-based side-channel fuzzing with Field-Programmable Gate Arrays (FPGAs). This investigation confirms that dynamic power analysis serves as an effective oracle for identifying the activation of a Trojan, creating a statistically significant side-channel anomaly. However, the work also demonstrates that random fuzzing is an impractical search strategy for discovering the low-probability trigger required for activation, highlighting a significant barrier to its widespread adoption. To overcome the limitations of methods requiring dynamic Trojan activation, this work explores static, on-chip sensing using Ring Oscillator Networks (RONs). This research addresses a gap in prior work by characterizing RON behaviour on a modern 28nm process and subsequently developing a statistical framework to distinguish malicious modifications from normal process variations. The proposed approach was validated against a benchmark hardware Trojan and successfully classified all Trojan-free and Trojan-infected devices. These results confirm that RON-based detection remains effective on 28nm process technology and demonstrate the robustness of the developed anomaly detection algorithm. By juxtaposing a dynamic, trigger-based detection method with a static, reference-based approach, this thesis illuminates the fundamental trade-offs inherent in hardware trust verification. The findings reveal a practical difference between the high specificity of dynamic analysis and the broad applicability of static verification. This research concludes that while physical side-channels are powerful tools, future progress will depend on developing solutions that effectively balance these competing demands, for a more comprehensive security strategy in the IC supply chain.
Deep Learning and Dynamical Systems Approaches to Critical Transitions in Socio–Climate and Complex Systems
(University of Waterloo, 2025-11-19) Babazadeh Maghsoodlo, Yazdan
This thesis explores how dynamical systems, stochastic processes, and deep learning can be integrated to study critical transitions in socio-climate and other complex systems. Chapter 1 establishes the conceptual foundation, introducing complex systems, tipping points, bifurcation theory, stochasticity, early warning signals, and the role of deep learning. It also highlights flickering as a precursor to collapse and motivates the importance of coupled socio-climate feedbacks. Chapter 2 develops a hybrid CNN--LSTM framework to classify bifurcations in noisy time series. Trained on synthetic dynamical models, the classifier generalises to empirical data and outperforms traditional early warning signals, offering a robust method to identify fold, Hopf, and transcritical bifurcations. Chapter 3 introduces a deep learning approach to detect flickering dynamics, noise-driven switching between alternative equilibria. The model distinguishes true flickering from noise-induced variance inflation across diverse systems and demonstrates applicability to empirical data such as palaeoclimate records and physiological signals, providing an early warning beyond variance-based methods. Chapter 4 presents a coupled socio-climate model where social behaviour feeds back on emissions and climate thresholds. Results show that social dynamics, such as faster learning rates or stronger norms, can delay or prevent climate tipping, while delays or weak norms accelerate collapse. This chapter highlights the potential of social tipping points to stabilize climate trajectories. Chapter 5 evaluates whether binary opinion models suffice to represent socio-climate interactions compared to richer spectrum models. Using replicator and Friedkin–Johnsen frameworks coupled to climate-carbon and forest-grassland systems, the study finds that binary models capture essential coupled dynamics with surprising accuracy, despite their simplicity. Together, the chapters demonstrate that combining dynamical systems theory, stochastic analysis, and deep learning yields powerful tools to anticipate tipping points. The findings advance both methodological development and practical insight, showing that human social responses can critically shape whether climate transitions are mitigated or exacerbated.
On Token Movement Problems
(University of Waterloo, 2025-11-19) Maaz, Stephanie
Classically, the study of computational problems has been primarily focused on what we call a static model of the world. Specifically, given a fixed unchanging input instance, e.g., a graph, the usual goal is to compute a fixed subset of vertices or edges that minimizes or maximizes some objective function. In addition to unchanging instances, the static model ignores the potential existence of prior (partially feasible) solutions as well as the costs associated with materializing a new (more optimal) one. These limitations become more apparent when one considers the dynamic model of the world in which we sometimes seek efficient transformations of a given system from some state to another. Upgrading public transport lines is a typical example of this since a reasonable strategy is expected to minimize criteria such as cost, environmental impact, and disruption time. It is therefore crucial for a corresponding computational problem to have knowledge of the current state of the system so as to seek a new, more desirable state, while minimizing the number of required "changes'' (and the number of undesired changes). Solution discovery and reconfiguration problems constitute two possible ways of addressing such computational problems arising in our dynamic model of the world, and they are the main topics of this thesis. Both problems model system states as configurations, such as sets of tokens placed on one of vertices or edges of a graph. Under our solution discovery problems, we start with an initial graph configuration and seek to transform it into any final configuration that satisfies a desired property (such as forming a shortest path) and where each vertex contains at most one token, using at most a given budget of token slides along graph edges. In the reconfiguration problems we study, both the initial and target configurations are specified, and we must determine whether one can be transformed into the other within a given budget of token multi-slides, where a token moves along a path of vertices with no other tokens. These token movements capture real-world constraints where changes must be local, that is, prohibiting arbitrary relocation, as seen in applications ranging from robot motion planning to quantum circuit compilation. This thesis contributes to the computational complexity landscape for these transformation problems. For solution discovery problems where the target property is to form a matching, vertex/edge cut, or shortest path, we show that even though these properties are efficiently computable in the static setting, their transformation variants are NP-hard. Similarly, our reconfiguration problems are NP-hard regardless of whether tokens are distinguishable or indistinguishable. This necessitates a parameterized complexity approach, which provides a more refined analysis by developing (fixed-parameter tractable) algorithms whose running times are exponential only in carefully chosen parameters, which are typically small in practice, while remaining polynomial in the input size. Under parameterized complexity, we investigate these transformation problems, including solution discovery problems for polynomial-time solvable properties (matching, vertex/edge cut, shortest path) and NP-hard properties (independent set, dominating set, vertex cover), under the fundamental parameters of number of tokens k and transformation budget b. Additionally, we examine how certain structural parameters affect the parameterized complexity; particularly, we analyze the independent set, dominating set, and vertex cover solution discovery variants with respect to the parameter pathwidth. Beyond fixed-parameter tractability, we investigate the kernelization complexity of the studied solution discovery problems to understand the limits of preprocessing. Kernelization provides a mathematical framework for analyzing data reduction, asking whether large instances of a problem can be efficiently compressed to equivalent instances whose size depends only on the parameter. A polynomial kernel exists when instances of a problem can be reduced to a size that is at most polynomial in the parameter, indicating that effective preprocessing is possible. For problems that we prove admit fixed-parameter tractable algorithms, we employ advanced techniques to establish upper and lower kernel bounds. Our analysis extends to combined parameters, such as examining how the pathwidth structural parameter interacts with the transformation budget to affect preprocessing possibilities. Finally, we transcend individual problem analysis through meta-theorems that characterize entire families of solution discovery problems using descriptive complexity. Rather than proving similar results repeatedly for each graph property, we investigate general theorems for all solution discovery problems whose target properties are definable in logic, particularly first-order (FO) or the more expressive monadic second-order (MSO) logic. We prove that MSO solution discovery is fixed-parameter tractable when parameterized by the structural parameter neighborhood diversity, exploiting vertex type equivalences to reduce the search space. Conversely, we demonstrate that even FO solution discovery is hard classically and under parameterized complexity assumptions for several natural structural parameters including twin cover, modulator to stars, and modulator to paths numbers. Through these results, we delineate precise boundaries within a well-studied hierarchy of structural parameters, establishing where in the hierarchy meta-tractability results are possible.
OPTIMIZATION OF BATTERY-FREE WATER LEAK DETECTORS
(University of Waterloo, 2025-11-18) OGINNI, ADETOUN
Battery-free water leak sensors offer a sustainable solution for real-time leak detection by harvesting energy from water-triggered reactions to power communication modules such as BLE (Bluetooth Low Energy). A key challenge for their practical use is ensuring reliable and rapid activation of the BLE electronics under varying conditions. This work investigates how material loading and sensor design parameters, such as water inlet size and elevation, influence activation time, current output, and structural stability. In this study, the influence of powder mass loading, water inlet size, and sensor elevation on activation time and electrical output was systematically investigated. Among the different mass loadings tested, the 400 mg configuration consistently demonstrated superior performance, achieving both shorter activation times and higher current output compared to other loadings. This optimal behaviour is attributed to a favourable balance in packing density, which improves conductivity and current generation without impeding water penetration. Design parameters such as inlet size and sensor elevation were also found to significantly affect wetting dynamics and activation timing. Further validation in natural water conditions confirmed the robustness of the 400 mg configuration, showing consistent BLE activation across 25 test samples. Mechanical drop tests revealed that lower mass loadings (e.g., 300 mg) resulted in pellet instability and performance degradation, while 400 mg maintained structural integrity. Overall, the results highlight 400 mg mass loading in combination with optimized structural design as the most effective configuration for reliable BLE activation. These findings provide critical insights for advancing battery-free water leak sensors toward real-world applications in leak monitoring and water damage prevention.
Control and Characterization of the Central Spin System
(University of Waterloo, 2025-11-18) Chen, Jiahui
Precise, coherent, robust quantum control and characterization of quantum systems play important roles in the development of applications of quantum technologies. In particular, advancing the quality of control requires precise characterization, which, in turn, depends on the quality of control. In the first part of the thesis, we introduce a general framework for designing efficient, precise, and robust quantum control strategies using effective Hamiltonian engineering. The methods enable designs that are robust to systematic control errors and variations in the Hamiltonian. The efficiency benefit of achieving control at zeroth order in the Magnus expansion is highlighted. Design tools, such as methods that identify the space of achievable effective Hamiltonians at each order from the Magnus expansion, are introduced. Objective functions for engineering arbitrary effective Hamiltonians are provided and can be used by numerical optimizers for control sequence design. The second part of the thesis explores the characterization of general noise models based on experiments on a central spin system. The noise is probed through stimulated echo experiments, multi-dimensional correlation spectroscopy, and multi-quantum experiments to characterize system/environment correlation and environmental memory effects. Combined with Bayesian inference, these experiments provide quantitative measures of correlation growth, environmental mixing, and deviations from stochastic noise models. Measures that influence the choice of control schemes include non-Gaussianity, non-stationarity, and non-Markovianity. The multi-quantum experiments can also reveal an extended environment and show how the environmental mixing propagates quantum information throughout the environment.

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