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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Polyelectrolyte templated synthesis and formation behavior of high entropy alloys
(University of Waterloo, 2025-12-18) Li, Alexander
High entropy alloys have recently received significant attention in electrocatalysis because their unique compositional complexity can enhance both catalytic activity and long term stability. Despite this promise, there remains a lack of scalable synthesis methods that can produce nanoscale high entropy alloys with controlled and more complex morphologies. One promising strategy is to leverage the electrical double layer that forms when polyelectrolytes interact with metal salts. Polyelectrolytes can serve as effective templates by creating a locally high ion concentration along their surface, which promotes initial mixing during nanoparticle nucleation. In particular, polystyrene sulfonate can also bridge nucleating particles, allowing for the formation of more intricate, networked morphologies. The goal of this work is to investigate how polyelectrolyte concentration, polymer chain length, and different reducing agents influence the resulting catalyst composition and morphology. In addition, this study aims to provide insight into the mechanisms of nanoscale high entropy alloy formation.
Robust and Hierarchy-Aware Classification
(University of Waterloo, 2025-12-18) Pellegrino, Nicholas
The BIOSCAN project, led by the International Barcode of Life (iBOL) Consortium, is an international, multi-year, and multidisciplinary effort seeking to catalogue all multicellular life on Earth by 2045 to enable the global-scale study of changes in biodiversity, species interactions, and species dynamics. Access to this information has the potential to inform strategies to mitigate the damaging ecological effects of climate change. In the near term, the goal is to catalogue all insects. Each sample is imaged, genetically barcoded, and taxonomically classified by domain experts, a time- and resource-intensive process that is becoming increasingly impractical as collection rates surpass five million samples annually. Addressing such needs is among the foundational motivations for the research of this thesis. This thesis presents several contributions motivated by the challenges of the BIOSCAN project. Over five million insect samples were organized into a machine-learning-ready dataset, and a deep neural network classifier was developed to establish a baseline for image-to-taxonomy classification performance. To mitigate the harmful impacts of mislabelled samples in training data, a study of neural network architecture robustness was conducted alongside the development of two novel loss functions: Blurry and Piecewise-zero loss. Blurry loss de-weights and reverses the gradient of samples likely to be mislabelled, while Piecewise-zero loss disregards these samples. These improvements strengthen model robustness and enhance label error detection, enabling the referral of suspicious samples for expert review and correction. Additional work investigates the hierarchical structure of biological data and its integration into classification models, specifically through Hyperbolic neural networks, and measures the benefits of doing so in comparison to using conventional architectures. Finally, this thesis explores aligning image, genetic, and taxonomic representations in a hierarchy-aware manner to improve retrieval across modalities. The contributions of this thesis advance the application of machine learning to facilitate the ongoing global-scale cataloguing of insect life. As challenges such as label errors, hierarchical structures in data, and incomplete annotations are present across many domains, the contributions are valuable to both the machine learning community and the global network of BIOSCAN collaborators.
Predicting ACL Injuries Using Machine Learning Models and Tibial Anatomical Predictors
(University of Waterloo, 2025-12-18) Cheng-Hao, Kao
The tibial slope and the tibial depth are well-established risk factors for Anterior Cru- ciate Ligament (ACL) injury. As ML continues to progress, it has become an increasingly reliable tool for clinical screening and risk factor analysis. This thesis aims to develop and validate an explainable prognostic ML model to predict ACL injury outcomes from these Tibial Anatomical Feature (TAF), and identify the most predictive features among these parameters. A dataset comprising Coronal Tibial Slope (CTS), Medial Tibial Slope (MTS), Lat- eral Tibial Slope (LTS), Medial Tibial Depth (MTD), and sex was constructed using MRI scans taken from 104 subjects (44 males: 22 injured, 22 uninjured; 60 females: 27 in- jured, 33 uninjured). Two distinct ML pipelines were developed: a self-developed pipeline (including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), XGBoost, CATBoost, Multi-Layer Perceptron (MLP), and TabNet) and an advanced AutoGluon pipeline (including XGBoost, LightGBM, CatBoost, TabPFN, TabM, TabICL, MITRA, and their weighted ensembles). Both were designed as end-to-end pipelines to pro- cess the dataset and output predictions with integrated feature importance explanations. Empirically, the AutoGluon Pipeline demonstrated superior performance and training-time efficiency. The recommended F2-tuned standard ensemble achieved an F2-score of 0.736 on the validation set. On the test set, it demonstrated a test balanced accuracy of 0.955, F1-score of 0.952, F2-score of 0.980, ROC AUC of 1.000, precision of 0.909, and recall of 1.000. A full-dataset model, the F2-tuned full-dataset ensemble refitted on the entire dataset for clinical deployment achieved a validation F2-score of 0.813. The global feature importance analyses performed via SHapley Additive exPlanations (SHAP), established the descending order of influences as MTD, LTS, MTS, CTS, and sex. In summary, the study recommends two versions of the F2-tuned prognostic models, one being a standard ensemble model and the other a full-dataset ensemble. The former, which demonstrated moderately high predictive power, was designed for subsequent research comparison. The latter, without access to the original held-out test set, is constructed for maximum robustness and generalization in real-life clinical deployment. Global feature importance analyses elucidated from the standard ensemble decreased MTD along with increased LTS and MTS as most contributive features for ACL injury. These models serve as both feature attribution tools as well as clinical screening tools. These models are intended to be integrated into clinical practice as explainable machines to assist clinicians in predicting the likelihood of ACL injury.
Identifying the Relative Contribution of Motoric and Cognitive Engagement on Spatial Memory
(University of Waterloo, 2025-12-18) Sivashankar, Yadurshana
I investigated the cognitive mechanisms underlying spatial memory, with the aim of differentiating the contributions of motor engagement and decision-making. In Experiment 1, I examined whether volitional motor control or decision-making, during initial exploration of a map within virtual reality, better supported retention of routes travelled. Participants explored virtual cities under three navigation conditions that varied in terms of motor and decision-making demands. During Active navigation, participants had volitional control over their movement using hand-held controllers, allowing head and body rotation in a swivel chair, and made independent decisions about which route to take to reach a target location. During Guided navigation, participants still controlled their movement, but followed a visually guided path overlaid onto the road, eliminating the need for decisionmaking. In the Passive condition participants observed a pre-defined route without having to make any decisions or engage motorically. Following exploration of each environment, participants were asked to “re-trace their steps” using the exact route they had just traveled, from the same starting point. Route memory was significantly better following Active and Guided encoding relative to Passive, suggesting that volitional movement during navigation underlay the benefit. Notably, the complexity of the path chosen by participant at encoding did not predict accuracy of route memory. Experiment 2 assessed the necessity of motor engagement and decision-making by comparing memory benefits following two types of VR implementation: Desktop-VR, in which movement was limited to keyboard input (lower motor engagement), and Headset-VR, in which participants navigated using a steering wheel (higher motor engagement). An effect of navigation strategy emerged only in the Headset-VR group: Active and Guided navigation at encoding led to significantly better route memory relative to Passive. No significant differences emerged between Active and Guided trials, suggesting that motoric engagement, rather than decision-making, is the driver of memory performance. Interestingly, in Headset-VR, a stronger personal preference for Active exploration predicted better route memory, whereas in Desktop-VR, personal motivation predicted route memory accuracy. However, neither motivation nor preference mediated performance, indicating that these factors did not account for the effect of navigation condition on memory. If motor engagement contributes to the formation of route memories, as suggested by experiments 1 and 2, then reduced mobility in older adults may influence performance, and the components underlying it. At the same time, reliance on landmark memory to guide memory may be heightened, as landmarks provide salient external cues that could compensate for reduced motor-based encoding. To test these predictions, in Experiment 3 I examined route and landmark memory in younger and older adults as they explored virtual environments. In younger adults, both Active and Guided navigation equally enhanced memory for routes compared to Passive, replicating experiments 1 and 2. However, in older adults only Active navigation, which engaged both movement and decision-making, resulted in improved route memory. Further, landmark memory in older adults benefitted the most from Active relative to Passive and Guided navigation. Simply put, active encoding eliminated age-related deficits in route memory, suggesting that decision-making (present only in this condition) during navigation may be particularly important for supporting spatial memory in aging populations. This benefit may reflect increased recruitment of frontal lobe-based resources during active navigation, which can compensate for reductions in motor engagement. There were some differences in motivation and preference ratings across conditions in both age groups. However, these subjective measures did not emerge as significant predictors of memory performance. Overall, my findings suggest that motor engagement plays a more critical role than decision-making in enhancing subsequent route memory in younger adults, whereas conditions that require decision-making benefit memory in older adults. These findings have important implications for the design of navigational tools and cognitive interventions aimed at promoting spatial independence, particularly among older adults.
Mindful Streets: Examining the politics and practices of everyday mobility negotiated by those who are neurodivergent and the potential for more inclusive (and just) street design for ‘all’
(University of Waterloo, 2025-12-18) Leger, Samantha
Accommodating ‘all abilities’ in the planning and design of streets and transport-spaces has been a longstanding, yet unmet, objective in transportation planning. Although the discipline has been striving to become more inclusive with efforts to plan for ‘complete streets’ that accommodate diverse mode-users of ‘all ages & abilities’, the ways in which different abilities are planned for often remain limited. This is evidenced in cursory and unnuanced considerations that do not engage with the relational lived experiences of getting around whilst dis/abled or differently abled (Stafford et al., 2022). As such, despite objectives to accommodate ‘all’ abilities in complete streets planning paradigms, differential mobilities produced by being differently abled can remain under-considered. This is especially true for people who experience cognitive difference or who are neurodivergent, whose mobility needs are too often not well-articulated and are entirely or nearly absent from inclusivity considerations. The misalignment between the promises and outcomes of planning for complete streets inclusivity promises is representative of a broader tension in the planning and design of transportation, wherein inclusive-transportation paradigms are constrained within wider politics of automobility and rationalist legacies within the transportation planning discipline. In this, for what (and whom) the street functions is contested within an inertia of autocentric values, ideologies, and ways-of-planning that have proven difficult to unsettle. However, the resultant status-quo of flattened and ‘one-size-fits-all’ approaches to accommodating differential abilities cannot be upheld. As of 2022, 8 million people across Canada live with some form of dis/ability. Particularly, many of those people fall within the umbrella of being ‘neurodivergent’, including in developmental, learning, and mental-health dis/abilities. Notably, those with development dis/abilities (including Autism Spectrum Condition) were the most likely to report unmet accommodations needs (70%). Moreover, learning dis/abilities, such as Attention Deficit and Hyperactivity Disorder (ADHD), were the second-most emergent form of dis/ability for youth (after only mental-health dis/abilities) (Statistics Canada, 2024a). In this, there is an evident need for transportation planning to challenge the current ways in which neurodivergence is included within the efforts to plan for ‘all’ abilities; but the question then becomes, how? Utilizing a framework informed by both the neurodiversity paradigm, that reframes neurodivergence not as a deficit but rather part of the full-spectrum of cognitive ability and Mimi Sheller’s Mobility Justice, that situates transportation planning within the politics and production of mobilities across the macro, mesa, and micro-political, the enclosed thesis responds to this gap in a three-fold approach which 1) explores how politics of automobility continue to shape emergent transportation-planning paradigms, 2) engages with the lived-experiences of getting around in everyday travel for people who are neurodivergent (those who identify as either Autistic or with ADHD) and 3) interrogates how such experiences can inform more-inclusive efforts to plan for ‘all abilities’ in transportation. To respond to the objectives, a qualitative research approach was designed based in both critical analysis of complete street planning documents (n=5) and sit-down (n=30) and go-along (n=14) interviews with people who are neurodivergent on their experiences of being-in-travel and navigating everyday-travel spaces. The findings were discussed in three manuscripts enclosed within this thesis. Particularly, the first objective is addressed in manuscript #1, which examines the current politics of complete streets through a critical discourse analysis of current complete street design guidelines sourced from across Ontario. This review allowed for a better understanding of how complete street planning paradigms remain embedded within politics of automobility, and the resistant potential of complete streets to emulate vélomobility. This thus provided insight into how transportation planning both influences and is influenced from the macro-political (or from “above”). Of note, the first manuscript provides the basis in which the second and third manuscript were informed, noting that complete street planning paradigms are not untethered from broader societal power-structures which can then construct (or constrain) inclusivity-potential. In the second manuscript, the focus shifted to then unpacking how mobility was then produced in micro-political mobility practices (or from “below”), tracing the differential mobilities produced by people who are neurodivergent. Particularly, this manuscript interrogated the lack of research that engages with dis/abled mobilities, and the relational and lived experiences of navigating and negotiating everyday travel. Based on 30 sit-down interviews with people who identify as Autistic or with ADHD, this analysis traced the emotional influences of mobility and how focus, habit, navigation, and sensory sensitivities constructed everyday mobility practices. Further, the mobile geographies of neurodiversity were then scaffolded; examining the adaptive tactics (relating to negotiating predictability and agency/interest) that emerged and could then subvert expectations for how mobility practice ‘ought’ to function. Finally, the third manuscript examined the experiences of people who are neurodivergent in journeying everyday-transport spaces, and the ways in which differential ways-of-knowing can construct capabilities on the street. Guided by go-along interviews conducted in Waterloo, ON, this manuscript identifies specific transport-spaces that can be overwhelming or disorienting for people who are neurodivergent (including transit stations/stops, shared-spaces, and intersections). From this, recommendations were made for rescoping efforts to plan for ‘all abilities’ which consider neurodivergence and can have the potential to more-inclusively engage with the many ways in which ability is relationally and pluralistically constructed. Overall, this thesis provides transportation planning scholars and practitioners with an alternative framework for confronting the complex politics that then construct for whom streets and everyday-transport spaces function; interrogating how to more meaningfully- and mindfully- plan for ‘all abilities’ on the street and in transport spaces. Aptly, throughout this research, transportation planning, particularly complete street planning paradigms, are (re)situated within the production of mobilities as a means to close-the-gap between current complete street initiatives, and their mobility-justice potential (Sheller, 2018).
Health System Resilience for Climate Change Adaptation: An Empirical Evaluation of Access and Utilization in Western Province, Zambia
(University of Waterloo, 2025-12-18) Chiarot, Cameron B.
Background Achieving Universal Health Coverage (UHC) in low- and lower-middle-income countries (LMICs) is jeopardized by the convergence of climate-related shocks and chronic health systems stressors. In Western Province (WP), Zambia, a vast, rural, and remote area characterized by the Barotse Floodplain, progress toward UHC is hindered by the interplay between seasonal flooding variations and pre-existing challenges such as low health facility density and geographic barriers to accessing and utilizing essential primary health care services. A significant gap exists in the empirical evidence necessary to quantify these adverse synergistic interactions, improve routine surveillance systems that currently lack reliable population denominators, and develop dynamic models to assess the impact of shocks and stressors on health service utilization. Objectives This dissertation develops and applies a comprehensive methodological framework to empirically evaluate access to and utilization of essential health services in WP, Zambia. It endeavors to (1) define and measure the dimensions of access, encompassing supply- and demand-side conditions within the context of spatial and temporal parameters; (2) establish an innovative methodology for generating population denominators to enhance disease surveillance and epidemiological metrics; (3) quantify the synergistic impacts of environmental and systemic challenges on service utilization through a novel econosyndemic framework; and (4) model health system resilience dynamically by forecasting utilization patterns and evaluating the effects of various shocks and stressors over time. Furthermore, this dissertation concludes with a policy brief, representing a preliminary policy assessment (Phase I) that employs a location-allocation model to optimize the current health facility network for geographical efficiency, thereby identifying existing access gaps and redundancies within the system. This initial optimization serves as a foundation for a proposed multi-stage framework designed to generate actionable investment strategies by integrating health system capacity, cost considerations, and evolving population needs into future analyses (Phase II). Ultimately, this work offers an integrated, evidence-based framework aimed at strengthening health system resilience as a vital climate change adaptation strategy, thereby advancing the overarching objective of ensuring equitable access to healthcare for vulnerable populations. Methods This dissertation, grounded in comprehensive research, comprises four empirical studies in addition to a policy analysis. Study no. 1 employed a cross-sectional design utilizing geospatial analysis of 220 health facilities (centres and posts) to assess access through metrics such as facility density, population growth, travel durations, and personnel distribution. Study no. 2 introduced and applied an innovative Spatially Defined Catchment Area and Population Under Rooftop (SCSO-PUR) methodology, leveraging satellite data to establish denominators for 321 health facilities, as exemplified in a malaria surveillance epidemiological case study spanning 2017 to 2024. Study no. 3 presented and quantified the econosyndemic framework within an ecological longitudinal study of 62 health centres and posts from 2017 to 2023, employing beta regression and structural equation models (SEM) to analyze the interaction between flood exposure and health system capacity concerning maternal and child health, as well as overall general utilization (i.e., outpatient visits). Study no. 4 utilized time-series forecasting and an Interrupted Time Series (ITS) analysis on the same dataset to measure the dynamic effects of three distinct shocks—the 2019 drought, the 2021 COVID-19 pandemic, and the 2023 complex flood event —on overall utilization, namely outpatient visits. The Policy Brief, Preliminary Assessment (Phase I), employed a Set Covering Problem (SCP) model on the network of 321 facilities to optimize geographic coverage and identify system-wide efficiencies. Findings Geographic access constitutes a primary barrier, with projected declines in facilities per capita and estimated mean travel times ranging from 6.6 to 13.9 hours. A substantial proportion of the population (26.4%, exceeding 322,000 individuals) resides beyond the World Health Organization's recommended two-hour travel time to a comprehensive Health Centre. The SCSO-PUR methodology has demonstrated feasibility in establishing standardized denominators, thereby elucidating previously obscured spatial-demographic disparities in malaria risk. The econosyndemic framework has been empirically validated; notably, the interaction between high flood depths and health system stressors was found to significantly disrupt essential services, including antenatal care (ANC) and facility-based births. The Interrupted Time Series (ITS) analysis indicates that various shocks yield distinct and quantifiable impacts on overall utilization patterns, ranging from immediate declines (drought) to paradoxical increases (COVID-19) and gradual recoveries (i.e., complex flood event). Lastly, the health system optimization analysis uncovers significant spatial redundancy; specifically, while comprehensive geographic coverage could be theoretically achieved with only 46 of the 321 existing facilities, this would entail accepting travel times of up to 7.5 hours, thereby underscoring a crucial trade-off between efficiency and equitable access. Conclusion This dissertation provides a comprehensive, empirically grounded framework for understanding and strengthening health system resilience in a climate-vulnerable setting. It demonstrates that the adverse synergistic interaction between environmental shocks and systemic supply- and demand-side stressors creates an econosyndemic that dynamically and inequitably disrupts access to and utilization of essential health services. The novel methodologies developed offer scalable, data-driven tools for ministries of health to transition from reactive to proactive, evidence-based planning. The findings provide a clear policy directive: building health system resilience for climate adaptation requires targeted, context-specific interventions that address underlying vulnerabilities in infrastructure and geographic accessibility.
The Role of ICT MNCs in Climate Adaptation Through Disaster Response: Motivations, Technology, and Climate Security Implications
(University of Waterloo, 2025-12-18) Battikh, Joe Yousr
Climate change is intensifying the frequency and severity of natural disasters with devastating effects, particularly in developing countries where vulnerabilities are amplified, while traditional disaster management and governance systems are increasingly overstretched. These climate-driven crises, which cost billions and displace millions annually, demand urgent adaptation to mitigate their catastrophic impacts on fragile societies. Multinational corporations (MNCs), especially those from the Information and Communications Technology (ICT) sector, are emerging as critical actors in disaster response, leveraging their resources and expertise to support relief and recovery efforts. This dissertation examines the role of ICT MNCs in addressing natural disasters, and explores their interventions, motivations, and potential to mitigate climate security risks by enhancing resilience in vulnerable regions. Through a multi-method approach, including bibliometric analysis, content analysis of sustainability reports, and a qualitative case study, this research reveals the growing involvement of ICT MNCs in disaster response by utilizing their technological capabilities to bridge critical gaps. However, a concerning geographical disparity is identified with declining corporate engagement in developing countries, despite their increased vulnerability. The case study of ICT MNCs' response to the 2024 Cyclone Hidaya floods in Kenya proposes the empirically grounded TEC Response framework (Triggering response–Engagement motivation–Championing technology), illustrating how corporate interventions are triggered by local presence, driven by a complex interplay of corporate social responsibility (CSR), ethical imperatives, and strategic interests, and implemented by leveraging core technological competencies. This dissertation affirms established CSR theory and contributes novel empirical insights to private governance scholarship by providing empirical evidence of the strategic and ethical dimensions of MNC involvement in disaster contexts and by highlighting their voluntary, uneven, and often unaccountable role in disaster governance, including their capacity to mitigate or inadvertently reinforce climate-induced vulnerabilities. The findings offer practical insights for policymakers and MNCs, emphasizing the importance of cross-sector collaboration, technological integration, and long-term resilience-building to enhance equitable and sustainable disaster management and climate adaptation efforts while addressing critical gaps in mitigating climate-induced vulnerabilities in fragile settings.
Examining the influence of attentional distraction and emotional distress on gait behaviour in Functional Gait Disorders
(University of Waterloo, 2025-12-17) Girlea, Alexandra Laura
Functional Gait Disorders (FGD) are a subgroup of Functional Neurological Disorders (FND) that are characterized by the presence of abnormal postures and movements whose appearance and severity fluctuate, or are inconsistent, over time. These movements appear voluntary but are reported to be involuntary by the patient. FGD symptoms may arise from abnormalities in attentional and emotional regulation, and these dysfunctions lead to loss of agency (control) over movement. These movements may mimic those of other neurological disorders, however sensory and motor testing reveals normal function. This preservation of function despite symptom presentation is referred to as symptom incongruency. Due to the inconsistency and incongruency FGD symptoms, as well as the heterogeneity in phenomenology across individuals with FGD, diagnosis proves a challenge. Individuals with FGD often face delays and excessive costs when pursuing an appropriate diagnosis, ultimately prolonging their disability and delaying access to adequate treatment. A majority of previous work has utilized qualitative approaches to phenotype FGD and aid in diagnosis. While these approaches provided key foundational information about FGD, they do not capture all aspects of FGD. Therefore, quantitative methods of FGD may provide more insight into its phenomenology. However, there is a lack of work utilizing quantitative approaches in FGD. Existing work has primarily focused on the influence of attentional distraction on symptom presentation and severity during gait, providing mixed evidence addressing the influence of attention on functional gait. Similar to qualitative descriptions, some quantitative evidence shows an improvement in symptom presentation and severity while completing a secondary task. To date, there have been no studies investigating the effect of emotional distress on gait in FGD, and this area would be important to investigate given that emotional dysregulation is a key component of FGD phenomenology. While anxiety during gait has been manipulated with postural threat paradigms, the threat of shock paradigm has also been shown to elicit anxiety in laboratory settings. However, the threat of shock has not yet been utilized in gait research. Investigating its validity for use in gait may provide more insight into how this paradigm provokes anxiety during walking, and how walking may change during this scenario. As such, the present thesis aims to address several gaps in the literature. The first is that there are few quantitative descriptors of FGD phenomenology, therefore identifying descriptors may complement and augment qualitative observation and subsequent diagnosis. The second is that while the influence of attention has been investigated in FGD, there has been limited work done investigating the influence of emotional distress on gait in this population, despite its self-reported influence on symptom provocation. Understanding or characterizing the influence of emotional distress on gait in this group may provide novel insights for clinical diagnosis. The use of the novel threat of shock paradigm may also provide more insight into the influence of anxiety on walking in FGD. Together, the present study aimed to address these gaps by utilizing the novel threat of shock paradigm to investigate the influence of attentional distraction and emotional distress on gait behavior and symptom severity in individuals with FGD. It was expected that the threat of shock paradigm would elicit anxiety in all participants. Additionally, it was expected that the dual task would lead to normalized gait in the FGD group, and that the threat of shock would yield a worsening of gait in the FGD group. Finally, it was anticipated that greater levels of movement reinvestment in FGD patients would be associated with a greater dual-task effect on gait in this group, meaning that greater reinvesters would show a greater degree of movement normalization when completing a dual task compared to walking in the absence of a dual task. Eleven FGD patients and 17 age- and sex-matched controls completed 11 walking trials that spanned 4 conditions (neutral, dual task, shock, dual task + shock). All participants self-reported their level of anxiety after each trial, and at the end of the study, completed a battery of questionnaires addressing their movement reinvestment and anxious tendencies. The present study confirmed the utility of the threat of shock during gait, as participants reported greater anxiety when the shock was present. Interestingly, the dual task did not lead to gait normalization in FGD patients. Rather, both patients and matched controls showed worsened gait in the presence of the dual task. Additionally, the threat of shock only impacted step length variability in FGD patients, wherein patients showed greater variability compared to controls in both the presence and absence of shock. Finally, the present study revealed insights into the relationship between reinvestment and symptomology in FGD, wherein patients with a greater degree of reinvestment showed improved gait while dual tasking compared to their baseline gait. Taken together, the present study not only illustrates the utility of the threat of shock paradigm in gait assessments, but also shows nuances in the relationship between attentional distraction and FGD symptomology. Findings from the present study set the stage for future use of the threat of shock during gait, as well point to the importance of movement reinvestment in the presentation and potential resolution of symptoms in FGD. Future work should continue to investigate reinvestment in the context of FGD and should take a more nuanced and individualistic approach to better encapsulate the heterogeneity inherent in functional gait disorders. Doing so may provide a more complete picture of these disorders and may improve characterization, diagnosis, and treatment of these patients.
A Neural-network-based Solver for the Three Dimensional Shape of Vesicle Membranes
(University of Waterloo, 2025-12-17) Rohanizadegan, Yousef
A neural-network-based numerical solver is developed for computing three-dimensional (3D) equilibrium shapes of deformable biomembranes, specifically phospholipid vesicles modeled by Helfrich's curvature elasticity theory. The solver represents vesicle morphology using a phase-field formulation, in which a scalar field distinguishes the interior and exterior of the vesicle through a diffuse interface. The phase field is parameterized by a compact feedforward neural network, and the equilibrium shape is obtained by direct minimization of the Helfrich bending energy subject to global surface-area and volume constraints, enforced via Lagrange multipliers. Automatic differentiation is used to evaluate all spatial derivatives, thereby avoiding finite-difference truncation errors and explicit surface discretization. This framework produces both axisymmetric and fully non-axisymmetric vesicle shapes without imposing symmetry assumptions. Canonical free-space branches, namely prolates, oblates, and stomatocytes, are reproduced, and the classical bending-energy–reduced-volume diagram is recovered in close quantitative agreement with established results in the literature. In addition, a phase-field expression for the bilayer area-difference constraint is derived and incorporated into the solver, providing a numerical setting for the computation of non-axisymmetric equilibrium morphologies in free space. A major contribution of this work is a systematic investigation of vesicle morphology under confinement. Vesicles are studied within a range of hard-wall geometries, including cylindrical (tube), slit, spherical, and cubic confinements. By varying confinement size and reduced volume, the solver captures a rich spectrum of deformations, including biaxial squeezed states, bent prolates, squeezed stomatocytes, and cubic and clam-like morphologies. Stability diagrams, bending-energy curves, and phase diagrams are constructed for each confinement, revealing both discontinuous (first-order) and continuous (second-order) shape transitions, as well as hysteresis and metastable branches. These results extend existing confinement studies by providing fully three-dimensional, non-axisymmetric solutions across multiple geometries and different regimes of confinement (free space to weak to strong) within a unified computational framework. Overall, this work establishes a versatile and scalable neural-network-based phase-field approach for vesicle shape modeling. By unifying classical membrane elasticity theory with modern machine-learning optimization, the solver facilitates a structured exploration of equilibrium morphologies, phase transitions, and confinement effects beyond the reach of traditional axisymmetric or surface-discretization methods. The framework provides a foundation for future extensions to more complex membrane models, dynamic processes, and biologically relevant geometries in soft-matter and biophysical systems.
Investigation of Organic ETLs in QLEDs and a Metal-based RGB Patterning Technique for QLED Displays
(University of Waterloo, 2025-12-17) Mobarak, Saad
Colloidal quantum dot light-emitting devices (QLEDs) have attracted significant interest for next-generation emissive display and lighting applications owing to their narrowband emission, tunable bandgaps, and compatibility with solution-based fabrication. Their emissive layers (EMLs), composed of colloidal quantum dots (QDs), exhibit discrete energy states and size-dependent bandgaps, allowing precise spectral tunability and narrow emission linewidths (FWHM < 25 nm). The high photoluminescence quantum yield (PLQY), excellent photochemical stability, and compatibility with low-temperature fabrication processes make them highly suitable for large-area and flexible devices. Collectively, these properties position QLEDs as strong contenders to replace organic-LEDs (OLEDs) in future display technologies, offering improved color saturation, reduced power consumption, and enhanced manufacturing versatility. Despite these advantages, QLEDs still face fundamental challenges related to charge transport and device efficiency. In particular, the use of organic electron transport layers (ETLs) has been limited due to their perceived low electron mobility and inferior performance compared to inorganic metal-oxide ETLs. However, organic ETLs remain attractive for certain device architectures because of their solution processability, tunable energy levels, and compatibility with low-temperature and flexible fabrication. Moreover, organic layers can form smoother, defect-free interfaces with the QD EMLs compared to metal-oxide ETLs, which may introduce interfacial traps or cause damage during deposition. While the low efficiency of QLEDs employing organic ETLs has conventionally been attributed to their poor electron mobility, the findings presented in this thesis reveal that uncontrolled electron leakage from the QD EML to the hole transport layer (HTL) plays a more dominant role. Based on the finding, the design and optimization of multilayer organic ETL architectures with electron-blocking interfaces effectively suppress electron leakage, leading to improved charge balance and enhanced device efficiency. Using this approach, both red and green QLEDs achieve maximum EQEs approaching 10%, representing among the highest reported values for devices employing organic ETLs. Another limitation in QLEDs is their limited amenability to high-resolution patterning of RGB arrays for full-color displays. Conventional techniques, such as inkjet printing or photolithography, often suffer from limited resolution, QD degradation, or complex processing steps that can compromise device performance. This thesis also presents a novel RGB patterning technique based on metal-induced quenching. Thin metal layers are selectively deposited via a shadow mask onto target areas of the QD EMLs, where subsequent metal diffusion into the EML locally suppresses luminescence through non-radiative energy transfer, while unexposed regions retain their intrinsic emission characteristics. Optical and morphological characterization shows that metal-coated QD regions develop increased surface roughness and island-like features, indicating that metal diffusion into the QD layer plays a significant role in facilitating non-radiative quenching. Using this approach, we demonstrate the fabrication of devices containing multiple QLEDs from a single multilayer stack, each producing spectrally pure electroluminescence (EL) without detectable parasitic emission. Additional patterned structure demonstrates controlled microscale emission at the device level, establishing the feasibility of achieving spatial color definition with high precision. These results validate metal-induced quenching as an effective methodology for QLED color patterning and provide insight into metal-QD interactions.
Microfluidics meet predictive modeling: spatiotemporal characterization of antagonism in bacterial communities
(University of Waterloo, 2025-12-17) Ahmadi, Atiyeh
Microbial habitats (e.g. in the mammalian gut, in soils) are strongly spatially heterogeneous: diffusion limits, advection, and porous structure generate micron-scale gradients in nutrients, oxygen, pH, and antimicrobials. As a result, immediate neighbors can experience different exposures over time, so population-level behavior emerges from local interactions rather than averages. To explain community assembly, stability, and responses to perturbations, we therefore need to characterize interactions among bacterial populations at single-cell, spatially resolved scales. Within this landscape, antagonistic interactions (diffusible bacteriocins, contact-dependent inhibition, and competition for space/resources) are major determinants of fitness and composition, but their efficacy depends on cell-tocell variability in production, receptor status, and exposure paths. This thesis bridges single cell based experiments and predictive modeling to make those dynamics measurable and modelable at scale. I first establish a methodological foundation by benchmarking time-lapse image-processing software for bacterial populations, creating ground-truth datasets and mapping performance trade-offs to guide tool selection (Chapter 2). I then introduce TrackRefiner, a post-processing software that identifies and corrects tracking errors in time-lapse images of rod-shaped bacteria, thereby improving lineage fidelity for downstream analyses (Chapter 3). To bridge experiments and models, I survey and systematize machine-learned summary statistics for Bayesian parameter inference in systems biology (Chapter 4). Building on these elements, I present a pipeline that carries data from microfluidic image acquisition to agent-based model calibration (Chapter 5). Chapter 6 was intended to apply this toolkit to single-cell antagonism in bacterial communities, characterizing spatiotemporal interactions and developing a predictive model. Because of time constraints, I focused on building time-lapse microscopy datasets and analyzing them with the methods from Chapters 2 and 3. These analyses also help to understand aspects of the biology of the antagonistic system we study. I implemented a preliminary agent-based model to capture cell growth and toxin diffusion/uptake; calibration and validation are left for future work. Collectively, the thesis delivers (i) validated image processing practices with openly released ground truths for segmentation and tracking, (ii) open-source software that enhances tracking quality, and (iii) a reproducible calibration workflow for agent based models. To the best of my knowledge, (iv) it also presents the preliminary single-cell, spatiotemporal characterization of colicin Ib–mediated antagonism in microfluidic environments. The impact is twofold: experimentalists gain a principled framework to quantify and compare antagonistic strategies at single-cell resolution, and modelers obtain reliable, information-rich statistics for forecasting community dynamics and evaluating interventions. By unifying microfluidics with inference, the work is a step towards data-driven control and design of microbial consortia.
Machine Learning Approaches for Thermoelectric Performance Predictions
(University of Waterloo, 2025-12-17) Barua, Nikhil
The area of thermoelectric (TE) research suffers from an affordable pathway to achieve high performance TE materials. This is because the merit of the experimental approach, although sacrosanct and irrefutable, often, resorts to a trial- and-error method approach. This approach is achieved through training, experience, and observed knowledge. Additionally, while working to find an effective solution through this approach, the target TE material is kept in mind. This introduces biasness in TE materials discovery. In TE research, recent studies have demonstrated the potential for accelerated materials discovery through artificial intelligence (AI) driven methods. Building on these advances, the thesis aims to assist experimental researchers in predicting the properties for high-performance thermoelectric (TE) materials. The objective in the thesis is realized with the developed and tested machine learning (ML) models to predict TE properties. The developed models are based on extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) algorithms. These algorithms, which form part of the ML framework, were trained using curated datasets. The models achieved good predictive accuracy of TE properties. The interpretability of the model predictions through SHapley Additive exPlanations (SHAP), provided interesting and chemically meaningful insights between TE material compositions and the TE properties. In one of our studies, the predictive models were validated experimentally for new doped SnSe systems to observe near consistency between predicted and measured κ values. Subsequently, we developed an end-to-end web application embedded with these ML models, hosted on Git-Hub and Microsoft Azure cloud to deliver rapid TE property predictions. The application is made accessible to TE researchers worldwide. The researchers can upload any set of compositions to the web interface and receive immediate thermoelectric (TE) property predictions. The methodology of the overall ML pipeline explained in the chapters are open for diversification with other Deep Learning (DL) algorithms or Generative AI (Gen AI) models. Furthermore, the ML models can be retrained by modifying the data in the existing dataset, in the direction of improving model accuracy. With this, the models can be used for experimental or first-principle based computational validation. The scope of this research offers more questions than answers leading to an extensive scope of hypothesis generation. Therefore, this opens unlimited opportunities for future investigations.
On Asymmetric Induced Saturation
(University of Waterloo, 2025-12-17) Hajebi, Sahab
Given a graph H, a graph G is H-free if no induced subgraph of G is isomorphic to H. A graph G is H-induced-saturated if G is H-free but deleting or adding any edge in G creates an induced copy of H. The notion of induced saturation originated in a 2012 work by Martin and Smith [23] concerning the extremal properties of H-induced-saturated graphs. On the structural side, a large body of work since then has been devoted to the study of graphs H for which H-induced-saturated graphs do exist in the first place. We say that H is normal if there exists an H-induced-saturated graph. It is immediate that complete graphs (except when on two vertices) are not normal because deleting edges from a graph cannot increase its clique number. Similarly, empty graphs (except when on two vertices) are not normal because adding edges to a graph cannot increase its independence number. Beyond these trivial cases, however, characterizing normal graphs is quite difficult: The four-vertex path is the only other graph currently known not to be normal, and very few graphs are known to be normal. In particular, it remains open whether all even cycles are normal. In this thesis, we study the analogous notions with only one of the two operations – edge deletion or addition – required to create an induced copy of H. Given a graph H, we say that a graph G is H-deletion-saturated if G is H-free, has at least one edge, and deleting any edge in G creates an induced copy of H. We say that H is deletion-normal if such a graph G exists. (The complementary notions of H-addition-saturated and addition-normal are defined similarly.) These “asymmetric” weakenings of induced saturation appear to be more tractable. For example, in contrast to the fact that, as mentioned above, it remains open whether all even cycles are normal, Tennenhouse [28] proved that all even cycles are addition-normal, and with Fan, Sepehr Hajebi, and Spirkl, we proved recently [16] that all even cycles are deletion-normal. We conjecture that every non-complete graph H is deletion-normal (or equivalently, that every non-empty graph H is addition-normal), and provide evidence for our conjecture by proving it for a variety of graphs H, including all complete multipartite graphs with a unique largest part, line graphs of all trees, all triangle-free graphs with exactly one cycle, and all graphs on at most six vertices.
Charting Skills in Uncharted Domains: Evaluating How Video Game Competence is Viewed Outside Competitive Desktop Gaming Environments
(University of Waterloo, 2025-12-16) Senthil Nathan, Kaushall
Player competence heavily shapes multiplayer gameplay experiences, from team success to avoiding frustration, yet existing research focuses predominantly on competitive esports contexts on PC platforms. This lack of research leaves players in understudied domains without a clear understanding of competence. Therefore, I examined the contexts of casual, cooperative games and VR multiplayer games to uncover how competence is conceptualized within them. In study 1, I conducted a mixed-methods experiment with 23 participants playing Overcooked 2 with a competent or incompetent teammate, to examine competence, frustration, and cooperative behaviour. The results of study 1 showed that players evaluated teammate performance comparatively rather than through absolute metrics, and that current frustration and cooperation measures were insufficient in capturing the nuances of player experience. In study 2, I surveyed 111 VR multiplayer gamers to identify novel skill clusters, how skills are adapted from PC to VR, and whether player rank affects the importance of these skills. Findings revealed five new VR-specific skills, highlighted the body’s central role in skill adaptation, and found no significant rank-based rating differences. The overarching contribution is in demonstrating that an evaluation of competence drawn from competitive esports is insufficient in describing competence in these domains. Casual, cooperative players judge competence in primarily in relation to their teammates, while VR multiplayer gamers regard physicality and embodied interaction as essential to displaying relevant skills. My thesis puts forward new definitions of competence in casual, cooperative games and VR multiplayer games, as the first step to chart skills in uncharted domains.
Electrolytes Design for Metal-based Anode Batteries
(University of Waterloo, 2025-12-16) Ma, Qianyi
Aqueous zinc-metal batteries (AZIBs) promise intrinsically safe, low-cost energy storage, yet their practical deployment is constrained by interfacial instabilities—hydrogen evolution, corrosion, and dendritic growth—especially under high current density, high depth-of-discharge (DOD), and sub-zero temperatures. This thesis develops an electrolyte-centric roadmap that couple’s solvation-structure regulation with interphase chemistry to stabilize Zn plating/stripping across harsh operating regimes. The approach integrates three mutually reinforcing pillars: (i) outer-solvation-shell tailoring to direct desolvation and crystal orientation, (ii) interphase engineering with multifunctional additives to build robust, ion-conductive SEIs, and (iii) radical management to arrest chemistry that triggers corrosion and gas evolution. Multiscale evidence from synchrotron probes, in-situ/operando imaging, depth-profiling spectroscopies, and simulation closes the loop between molecular design and device-level durability. First, I introduce an “outer-solvation-shell” strategy using 2-propanol in Zn(OTf)2/H2O that selectively modifies the second solvation environment of Zn²⁺ while preserving the canonical inner shell Zn(H2O)62+. EXAFS/XANES, wide-angle X-ray scattering, NMR, and molecular dynamics consistently indicate water-dominant inner coordination with 2-propanol and OTf- participating in the outer shell. Density-functional theory combined with 2D grazing-incidence XRD reveals preferential adsorption/desolvation pathways on Zn(101)/(002), enabling oriented, compact deposition with lower nucleation barriers. This manifests as markedly extended symmetric-cell lifetimes (≥3000 h at 1 mA cm-2), stable cycling under heavy load (15 mA cm-2 with ~50% DOD), broadened electrochemical stability, suppressed corrosion/HER, and robust low-temperature operation down to −40 °C. Second, I employ N, S-dual-doped graphene quantum dots (GQDs) as a multifunctional electrolyte/interphase regulator. Their heteroatom sites and surface functionalities coordinate within the solvated layer and at the metal interface to reduce interfacial resistance and homogenize nucleation. Electrochemical analyses (EIS, Coulombic efficiency) and multimodal imaging (in-situ optical/TXM, SEM/FIB-SEM) show dense, void-free deposits and smoother morphology evolution. Depth-profiling (XPS, ToF-SIMS) and diffraction (GIXRD) confirm a ZnF2-rich, mechanically resilient SEI that sustains reversibility under high-rate. Third, I identify hydroxyl radicals (•OH) as direct drivers of interfacial degradation and demonstrate that free-radical scavengers (FRS) effectively suppress radical-induced corrosion and gassing. EPR verifies radical quenching; cryo-TEM and computed laminography visualize mitigated porous by-product layers and reduced “dead-Zn”; line-scan micro-GIXRD tracks crystallographic evolution during plating/stripping. When integrated, the three pillars deliver coin cells with high-rate, long-life operation; Zn∥Zn symmetric cells sustaining up to ~1700+ h at ~45–51% DOD; high-areal-capacity cycling (≥5 mAh cm-2 at 2 C for extended cycles); and 17Ah-class Zn∥V2O5 pouch cells. The chemistry is compatible with scalable manufacturing, including dry-electrode processing using Zn powder anodes. Methodologically, the thesis leverages synchrotron metrologies (VESPERS-GIXRD, HXMA-XAFS, BMIT laminography/TXM), interfacial mechanics (electrochemical-AFM force spectroscopy), and depth-profiling (XPS, ToF-SIMS), complemented by MD/DFT, to establish causality from solvated Zn2+ structure through desolvation kinetics and interfacial reactions to macro-scale durability. Collectively, the results constitute a generalizable design playbook—outer-shell tailoring, interphase engineering, and radical management—that advances fast-charging, low-temperature, and high-DOD AZIBs toward practical, safe, and scalable energy storage.
Parallel Oblivious Joins using Radix Partitioning
(University of Waterloo, 2025-12-16) Ahmed, Nafis
We present parallel doubly oblivious algorithms for both non-foreign key and foreign key joins using an oblivious radix partitioning technique. Oblivious query processing enables secure execution over encrypted data when organizations outsource data to the cloud. When the cloud server processes encrypted data within hardware enclaves, the data is vulnerable to side-channel leaks caused by data-dependent memory access patterns and control flow. Our algorithms efficiently defend against these vulnerabilities by combining data partitioning with parallel execution. Specifically, we propose a doubly oblivious radix partitioning approach that divides input arrays into disjoint partitions without leaking the multiplicity of individual elements, unlike vanilla radix partitioning. This is especially important for join operations, where duplicate keys are common. To construct our join algorithm, we apply oblivious radix partitioning independently to each input table, allowing the algorithm to compare tuples only within corresponding partitions. When input tables are presorted, our oblivious join algorithm is the first to avoid combining and obliviously resorting them, yielding performance improvements over the state-of-the-art scheme, Obliviator. Beyond joins, our oblivious radix partitioning technique is a standalone primitive with applications to a broad class of problems, including oblivious aggregation and private set intersection.
Bridging Models and Mechanisms: Integrating Proteome Remodeling with Antibiotic Response
(University of Waterloo, 2025-12-16) Howell, Brittany
Antibiotic resistance is an urgent challenge in medicine, and treatment outcomes are often moulded not only by genetic resistance but by the physiological adaptation of bacteria under drug exposure. Comprehending these constraints requires integrating how translational capacity, nutrient supply, and global feedback cooperatively determine recovery and survival. This thesis extends and validates a mechanistic model that integrates nutrient-dependent growth laws with dynamic proteome allocation to capture Escherichia coli's response to pulse-dose exposure to the ribosome-targeting antibiotic tetracycline in glucose- and glycerol-based media. Experimental measurements of growth delay times, RNA/protein ratios, and inhibition curves supplied direct physiological targets that guided model refinement, making certain that theory remained connected to reproducible lab data. The modeling effort highlighted two crucial effects that had previously been overlooked. First, following the removal of a ribosome-targeting antibiotic, ribosomes no longer constitute the primary limiter of growth, unlike their role under most steady-state conditions. Second, a proportional feedback controller based on the non-steady-state mismatch in amino acid flux was necessary to capture the rapid timescales of antibiotic adaptation and post-pulse recovery. The resulting model reconciled all experimental datasets across both carbon sources, reproducing delay time plateaus, RNA/protein recovery dynamics, and inhibition profiles in a physiologically interpretable way. Sensitivity and Hessian analyses showed that recovery dynamics are primarily governed by transport rates and the strength of feedback control, whereas shifts in how binding and transport interact have little influence on the resulting physiological behavior predicted by the model. This contrast showcases which regulatory components are necessary for shaping recovery and which play only a minor, compensatory role. Clinically, the model argues against prolonged dosing, which permits rapid recovery during treatment, and instead supports pulses that maximize growth inhibition for a fixed total amount of antibiotic. Such regimes minimize the time bacteria spend in sub-inhibitory drug concentrations, thus limiting the opportunity for resistant variants to emerge, and provide a quantitative rationale for pulse- and intermittent-dosing strategies that exploit the post-antibiotic effect. More broadly, this work exemplifies how combining experiments with physiologically grounded modeling can illustrate unifying supply–demand principles of bacterial adaptation. Although developed for E. coli and tetracycline, the mathematical modeling framework is readily adaptable to other reversibly binding ribosome-targeting antibiotics, and could possibly be extended to other antibiotic classes, offering a foundation for linking physiology to treatment strategies in diverse microbial environments.
Algebraic geometric methods for algorithms in satisfiability, irreducibility of varieties, and identity testing
(University of Waterloo, 2025-12-16) Garg, Abhibhav
In this thesis we study three problems that lie in the intersection of abstract algebra and theoretical computer science. The first of these is the polynomial identity testing problem, which is the task of determining if an algebraic circuit computes the identically zero polynomial. We give the first polynomial time deterministic algorithm for the special case of depth four algebraic circuits, with top fan-in three, and constant bottom fan-in. We also give the first such algorithm for circuits with bottom fan-in two, and constant top fan-in. Our methods involve studying higher degree generalisations of classical incidence configurations known as Sylvester–Gallai configurations. The second of these is the problem of checking if a system of equations is satisfiable. In the regime when the number of variables in the system is a constant, we show that satisfiability can be checked in constant depth by algebraic circuits. In particular, we show that the multivariate resultant has a constant depth circuit in this regime, independent of the degrees. The previous best known constructions of the resultant required depth that was logarithmic in the degrees. The final problem we consider is the problem of deciding if an ideal theoretically defined variety is irreducible in characteristic 0. We show that this task can be solved in the polynomial hierarchy assuming the generalized Riemann hypothesis. This improves the previous best known bound of PSPACE.
Towards Safe Initialization of Scala Global Objects
(University of Waterloo, 2025-12-16) Xing, Enze
This thesis focuses on safe initialization of global objects in Scala. Global objects encapsulate global information in Scala, and their initialization is susceptible to causing run-time errors. Moreover, global objects are initialized by demand (i.e. on their first access). The initialization safety of a global object is brittle if it depends on the initialization point of the object, because the initialization point is the first access in the entire program. This motivates the idea of automatically detecting potential initialization errors during compilation. The main contribution of this thesis is designing and implementing a global object initialization checker in the Scala compiler. Theoretically, we identified run-time errors caused by unsafe initialization patterns of global objects and organized three static principles to enforce on Scala programs: Prohibiting accesses to uninitialized fields, which prevents null pointer exceptions; partial ordering of global object initialization order, which prevents deadlocks between locks that guard the initialization of global objects; and initialization-time irrelevance, which ensures that initialization safety of the global object is independent of the initialization point. We then designed the global object initialization checker by proposing the formal initialization semantics of a Scala initialization calculus, and the initialization checker is presented as an abstract interpreter of the initialization calculus. The initialization checker also checks the initialization process of each global object individually rather than conducting a whole-program analysis. Practically, we have integrated the abstract interpreter into the Scala compiler after extending the initialization semantics with more Scala features. The initialization checker can be turned on when compiling Scala programs, and we evaluated the initialization checker during the compilation of many widely-used open-source Scala projects which form a test suite. The initialization checker reports warnings in several projects that are verified to be true positives. The result highlights the necessity of checking the initialization safety of Scala projects and the utility of the global object initialization checker in this thesis.
Integrating Cognitive Work Analysis into an ACT-R Model for Cybersecurity Applications
(University of Waterloo, 2025-12-16) He, Fan
Cybersecurity is a trending concern with the rapid development of many systems. While humans are often considered vulnerable targets, research on human factors remains limited compared to the extensive technical focus on defense and mitigation strategies. Human-focused cognitive research in this domain faces two primary challenges: the evolving and complex nature of the cybersecurity landscape, and the domain-specific characteristics of the systems under attack. These challenges point to the need for modeling human performance in identifying vulnerabilities, with both precise dynamic measurement and domain-specific fidelity. Accordingly, we proposed a solution by integrating CWA into ACT-R models. A detailed elaboration on the CWA and ACT-R's structural compatibility across dimensions, their fundamental strengths as complements, and the functional competencies with integration was presented. This conceptual exploration demonstrated the feasibility of integrating the CWA and ACT-R, leading to improvements in model construction efficiency and domain-specific validity. We explored CWA and ACT-R for modeling humans in vehicle cybersecurity. While we were able to demonstrate a model, a follow-up study with human participants showed that drivers may not actively identify vulnerabilities and mitigate cyber threats. We then practically implemented and applied the integrated model, from model construction preparation to detailed rule development, guided by CWA’s Work Domain Analysis, Control Task Analysis, and Strategies Analysis, to simulate the SOC analysts' cybersecurity alert triage performance. The model construction process demonstrated better efficiency with a systematic approach, and the resulting model showed improvement trend in quantitative accuracy, domain-specific validity, and the interpretability of human adaptability and flexibility. However, the model is limited in capturing human exploratory behavior, prompting a brief test of using Generative AI (GAI) models to address this gap. This thesis is the first exploration and implementation of integrating CWA-guided domain-specific analysis with ACT-R’s computational capabilities to develop an integrated cognitive model for humans in complex work domains. The effort advances the development of cognitive modeling by providing theoretical grounding and practical insights for applying and extending cognitive models. Finally, we discuss whether GAI models might enhance cognitive modeling, as GAI capabilities become more available.

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