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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A Framework for Human-Robot Interaction in Industrial Environments
(University of Waterloo, 2026-05-21) Dubay, Shaundell
With the advantages of human-centric industrial environments, developing new technologies for human-robot interaction (HRI) has become an important research frontier for advancing the capabilities of industrial facilities. However, based on the literature, there is a lack of safe and reliable industrial collaborative robots capable of performing independent tasks and working collaboratively with a human. To address this gap, this thesis proposes a novel framework version for HRI suited for industrial environments. The framework integrates a form of perception, decision-making, and control to enable structured and, thereby, safe interaction between humans and robots. In addition, the design is suited for relatively cost-effective equipment commonly found in industrial settings. First, a custom dataset is created to represent established categories of interaction modes, tasks, and actions relevant to industrial settings. Gesture recognition is achieved through human pose estimation and the formulation of distinguishing geometric relationships between skeletal keypoints, combined with image classification of a physical identifier to associate the robot with the intended human collaborator. Together, the perception components enable a lightweight and effective method of human-robot communication and task identification. Based on the dataset size, the evaluation of the gesture recognition strategy shows relatively good generalization to unseen participants. Then a gesture-based switching finite state machine (FSM) is designed to enable structured human–robot communication. Together with the perception components, clear decision-making can occur to enable a safe protocol-driven HRI. Second, a path following control strategy suited for human-robot collaboration (HRC) is designed in the robot control. Implementing impedance control realizes a compliant robot behaviour for human-robot collaborative tasks, where the human, with expert contextual perception and cognitive on-demand adaptation, can guide robot motion. By mapping the robot coordinates into tangential, transversal, and orientation states relative to a nominal path, the approach potentially allows direction-specific motion objectives unique to the task. Finally, the gesture recognition, FSM decision logic, and path following robot control are integrated into an actionable framework. The resulting system enables clear protocols to facilitate safe and effective HRI. Experimental validation demonstrates successful HRI, human-robot collaborative tasks, and highlights the potential tailoring of the control to different collaborative task conditions.
Investigating LLM’s Knowledge about English G2P Rules and Pronunciation with Pseudo-words
(University of Waterloo, 2026-05-21) Yao, Sheng
For large language models (LLM), the real world is mapped onto a world made of text strings, and one current research direction in NLP is to examine how much knowledge LLMs learn inside their text world. Studies have shown that they have knowledge of English grapheme-to-phoneme conversion (G2P) and pronunciation, but only to a moderate degree. They mainly use tasks such as rhyme detection, syllable counting, and G2P involving words inside the vocabulary of a language - in most cases English. While the results are convincing, we still believe that the acid test of such knowledge should involve pseudo-words - made-up orthographic words. For example, state-of-the-art LLMs such as GPT5 and Gemini3-Pro have no problem providing the pronunciation of an in-vocabulary English word as they are clever enough to fetch the fact from their training data. When given a pseudo-word, however, their predictions can sound unnatural from a human perspective. On the other hand, if human participants all agree on a certain pronunciation for a given pseudo-word, it means they have used some common (implicit) knowledge about G2P and pronunciation when making their prediction. Therefore, we aim to examine the degree of similarity between human participants and LLMs when they are predicting the sound of pseudo-words as an indicator of whether LLMs have learned about G2P and pronunciation in their text world. It turns out that LLMs’ knowledge does have a remarkable degree of human-likeness, not only because 80% of LLMs’ predictions are the same as humans’ when there is zero inter-human divergence, but also because LLMs’ bewilderment (measured by how LLMs’ predictions vary across runs) correlates with humans’. That is, models and humans are dealing with the G2P task in more or less the same way. However, we also see substantial numbers of cases where LLMs’ predictions are far from humans’ predictions. When we took a closer look at such cases, we found a couple of tendencies and further validated the findings using real English words. More than half of these tendencies suggest that LLMs actually oversimplify the matter of G2P, sticking to the most common mappings in the English vocabulary. These tendencies can be useful when we try to improve the performance of LLMs and even text-to-speech models on pronunciation tasks. In fact, we also included the text-to-speech component of SpeechT5, in order to compare the performance of text-only models and bi-modal ones. We find that, while there seems to be a bottleneck for LLMs in the sense that the most powerful model, GPT5.4, does not significantly outperform a much weaker Llama3 on the G2P task, SpeechT5 is easily more human-like than all LLMs on several metrics. It seems that bi-model learning does give text-to-speech models such as SpeechT5 an advantage on a sound-related task, despite the fact that SpeechT5 is much smaller than LLMs.
A Low-Power Wireless Transmitter for a Continuous Glucose Monitoring System-on-Chip
(University of Waterloo, 2026-05-21) Loka, Rino
Continuous glucose monitoring (CGM) can improve diabetes management by providing frequent measurements and revealing trends that are difficult to capture with invasive, intermittent tests. To this end, a miniaturized, wearable sensing platform that operates continuously and delivers timely alerts is beneficial for individuals who require active glucose management. This thesis presents a wireless transmitter, implemented in a complementary metal-oxide-semiconductor (CMOS) integrated circuit technology, for energy-efficient telemetry of biomedical signals from a wearable device that transmits short packets to a smartphone, enabling practical daily readout. Since the transmitter must meet Bluetooth Low Energy (BLE) constraints under a tight energy budget, several circuit and system-level design choices that balance frequency accuracy, spectral compliance, and robustness to process-voltage-temperature (PVT) variation at low supply voltages must be made. The proposed wireless transmitter is one component of a larger mixed-signal system-on-chip (SoC), which integrates a commercially-available electrochemical sensor that interacts with glucose and/or ketones, to enable continuous concentration measurement. The sensing front-end electronics of the SoC converts chemical activity into an electrical signal that is conditioned and digitized on-chip. This measured concentration is then encoded for BLE-compatible transmission by modulating the digitized data using Gaussian frequency-shift keying (GFSK) in the 2.4 GHz industrial, scientific, and medical (ISM) band, implemented as an integer-N charge-pump phase-locked loop (CP-PLL) with direct voltage-controlled oscillator (VCO) modulation. Simulated and experimental results from a fabricated prototype chip demonstrate the feasibility of the proposed approach in terms of sensor readout, burst energy, and BLE spectral compliance. Implemented in a 0.18 µm bulk CMOS process, the fabricated transmitter delivers -7.06 dBm at 13.5 mW DC power, with an intra-packet drift of 41.56 kHz and reference spurs of −26.4 dBm at ±2 MHz offsets — both within BLE LE 1M limits. End-to-end validation confirms the successful reception of BLE advertising packets on a commodity Nordic nRF5340 receiver, with the digitized electrochemical sensor data reconstructed from over-the-air stream. The presented methodology provides a foundation for compact wearable bio-sensing platforms, combining continuous chemical sensing with standardized wireless communication for patient-facing monitoring and alerts.
Quantum Optics with Nanowire Quantum Dots
(University of Waterloo, 2026-05-19) Pennacchietti, Matteo
Quantum technologies, such as quantum computing and quantum networks, are a rapidly developing new frontier in information technology, promising capabilities exceeding those of classical devices. Single particles of light, known as photons, are an important component for realising these quantum technologies, given that they can carry quantum information, known as qubits, over long distances. Generating single photons is possible using quantum emitters, which act as interfaces between light and matter—linking flying qubits (photons) with stationary qubits (such as electron spins). Semiconductor quantum dots (QDs) are a promising example of such quantum emitters, offering benefits such as high brightness, controllable emission wavelengths including in the telecom bands, and the scalable integration into photonic nanostructures for enhanced light-matter interactions. Among the many available QD devices, nanowire quantum dots (NWQDs) are a particularly attractive platform as they offer deterministic positioning of QDs into tapered photonic waveguides for high brightness and a Gaussian emission profile. In this thesis, we develop these NWQDs as an entangled photon source (EPS) for quantum networks and as a platform for waveguide quantum electrodynamics (wQED). In chapter 5, we focus on using the NWQD as an EPS using the biexciton-exciton cascade under two-photon excitation (TPE). We show that by utilising fast single photon detectors, the NWQD can generate entangled photon pairs with∼98% fidelity. In addition, we demonstrate that the exciton fine structure splitting does not degrade the entanglement generated by the QD if fast single photon detectors are used. We further enhance the performance of the NWQDs in chapter 6 by developing a novel pick-and-place technique to transfer the nanowires onto pre-fabricated templates. We fabricated two NWQD devices using the transfer method: a NWQD on a gold mirror with an estimated single photon extraction efficiency of∼73%; and a NWQD between quadrupolar gates capable of tuning the QD emission wavelength by∼3 GHz. Finally, in chapter 7, we explore the potential of the NWQD as a platform for wQED where there is a strong interaction between the QD and photons in the single waveguide mode. We developed a mode-matching technique to couple ∼95% of a free space laser drive field into the fundamental mode of the nanowire waveguide. With this mode matching technique we performed resonance fluorescence in the Heitler (weak drive) regime, and observed the reflection of single photons from the QD. Taken together, the findings presented in this thesis demonstrate an enhancement of the NWQD platform’s performance and highlight its potential as a complete quantum emitter platform for quantum photonic technology.
In-situ Stress Characterization, Geothermal Energy Exploitation, and Geomechanical Risk Assessment at Mount Meager, BC, Canada
(University of Waterloo, 2026-05-19) Chai, Yutong
Geothermal energy offers a stable and sustainable source of baseload power that can significantly contribute to global decarbonization efforts. The Mount Meager Volcanic Complex (MMVC) in British Columbia is widely recognized as Canada’s most promising high-temperature geothermal resource, with reservoir temperature exceeding 250 °C recorded through previous exploration. However, uncertainties regarding the in-situ stress conditions, fractured reservoir behavior, and potential for induced seismicity continue to limit the development of geothermal energy at this site. This thesis aims to address the knowledge gap by in-situ stress characterization, geothermal systems design, and induced seismicity assessment. The characterization of the in-situ stress field within the Mount Meager region is challenging due to its complex surface topography that standard technique doesn’t account for. In this work, a three-dimensional numerical model based on the Displacement Discontinuity Method (DDM) is developed to address this challenge. The numerical results indicate that topography strongly influence the near-surface in-situ stress field. Based upon this discovered in-situ stress information, geothermal heat extraction potential at Mount Meager is assessed using both a closed-loop geothermal system and an enhanced geothermal system. For the closed-loop system, a coupled thermo-mechanical numerical model is developed to simulate long-term operation of a vertical coaxial borehole heat exchanger installed within the Mount Meager reservoir. The simulation results demonstrate that closed-loop geothermal systems can extract heat from low-permeability basement rocks through conductive heat transfer while producing relatively localized thermoelastic stress perturbations. For the enhanced geothermal system that relies on fluid circulation through fracture networks, a coupled thermo-hydro-mechanical (THM) model is developed to simulate long-term operation of fluid injection and production at Mount Meager. The results show that convective heat transfer through connected fracture networks significantly enhances heat extraction efficiency compared with conduction-dominated closed-loop systems. It is also shown that system performance depends strongly on fracture connectivity, reservoir geometry, and operational parameters. Seismic risk analyses are also conducted for the more promising enhanced geothermal systems. This starts with the investigation of the mechanical behavior of faults subjected to geothermal stress perturbations. Numerical analyses demonstrate that fault orientation, dip angle, and depth strongly influence stress concentrations and displacement along fault planes, highlighting the importance of geological structure in controlling fault stability during geothermal operations. Then a fully coupled THM model incorporating discrete fracture networks is used to assess the seismic risk associated with geothermal energy development. The simulations indicate that both pore pressure increases and thermally induced stress redistribution can promote slip along critically stressed fractures. Sensitivity analyses show that injection pressure, injection temperature, fracture density, and reservoir depth exert significant control on the magnitude and spatial distribution of seismic events. Overall, this thesis provides an integrated understanding of geothermal energy development potential at Mount Meager by linking in-situ stress characterization, geothermal system design, and seismic risk analysis. The results demonstrate that geothermal development at Mount Meager is technically feasible and provide a quantitative framework for evaluating geothermal system design, reservoir stability, and seismic risk in fractured volcanic environments.
Visual Segmentation with Explanations for Medical Decision Making: A CNN Architecture with a Transformer-Attention Extension
(University of Waterloo, 2026-05-19) Khalaf, Mahmoud
Medical image segmentation is essential for assisting medical professionals in locating anomalies in images. However, the lack of explainability in current segmentation frameworks limits clinical trust and adoption. This thesis presents two complementary frameworks for explainable medical image segmentation, demonstrating that accuracy and interpretability can be achieved simultaneously through innovative architectural design. The first framework introduces an attention-based CNN architecture that generates model-specific visual explanations through spatial attention gates integrated directly into the network. The proposed model achieves a Dice score of 0.8621 on the Kvasir-SEG polyp segmentation dataset, outperforming all evaluated explainable models, while producing attention heatmaps that faithfully reflect the model's decision-making process. The second framework advances this by introducing a dual encoder architecture combining a pretrained ResNet-34 CNN encoder with a pretrained Swin Transformer encoder, fused through a learned directional attention-gated mechanism at multiple scales. The dual encoder achieves a Dice score of 0.907 on Kvasir-SEG, outperforming all single encoder baselines, while generating richer multi-scale visual explanations that reflect the complementary contributions of both encoders. Finally, this thesis outlines a pathway in detail towards a fully multimodal explainability system, integrating textual explanations through SigLIP, Retrieval Augmented Generation, and a Large Language Model alongside the visual heatmaps. We explore future directions regarding breast cancer and echocardiogram segmentation, more specifically applications for ejection fraction computation,and insights gained from trials and errors that led to the innovative designs of the thesis.
Studying Conformal Field Theories in Three Dimensions with the Fuzzy Sphere
(University of Waterloo, 2026-05-19) Zhou, Zheng
Conformal field theory (CFT) is one of the central topics of modern physics. CFT has provided important insights into various aspects of theoretical physics. In condensed-matter physics, it has produced useful predictions about the critical phenomena. Many classical and quantum phase transitions are conjectured to have emergent conformal symmetry in the infra-red (IR). In high-energy physics and quantum field theories, CFT also plays important role in understanding string theory, duality with gravitational theories on anti-de Sitter (AdS) space, and renormalisation group flow structure. In 2D, the infinite-dimensional conformal algebra has made many theories exactly solvable. However, going to the higher dimensions, the CFTs are less well-studied due to a much smaller conformal group, although some of the conformal data are determined at high precision by conformal bootstrap. The fuzzy-sphere regularisation has emerged as a new powerful method to study 3D CFTs. This method involves studying quantum systems on a sphere that is `fuzzy' (non-commutative) due to a magnetic monopole at its centre. It offers distinct advantages including the exact preservation of rotation symmetry, the direct observation of emergent conformal symmetry and the efficient extraction of conformal data. In the fuzzy-sphere method, the state-operator correspondence plays an essential role. Specifically, there is a one-to-one correspondence between the eigenstates of the critical Hamiltonian on the sphere and the CFT operators, where the energy gaps are proportional to the scaling dimensions. The power of this approach has been first demonstrated in the context of the 3D Ising transition, where the presence of emergent conformal symmetry has been convincingly established. Since its proposal, various studies have greatly expanded its horizon, including accessing the conformal data of the 3D Ising CFT, developing and applying techniques to improve the numerical precision, realising various 3D CFTs at integer filling, exploring fractional quantum Hall (FQH) transitions, and studying the conformal defects, boundaries and lower-dimensional CFTs. In this thesis, I first review fuzzy-sphere regularisation and the numerical methods applied to it, and focus on three aspects of my own work: constructing 3D interacting CFTs from critical gauge theories and non-linear sigma models, extracting universal data for conformal defects and boundaries, and accessing criticality at fractional quantum Hall (FQH) transitions. First, we realise the critical gauge theories on the fuzzy sphere with the help of non-linear sigma models (NLSMs) with Wess-Zumino-Witten (WZW) terms. The NLSM captures help us match the symmetry and anomaly of fuzzy-sphere model with those of critical gauge theories with dynamical gauge fields coupled to critical matter. Our first target is the deconfined quantum critical point (DQCP), a paradigmatic beyond-Landau transition, featuring emergent SO(5) symmetry and field-theory dualities. We realise it on a 4-flavour fuzzy-sphere model and provide numerical evidence for approximate conformal symmetry and pseudo-criticality. We further generalise it into a new series of parity-breaking 3D CFTs with Sp(N) global symmetry, whose candidate theories include Chern-Simons matter theories. Second, we study conformal defects and boundaries, where defect or boundary deformations trigger RG flows to interacting defect or boundary fixed points (dCFTs/bCFTs) with reduced conformal symmetry. The bulk-defect/boundary interactions generate rich phenomena, advancing understanding of topological phases, confinement of gauge theories, quantum gravity, entanglement, and experiment. We introduce a computational strategy revealing a wealth of defect conformal data, including the first non-perturbative computation of an RG-monotonic quantity called $g$-function, via overlaps of different defect configurations; its power is shown for the pinning-field defect on the fuzzy sphere. On the other hand, we realise the ordinary and extraordinaryIsing boundary CFTs on the fuzzy sphere and reported the operator spectrum and OPE coefficients. Third, we extend fuzzy sphere to CFTs arising from fractional quantum Hall transitions. Many such transitions admit effective descriptions in terms of Chern-Simons-matter theories and dualities, and are motivated by experiments in Moiré materials. We present a minimal example described by a complex critical scalar coupled to U(1)_2 Chern-Simons gauge field, realised as a transition between a ν_f=2 fermionic integer quantum Hall state and a ν_b=1/2 bosonic fractional quantum Hall state. We show that the transition is continuous and governed by a conformal field theory with SO(3) global symmetry. The operator spectrum contains only one relevant Lorentz scalar, the SO(3) singlet with scaling dimension Δ_S=1.52(18), which tunes the transition. Finally, we construct free-Majorana-fermion CFTs on the fuzzy sphere using a set-up of boson-fermion mixture. On the phase diagram, we observe two continuous transitions described respectively by a free Majorana fermion and a gauged Ising CFT. We numerically confirm the emergent conformal symmetry through the operator spectrum and the two-point correlation function of the local Majorana fermion. We further establish a correspondence between the fuzzy-sphere models and the field-theory Lagrangians, and extend it to an interacting fermionic CFT --- the super-Ising theory with emergent super-conformal symmetry.qu
How to Color Graphs, and How Not to Chase Pointers
(University of Waterloo, 2026-05-15) Mittal, Parth
We present several results in the graph streaming and number-in-hand communication models. In the graph streaming model, the edges of the input graph stream by one-by-one, and the algorithm must process this stream with limited memory, which is significantly smaller than required to store the entire graph. Brooks' Theorem states that a graph with maximum degree Δ can be Δ-colored as long as it is not a clique or an odd-cycle. We show a 1-pass, O(n polylog(n)) space algorithm that can Δ-color a graph given as a stream. This is optimal up to log n factors. In the number-in-hand communication model, the input to some relation is partitioned between k players, who work together to compute an output to the relation while minimizing the number of bits they communicate to each other. We have three results in this model. First, we show an O(n) communication protocol that can (Δ + 1)-color a graph, whose edges are partitioned between two players. This is optimal up to constant factors. Our second and third results are about the pointer chasing problem. In the pointer chasing problem, two players receive functions from [n] → [n], and wish to find the sequence of elements of [n] obtained by applying their functions alternately k times on the starting element 1. We show that any k / 1000 round communication protocol that solves this task must use Ω(n) communication. This lower bound is optimal up to factors of log n. We also show an optimal lower bound for any (k - 1) round protocol that solves a version of this problem where each value of the input functions is further obscured behind a set intersection instance.
Forage Crop Productivity and Nutrient Use Efficiency on Newly Converted Boreal Podzolic Soils in Central Labrador
(University of Waterloo, 2026-05-15) Dhindsa, Aman
Climate change is driving agricultural expansion into Canada’s boreal north, however, the sandy acidic, and nutrient poor Podzolic soils resulting from forest-to-farmland conversion remain severely under studied for their capacity to support crop production. This study evaluated the effects of soil fertility enhancing treatments, including nutrient sources; inorganic mineral fertilizer, organic marine waste (e.g., shrimp compost, shrimp waste, and fish meal), forage biomass incorporation, and liming agents/organic matter inputs; limestone, peat moss, their combination, and biochar, on forage crops (e.g., oat, pea, and oat-pea intercrop). Yields, nutrient uptake, and nutrient use efficiency for N, P and K were evaluated across three boreal farmlands near Happy Valley-Goose Bay, Labrador, Canada. The three farmlands, Birch Lane (BL), Taiga Valley (TV), and Natures Best (NB), differed in conversion history, baseline soil fertility, and management, spanning a gradient from infertile, recently bulldozed mineral soil at TV to a moderately rehabilitated pasture under long‑term agricultural management at BL. Field experiments were conducted over two growing seasons (2023 and 2024) using randomized complete blocks with factorial design (factor 1: nutrient source, factor 2: liming agents/organic matter inputs). Results showed that forage crop yields and nutrient uptake for N, P, and K were influenced by both nutrient sources and liming agents/organic matter inputs applied (p<0.05), with the largest effects seen in the least fertile soils. While treatments were distinct but comparable across the three farms, forage responses were site specific, reflecting the overriding role of inherent soil fertility. In the longer-term managed BL field, inorganic and organic nutrient sources as well as application of limestone with peat, influenced yields, nutrient uptakes and nutrient use efficiencies (p<0.05). At BL, when shrimp compost was applied at similar N rates to the inorganic mineral fertilizer, shrimp compost produced higher yields. At the very recently converted TV, meaningful yields (above 1 t/ha) required the combined application of strong nutrient inputs with organic matter and acidity improving amendments (p<0.05). The application of hardwood biochar at TV, produced the highest yields and nutrient uptakes on the farm when paired with fish meal in the first year. At the intermediate fertility site NB, organic fertilizers, including shrimp compost and shrimp waste, performed similarly to inorganic mineral fertilizer, showing promise as locally useful organic marine waste by-products. Biomass incorporation contributed negligible available nutrients within a single season and did not improve yields above control. Nutrient use efficiency metrics revealed that high efficiencies were not solely a product of experimental soil inputs, but were likely influenced by inherent soil conditions, underscoring the importance of conversion history and cumulative land management on nutrient cycling in boreal agricultural soils. These findings provide evidence that northern boreal farms, such as those in Happy Valley-Goose Bay, can have agronomically meaningful forage crop production (5-7 t/ha) if soil fertility management is matched to site-specific constraints. The conversion history of these lands determines how intensive agricultural management must be to achieve crop productivity. As boreal agricultural development continues to expand in Newfoundland and Labrador and across northern Canada, this study highlights the importance of soil fertility management strategies that consider the interacting roles of nutrient supply, soil acidity, and organic matter status.
Development of Molecular-Pore-Containing Polymer Semiconductors via Thermal Side-Chain Cleavage for Enhanced Alcohol Vapor Sensing in Organic Thin-Film Transistors
(University of Waterloo, 2026-05-15) Papazotos, Jimmy
This work presents the development of a low detection-limit ethanol vapor sensor, operating as an organic thin-film transistor (OTFT). OTFTs have garnered much attention for their use in gas sensing applications; owing to their low-cost, relatively simple fabrication and ability to be deployed as miniaturized and wearable devices. As such, a series of polythiophenes were synthetized in this work with the aim of being the semiconductive channel material in ethanol vapor sensors. The materials were synthesized with various functionalized side chains – either thermally cleavable or stable in nature. The thermally cleavable sidechains (TCSs) are ester functionalities which can be removed and converted to carboxylic acids upon high temperature post-processing of the devices. The content of TCSs / thermally stable side chains within the polymers in the series were systematically altered to investigate their effect on sensing performance. It was found that complete side chain removal (owing to 100% use of TCSs) totally inhibits sensing performance due to collapse of the film morphology after post-processing. However, including thermally stable side chains in the polymer structure acts as a molecular scaffold and preserves film morphology after TCS removal. This imparts porosity into the thin-film, which facilitates analyte vapor diffusion into the sensing layer and consequently enhances the ethanol vapor sensitivity. A sensitivity increase of ~26% is observed after side chain removal in polymers containing molecular scaffolded structures, proving the formation of stable pores into the polymer films.
Anxiety disorder agreement among children with chronic physical illness and their parents
(University of Waterloo, 2026-05-15) Parks, Reese
Background: Assessment of child psychopathology using multiple informants provides a more comprehensive and accurate evaluation of child mental health; however, parent-child agreement is low-to-moderate in child psychiatry and tends to be lower for internalizing disorders. Children with chronic physical illness (CPI) are at an elevated risk of developing anxiety disorders, making accurate assessment especially important in this population. Despite this, longitudinal patterns and determinants of parent-child agreement in children with CPI remain underexplored. Objectives: The objectives of this thesis were to: (1) Estimate the magnitude of informant agreement for anxiety disorders on the Mini-International Neuropsychiatric Interview for Children and Adolescents (MINI-KID) between parents and children with CPI at baseline, 6, 12, 24, and 48 months, (2) Explore whether child sex moderates parent-child agreement, and (3) Identify sociodemographic and health factors associated with parent-child disagreement for anxiety disorders on the MINI-KID over time. Methods: Data for 119 dyads came from the Multimorbidity in Youth Across the Life-course (MY LIFE) study, a longitudinal study of children aged 2 to 16 years who had been diagnosed with a CPI and their primary caregiver. The prevalence-adjusted bias-adjusted kappa (PABAK) estimated the magnitude of agreement between parents and children with CPI at baseline, 6, 12, 24, and 48 months. Sex-stratified agreement analyses were conducted using the PABAK to investigate whether parent-child agreement was moderated by child sex. The method of variance estimates recovery (MOVER) was used to construct a confidence interval for the difference in κ estimates between male and female children at each timepoint. A generalized estimating equations model examined factors associated with parent-child disagreement over time. Results: Agreement ranged from fair to substantial over time (κ = 0.40-0.65). For male children, agreement was moderate to almost perfect (κ = 0.47-0.82), whereas for female children, fair to moderate agreement was observed (κ = 0.32-0.51). Moderation by child sex was only found at 6 and 48 months. Compared to baseline, time at 6 months (OR = 0.46, 95% CI = 0.23-0.91, p = 0.026) and 12 months (OR = 0.55, 95% CI = 0.31-0.97, p = 0.040) were associated with lower odds of disagreement. Female children were found to have significantly higher odds of disagreement compared to male children (OR = 2.04, 95% CI = 1.20-3.46, p = 0.008). Parents who were not partnered had lower odds of disagreement relative to partnered parents (OR = 0.27, 95% CI = 0.10-0.71, p = 0.008). Higher levels of parent psychopathology were also associated with increased odds of disagreement (OR = 1.15, 95% CI = 1.01-1.31, p = 0.032). Conclusion: Parent-child agreement ranged from low-to-substantial and varied over time. Moderation by child sex was only evident at 6 and 48 months. Predictors of parent-child disagreement may help identify dyads who may be at greater risk for informant discrepancies. Future research should examine the underlying mechanisms driving parent-child disagreement to inform targeted interventions that help strengthen agreement among parents and children with CPI.
Structural and Interfacial Engineering of 2,5-Dihydroxy-1,4-Benzoquinone Coordination-Polymer Cathodes for Sustainable Lithium-Ion Batteries
(University of Waterloo, 2026-05-14) Wang, Yonglin
Carbonyl-based organic compounds are one of the most promising sustainable cathode materials for next-generation lithium-ion batteries due to their highly reversible C=O redox center, high theoretical capacity, structural tunability, and potential derivation from abundant or biomass-related feedstocks. However, their practical deployment has been limited by intrinsically high solubility in conventional carbonate- and ether-based electrolytes. Dissolution-driven loss of active material not only leads to rapid capacity fading but also induces serious shuttle effect, self-discharge, and parasitic reactions. Therefore, suppressing dissolution is an essential prerequisite for achieving long-term stability and practical energy density in organic cathodes. Among various carbonyl-based candidates, 2,5-dihydroxy-1,4-benzoquinone (DHBQ) is attractive because of its high theoretical capacity (383 mAh g⁻¹), simple structure, and potential renewability. Yet DHBQ is highly soluble in common organic electrolytes, preventing stable cycling. In this thesis, coordination polymer (CP) synthesis was employed as the primary strategy to reduce the solubility of carbonyl-based cathodes. By incorporating redox-active quinone units into coordination frameworks, CP structures increase the energetic barrier for molecular detachment and solvation, thereby effectively suppressing dissolution. Moreover, CPs can be synthesized through relatively simple coordination reactions using accessible precursors, offering practical feasibility and potential scalability. In Chapter 3, a metastable quinone-based coordination polymer, Co-DHBQ·2H₂O, was investigated as a transition-metal-redox cathode. When cycled between 0.7–3.0 V, the electrodes undergo a reversible four-electron transfer process involving both DHBQ and Co redox reactions. Initial side reactions, including SEI formation and benzene-ring lithiation, lead to a high first-cycle capacity of 783 mAh g⁻¹. After stabilization, the cathode delivers 199 mAh g⁻¹ after 750 cycles, with 84% capacity retention between the 100th and 750th cycles. Structural analyses reveal that coordinated water molecules form strong hydrogen bonds (up to -40.5 kJ mol⁻¹) that stabilize the layered framework and preserve structural integrity during cycling. However, excessive lithiation at low voltages induces structural damage due to the metastable nature of the hydrogen-bonded layers. Comparative studies with anhydrous Co-DHBQ confirm that coordinated water is critical for maintaining structural integrity, enabling reversible Li⁺ accommodation, and achieving long-term electrochemical stability. In Chapter 4, a lithium-based, transition-metal-free Li₂DHBQ cathode was investigated to reduce mass penalty while maintaining low solubility. Although Li₂DHBQ exhibits extremely low solubility in the electrolyte, severe morphological degradation of the active material was identified as the primary origin of poor cycling stability. Repeated lithiation and delithiation induce particle fracture and progressive disruption of electronic percolation pathways, leading to capacity fading independent of dissolution effects. To address this issue, the discharge cutoff voltage was lowered to 0.5 V to promote electrolyte reduction and in situ formation of a protective solid electrolyte interphase (SEI) layer on the Li₂DHBQ surface. This strategy significantly enhanced morphological stability and improved electrochemical performance. When cycled between 0.5–3.0 V at 500 mA g⁻¹, the cathode maintained a capacity of 170 mAh g⁻¹ after 200 cycles, with a low decay rate of 0.16% per cycle. Furthermore, a preconditioning strategy in which the electrode was first cycled at 0.5 V for 20 cycles to form the SEI layer, followed by cycling within the normal 1.5–3.0 V range at 500 mA g⁻¹, resulted in even better performance, retaining 187 mAh g⁻¹ at the 200th cycle. In contrast, a cell cycled only within 1.5–3.0 V retained merely 87 mAh g⁻¹ after 200 cycles. These results demonstrate that controlled SEI formation effectively reinforces morphological stability, mitigates structural degradation, and substantially improves long-term cycling performance once dissolution has been suppressed. In Chapter 5, we build upon Chapter 4 and introduce a more controlled strategy for cathode surface stabilization through the incorporation of fluoroethylene carbonate (FEC) as a CEI-forming additive. The addition of 1 wt.% FEC promotes the formation of a robust CEI layer that significantly suppresses particle pulverization and enhances structural integrity during cycling. SEM and TEM analyses reveal that the optimized CEI layer is relatively uniform and approximately 30 nm thick, effectively mitigating active material degradation. As a result, the Li₂DHBQ cathode with 1% FEC exhibits substantially improved electrochemical performance. When cycled at 500 mA g⁻¹, the electrode retains 185 mAh g⁻¹ after 200 cycles with a low-capacity decay rate of 0.049% per cycle, compared to 81 mAh g⁻¹ and a decay rate of 0.302% per cycle for the FEC-free battery. In addition to enhanced cycling stability, the FEC-containing cell demonstrates superior rate capability, supported by a dominant capacitive contribution of up to 93.7%, indicating accelerated surface-controlled charge storage behavior. These findings confirm that CEI engineering via controlled additive incorporation effectively stabilizes the electrode structure, suppresses interfacial degradation, and optimizes charge storage kinetics once dissolution has been mitigated. The results highlight the importance of interphase design in enabling stable and high-rate organic cathode systems. Beyond electrochemical stability, in Chapter 6 this work also addresses sustainability and end-of-life considerations. A proof-of-concept recycling strategy for Li₂DHBQ-based cathodes was developed using solubility-selective disassembly. By exploiting the solubility contrast among active material, conductive additive, binder, and current collector, approximately 95% of Li₂DHBQ could be recovered under mild conditions. This result highlights the intrinsic compatibility of organic cathode systems with low-energy and environmentally benign recycling pathways. Overall, through coordination polymer immobilization, interfacial engineering, and recyclability-oriented electrode design, this work provides coherent design principles for developing stable, insoluble, and recyclable carbonyl-based cathodes toward sustainable lithium-ion battery technologies.
Evaluating the form and mobility of phosphorus in urban streambed sediment
(University of Waterloo, 2026-05-14) Gijzen, Jonathan
Phosphorus (P) enrichment remains a primary driver of eutrophication in freshwater systems, yet the processes governing its storage, transformation, and mobility in urban watersheds are not well documented. Sediment is the dominant vector for P transport in aquatic systems, and its fate is governed by interacting physical and biogeochemical processes. Once deposited, streambed sediments act as both sinks and sources of P, regulating downstream P transport during environmentally sensitive summer baseflow conditions through sediment-water dissolved P exchange. This study investigates the distribution, speciation, and mobility of sediment-associated P in a small (76 km²), predominantly urban watershed (Laurel Creek, Ontario, Canada), with an emphasis on fine-grained streambed sediments and longitudinal variability along the river continuum. Sequential extraction was used to quantify PP fractions (NAIP, AP, OP), while the major element composition of streambed sediment (Fe, Al, Mn, Mg, Ca, Na) was measured to assess geochemical controls on PP partitioning. Phosphorus mobility was evaluated using the equilibrium phosphate concentration (EPC0) and phosphate exchange potential (PEP) to determine the potential of stream sediments to function as sources or sinks of soluble reactive P (SRP). Results demonstrate that both PP form and mobility are strongly influenced by land use and impoundments. Total particulate phosphorus (TPP) ranged from 424 to 1220 µg g⁻¹, indicating substantial P storage within streambed sediments. Bioavailable PP (NAIP) was significantly enriched in agricultural headwaters (U = 0, p < 0.01) and was strongly correlated with Fe, Al, Mn, and organic matter. Downstream trends in sediment geochemistry, characterized by decreasing Fe, Al, and Mn and increasing Ca and Mg, were associated with reduced bioavailable P fractions. Similarly, higher EPC0 values and positive PEP were observed at agricultural sites, indicating a greater potential for SRP release during baseflow. In contrast, urban sites displayed lower and more variable EPC0 and PEP values, showing both potential source and sink behavior. Impoundments appear to play a key role in attenuating P transport in Laurel Creek, with reduced PP concentrations and lower EPC0 observed downstream, suggesting retention of P-rich sediments within upstream reaches. Overall, sediment P dynamics in Laurel Creek watershed are controlled by interactions among land use, geochemistry, and urban impoundments. Fine sediments function as both legacy P reservoirs and regulators of SRP, varying along the river continuum. These findings highlight the importance of integrating P speciation and mobility assessments to improve understanding of in-stream P cycling and to inform management strategies aimed at reducing downstream eutrophication in urbanizing watersheds.
Beyond the Lab: Integrated Biosensing Platforms for Point-of-Care Diagnostics and Continuous Monitoring in Blood, Skin, and Brain
(University of Waterloo, 2026-05-13) Keyvani, Fatemeh
Conventional disease diagnosis and health monitoring rely on centralized laboratory testing that requires invasive biofluid collection, complex processing, and specialized equipment. These methods are costly, time-consuming, and provide only intermittent data, limiting their utility for timely decision-making. This thesis addresses these challenges by developing advanced biosensing platforms that combine minimally invasive sampling with different detection modalities to enable point-of-care (POC) diagnostics and continuous monitoring. The overarching vision is to enable on-site biomarker quantification and continuous monitoring of disease-relevant indicators. The first platform focuses on POC screening for cervical cancer (CC), a disease that disproportionately affects women in low- and middle-income countries due to limited access to screening. We developed an Integrated Microfluidic Electrochemical Assay for Cervical Cancer (IMEAC), a low-cost and user-friendly system that combines a force-free plasma separation module with a graphene oxide-based electrochemical biosensor. The plasma separator isolates high-purity plasma directly from whole blood, while the biosensor employs sequence-specific probes to detect circulating tumor nucleic acid. The second platform expands the concept of decentralized diagnostics toward general clinical biomarker monitoring. We designed a hydrogel microneedle (HMN)–based assay capable of sampling interstitial fluid (ISF) in a minimally invasive manner. The extracted ISF is analyzed using a graphene oxide–nucleic acid (GO.NA) fluorescence biosensor, enabling real-time detection of clinically relevant biomarkers. This system was complemented with a portable fluorescence detector, yielding a complete and user-friendly POC solution. Building upon these foundations, the third platform centers on therapeutic drug monitoring (TDM), a clinical necessity for optimizing treatment efficacy. We developed a hybrid microneedle–flexible electrode biosensor (HMN-Flex) capable of real-time monitoring of two widely used antibiotics: vancomycin and gentamicin. The HMN array extracts dermal ISF and delivers it to an electrode, where target antibiotic concentrations are quantified electrochemically. The HMN-Flex system was validated in-vivo using rat models, with pharmacokinetic profiles showing strong concordance with conventional blood-based assays. The fourth platform translates the principles of minimally invasive, continuous biosensing into the neurocritical care environment. Patients with external ventricular drains (EVDs) require close monitoring of cerebrospinal fluid (CSF) to detect complications such as infection and drain malfunction. We developed NeuroSense, a multiplexed sensing platform that integrates seamlessly with standard EVD systems to provide continuous, real-time monitoring of CSF. NeuroSense provides measurements of CSF glucose, lactate, pH, and flow rate, thus reporting about potential infection and EVD malfunction. Taken together, the works presented in this thesis demonstrate how integrating novel sampling strategies, nanomaterial-enabled biosensors, and system-level design with interdisciplinary advances in microfluidics, microneedles, and electrochemical and optical sensing can overcome intrinsic limitations of laboratory-based diagnostics. These platforms establish a technological foundation for next-generation healthcare systems that prioritize accessibility, timeliness, and personalization, with the potential to improve patient outcomes in high-resource clinical settings and expand access to quality care in underserved regions worldwide.
Radiometric properties of galvannealed steel
(University of Waterloo, 2026-05-13) Kagaya, Michiyo
Galvannealed steel has a wide application in automobile industry due to its superior corrosion resistance, weldability, and paint adhesion. The intermetallic phases in galvanneal coatings are connected to these desired properties and strongly influenced by the heating schedule during the manufacturing process. However, the coating transformation during the galvannealing process affects radiometric properties of galvanneal coating and complicates inline pyrometry. Inaccurate temperature readings and lack of inline surface state monitoring methods lead to deficient properties and increase scrap rates during production. A good understanding of the radiometric properties of galvannealed steel helps improve inline pyrometry and potentially enables inferences of coating surface state based on radiometric measurements, but currently there is a lack of in-depth studies on radiometric properties of galvannealed steel in literature. This thesis fills the knowledge gap by first correlating the radiometric properties of industrially galvannealed steels with their surface morphology and phase composition through ex-situ analysis. The ex-situ data gives insight into pyrometry improvement and inline surface state monitoring with galvannealed steel, which are further verified with in-situ study. Results from in-situ radiometric experiments on lab-simulated galvannealing process at different wavelength ranges further confirm previous findings from the ex-situ analysis. The radiometric properties of galvannealed steel rapidly change during production due to the coating transformation. The 1.5–2.5 μm wavelength range, part of the near-infrared (NIR) region, is ideal for pyrometry due to the consistent radiometric behavior between different surface state of galvanneal coatings. The optimal wavelength range of inline surface state monitoring is the mid-infrared (MIR), or 2.5–15 μm, due to the strong correlation between galvanneal surface state and radiometric properties observed in this range.
Applying Fisher Knowledge and Scientific Data to Understand Species Importance in Chilika Lagoon, India
(University of Waterloo, 2026-05-13) Serrao, Natasha
Direct monitoring of fish biodiversity can be challenging because of financial and logistical constraints. Conservation biologists have designated “surrogates”, which are a small number of species that represent the health of other species and/or overall environmental conditions. One type of surrogate, a keystone species, is a species whose impact is disproportionate relative to its abundance; the absence of which would change the dynamics of an ecological or a human community. This thesis advances a novel approach for identifying important fish species in Chilika Lagoon, India, by engaging with keystone literature as a starting point. To achieve this, four specific objectives guide this research: to 1) examine the strengths, drawbacks, and gaps associated with both ecological and cultural approaches to measuring keystone species status, 2) compare vernacular naming conventions for locally important fish across three ecologically and geographically disparate villages within Chilika Lagoon, India, 3) to identify the most important fish within each of the three Chilika Lagoon villages, and 4) to further investigate the social and ecological dimensions that guide fish importance. To achieve these objectives, I applied a mixed-methods approach, with an emphasis on community-based approaches. First, a systematic scoping review was applied to examine the literature on keystone within the fish and fisheries context between 2014 and 2023. Key findings from this chapter highlight that many studies use the term “keystone” without formally testing to see if it is a keystone. Second, this review underscores the importance of ecosystem dynamic modelling through the program Ecopath with EcoSim in designating keystone species within an ecological context. Lastly, findings from this review point to an over-emphasis on ecological dimensions and an under-emphasis on cultural-social dimensions of the keystone concept. A recommendation from this chapter was to incorporate local knowledge of fisher folk when designating keystone species status. To better contextualize fisher knowledge with respect to individual species, there is a need to link vernacular and scientific fish naming, so that results can be interpreted between formal as well as traditional/customary resource management systems. To achieve this, photos of 56 locally important fishes were shown to fishers (n = 108) across three villages by applying an age-gender-village approach wherein equal fishers were selected within each of age and gender groupings for each village. Key findings from this chapter highlighted that most species had multiple vernacular names, with many of these names being the result of phonetic differences. Secondly, no notable age or gender differences was apparent in the study sites in how people name fish. Lastly, village differences were apparent in how fish are named across all three communities. This research created a path forward for grouping locally important fish species by their vernacular names. Subsequently, a community-based approach was undertaken to designate important species in Chilika Lagoon, and to identify the rationale guiding perceptions of their importance. To achieve this, household surveys (n = 90) were administered across the three villages by applying a gender-village approach. Key findings highlight that while many important species were village-specific, commercially lucrative fish were identified as consistent across all villages. Additionally, women tended to select cultural reasons for fish importance, while men tended to select ecological and economic reasons. The results presented in these three chapters illustrate the importance of taking a multi-dimensional approach that considers both keystone theory and community-based perspectives for designating and conserving important fish species. Chapter two provides us with a comprehensive understanding of the term keystone as it relates to fisheries holds significance because over 700 papers were examined to understand the trends, gaps, and strengths associated with the keystone concept. The value fisher knowledge can bring to designate keystone species was emphasized, and fisher perspectives were placed at the forefront of Chapters 3 and 4. This dissertation also helps to create stronger linkages between scientific and vernacular names (Chapter 3). Properly documenting the linkages between vernacular and scientific naming of local fish is necessary before fisher knowledge can be interpreted and applied. The linkages also allow us to accurately reference animal and plant species despite the many names applied across different languages. This initiative was significant because it captured the link between scientific and local names of Chilika fish and served as the building block to provide important insights for data interpretation and analysis of Chapter 4. Third, this research resulted in a novel method for determining species significance within a community because I consider both village and gender, as well as the underlying rationale for fish importance put forward by fishers, instead of using the keystone indicators from academic literature that were mentioned in Chapter 2. Fourth, this dissertation considers the intracultural diversity within the fisher community, and the importance of gender and village in helping to shape perspectives. These findings are important for both research and policy because it emphasizes the heterogeneity of fisher perspectives, and that findings from one group cannot be extrapolated to another.
Design, Dynamics, and Control of Upper-Limb Exoskeleton Robots
(University of Waterloo, 2026-05-13) Wang, Yuntian
Modern technology has enabled great improvements in the design and control of exoskeletons, which can assist users in various applications, including rehabilitation, muscle fatigue reduction, and power augmentation. However, existing power augmentation exoskeletons still face challenges in user comfort and transparency to the user. To improve the power augmentation, an active-passive shoulder exoskeleton was designed in a previous study, which combines the benefits of active and passive actuators, and was controlled by an electromyography-based (EMG) method. However, EMG-based control is sensitive to probe placement and unsuitable for factory use, while force/torque sensors add cost and depend on reliable contact. Therefore, we pursue model-based controllers of this active passive platform, without EMG or force/torque sensors. We first built a high-fidelity skeletal shoulder model in MapleSim, to guide our exoskeleton mechanical and controller designs. It was combined with the exoskeleton model to evaluate the proposed methods. To reduce unnecessary fatigue induced by human exoskeleton misalignment, it is important to understand the moving joint center of the human shoulder complex. The scapular kinematics is especially complex, so we proposed a simplified scapulothoracic model and validated it using bone-pin measurement data. To reduce human effort, a low impedance is required, but the long support chains in shoulder exoskeletons inherently make it prone to vibration. Hence, we proposed a model based vibration attenuation (VA) method for the exoskeleton in question. Static and dynamic human efforts were separately compensated, and the vibration attenuator was derived from identified structural elasticity. Furthermore, variable impedance can improve user comfort, but existing variable impedance profiles require expert tuning; thus, a new variable impedance law (Var-V) was proposed based on human biomechanics, which requires minimal tuning. To evaluate the proposed VA method and variable impedance law, we developed: i) a high-fidelity human-exoskeleton model in MapleSim; ii) a new 1-degree-of-freedom (DOF) human-exoskeleton adaptation model in MATLAB (CNS-MTG); iii) human-in-the-loop (HITL) experiments based on surface electromyography (sEMG). The MapleSim model assumes a perfect human adaptation that is not gradual, but it is more realistic than the 1-DOF adaptation model. The CNS-MTG adaptation model combined the human motor learning with muscle torque generator models, so that it has the advantages of both models. Two sets of HITL experiments were conducted: one for the VA method with a single participant, and the other for both the VA method and variable impedance laws with ten participants.
Adaptive Differential Privacy Budgeting Strategy for Optimizing Synthetic Data Generation and Privacy–Utility Trade-offs
(University of Waterloo, 2026-05-13) Padalko, Kateryna
Training generative models under differential privacy (DP) requires injecting calibrated noise into gradient updates, creating an inherent trade-off between privacy protection and data quality. In standard DP-CTGAN, a single discriminator processes all features under a shared privacy budget, so noise injected to protect sensitive demographic attributes equally degrades the learning signal for non-sensitive features, an architectural limitation, not a mathematical one. We propose the Dual-Path DP-CTGAN, a discriminator architecture that partitions features into sensitive and non-sensitive paths, each governed by its own DP-SGD mechanism and Rényi DP accountant. Gradient isolation confines privacy noise to its respective path, preserving the learning signal for non-sensitive features without relaxing the formal (ε, δ)-DP guarantee. By the post-processing theorem, the generator inherits the privacy guarantees of both paths without additional composition. We embed this architecture in a Bayesian multi-objective hyperparameter optimisation pipeline that jointly evaluates utility, distributional fidelity, and empirical privacy risk, using Pareto-dominance selection to surface non-dominated configurations. Experiments on the Adult Census Income benchmark demonstrate that Dual-Path at ε = 1 achieves distributional fidelity below the non-private baseline and reduces the downstream utility gap by 79% relative to single-path DP-CTGAN at the same budget, exceeding single-path performance at ε = 5 while maintaining comparable empirical privacy risk. Per-feature analysis confirms that the fidelity gain concentrates in the feature group freed from cross-path noise contamination, providing direct evidence for the gradient isolation mechanism. These results suggest that discriminator architecture, rather than the noise mechanism itself, is the primary bottleneck limiting utility in standard DP-GAN designs.
Mitigating Risks to Dependability from Vibe-Coding C for Embedded Systems
(University of Waterloo, 2026-05-13) Dunne, Murray
Vibe coding is the process of using a Large Language Model (LLM) to iteratively generate software code. It is popular, with 36% of workers at technology companies reporting adoption of generative artificial intelligence for software engineering in 2024 [1]. At this rate of use, LLM-generated code is quickly becoming part of the embedded-systems that comprise our everyday cyber-physical infrastructure. Most of this infrastructure is built on C language code [2]. LLM-generated C code poses threats to dependability, exhibiting faults such as buffer overflows, out-of-bounds writes, integer overflows, and more. In this work, we contribute methods for improving the dependability of these systems in three key parts: providing a real-world benchmark dataset for evaluating LLM-generated C code, protecting LLM code generation from poisoning attacks, and detecting changes in production embedded systems through power side-channel analysis. This work begins with an examination and categorization of weaknesses in LLMgenerated C code for embedded systems networking. Our findings suggest that LLMs perform poorly at programming tasks involving direct interactions with memory. Scores on existing LLM-generated C benchmarks do not adequately express this difficulty, as these benchmarks do not include sufficiently real-world C programming challenges. To support future testing of LLMs, we introduce EmbedEvalC, a dataset of C coding challenges to provide a benchmark against which LLMs can be evaluated on real-world tasks. Retrieval Augmented Code Generation (RACG) is an essential tool for vibe coding, but presents new threats to dependability from poisoning attacks. If an attacker can cause a RACG system to retrieve their crafted documents, they can induce the LLM to generate code with weaknesses. To detect this attack, we introduce canary functions, a process by which specific functions in the codebase are regenerated and re-tested to determine whether the addition of new documents induces new weaknesses. Finally, we consider the black-box setting where a systems integrator seeks to detect unexpected changes in embedded firmware. Such changes will only become more common with the proliferation of vibe coding. We suggest using power side-channel analysis to provide a feedback mechanism to a fuzzer in order to determine if a fuzzing input has caused a new response from the system. We show that responses involving five or more memory-interacting instructions are consistently detectable. In this work, we suggest a collection of techniques to mitigate risks to the dependability of embedded systems posed by LLM-generated C code. Abstract Citations: [1] Alex Singla, Alexander Sukharevsky, Lareina Yee, Michael Chui, and Bryce Hall. "The state of AI: How organizations are rewiring to capture value", McKinsey & Company, March 2025. [2] P. Soulier, D. Li, and J. R. Williams, “A Survey of Language-Based Approaches to Cyber-Physical and Embedded System Development,” Tsinghua Science and Technology, vol. 20, no. 2, pp. 130–141, 2015.
Efficiently Training Deep Learning Models on Elastic and Heterogeneous Cloud Resources
(University of Waterloo, 2026-05-12) Guo, Runsheng
Deep Neural Networks (DNNs) have demonstrated remarkable success across diverse domains, but their training requires substantial computational resources and is typically parallelized across large GPU clusters. However, such clusters are prohibitively expensive for most organizations to own and manage. Hence, instead of owning and managing their own clusters, organizations often rent clusters on cloud platforms to meet their training needs. While cloud environments offer elastic scalability and heterogeneous hardware options, they also introduce significant challenges for efficient distributed DNN training. Specifically, existing training frameworks lack support for dynamic reconfiguration during training, limiting the exploitation of cloud elasticity. Additionally, most systems assume homogeneous clusters, which rarely reflect the heterogeneous GPU clusters that organizations commonly use due to hardware availability constraints. Furthermore, heterogeneous network conditions in cloud environments create communication bottlenecks that limit the scalability of existing approaches. This thesis presents three systems that collectively address these limitations to enable efficient distributed DNN training on elastic and heterogeneous cloud resources. First, Hydrozoa leverages cloud elasticity through serverless containers, enabling dynamic scaling and configuration changes during training without the traditional pitfalls of serverless computing. By combining data and model parallelism with fine-grained resource provisioning, Hydrozoa achieves cost-effective training while eliminating cluster management overhead. Second, Cephalo addresses heterogeneous GPU clusters by independently balancing compute and memory resources across GPUs with different capabilities. Unlike existing approaches that tie workload assignment to computational speed, Cephalo separately optimizes compute distribution through proportional batch sizing and memory utilization through intelligent partitioning of training state, activation checkpointing, and gradient accumulation strategies. Third, Zorse tackles heterogeneous network conditions, which are particularly common in heterogeneous clusters, by efficiently combining memory-efficient data parallelism with pipeline parallelism. Through interleaved pipelining, parameter and activation offloading, and heterogeneous pipeline parallelism configurations, Zorse achieves both communication and memory efficiency for training large DNN models across diverse network topologies. The experimental evaluation demonstrates that these systems significantly improve training efficiency and resource utilization compared to existing approaches. Hydrozoa reduces training costs while providing seamless scalability, Cephalo simultaneously achieves high compute and memory utilization in heterogeneous clusters, and Zorse maintains high throughput under varying network conditions. Together, these contributions make distributed DNN training more accessible, cost-effective, and efficient in modern cloud environments, advancing the state of the art in large-scale machine learning infrastructure.

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