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Recent Submissions

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    Dissolvable Sugar-Based Untethered Magnetic Millimeter-Scale Robot for Blood Clot Removal
    (University of Waterloo, 2026-01-22) Sparkes, Sarah
    Thrombosis, or the formation of blood clots, is a potentially life-threatening condition that results in the complete or partial occlusion of a blood vessel. It remains one of the most prevalent causes of death worldwide. Current treatment approaches involve the intravenous administration of thrombolytic drug, which can increase the risk of uncontrolled bleeding, or catheter-directed treatments, which may have limited access to hard-to-reach areas of the vasculature and can cause catheter-related injuries. Untethered magnetic robots present an alternative approach for thrombosis treatment that addresses the current shortcomings. In this work, a rapidly dissolvable, millimeter-scale, magnetic robot is proposed for the delivery of thrombolytic drug for thrombus removal. The robot is composed of a sucrose-based material with embedded superparamagnetic iron oxide nanoparticles for magnetic control. Although assessment of the robot actuation showed limited forward propulsion from its helical structure, it was able to withstand a maximum flow rate of approximately 72 mL/min, which is comparable to the literature for small-scale magnetic robots. The mean dissolution time of the sucrose structure was 4.65 minutes. The blood compatibility of the robot material was measured through the upregulation of platelet activation markers and found to improve with decreasing material concentration. The drug delivery mechanism consisted of a sealed cavity along the center of the robot to maximize thrombolytic load and avoid drug exposure to high temperatures during fabrication. Release of a placeholder fluorescent protein was found to be gradual across the entire dissolution of the robot. To ensure the thrombolytic agent was not compromised upon loading into the robot, in vitro incubation with human thrombi was performed. The thrombolytic-loaded robot showed similar thrombus mass reduction compared to the direct administration of thrombolytic agent. Finally, the robot functionality was validated using an ex vivo endovascular thrombosis model of the sheep iliac arteries. The robot was clearly visualized under x-ray fluoroscopy due to its embedded magnetic material, enabling its guidance to the ex vivo thrombus via an external rotating permanent magnet mounted on a robotic arm. Successful navigation through a vascular bifurcation was demonstrated and robot mechanical action was shown to accelerate clot mass reduction. The effect of localized thrombolytic delivery on clot mass reduction in the ex vivo model was inconclusive. Overall, the proposed untethered and dissolvable robot would enable the localized delivery of thrombolytic agent to blood clots without the need for retrieval. This can lead to improved patient outcomes by reducing the risks of catheter-related injuries and uncontrolled bleeding resulting from the systemic administration of thrombolytic agents.
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    Resource Management for Edge-Assisted Extended Reality
    (University of Waterloo, 2026-01-22) Pei, Yingying
    Extended reality (XR) enables immersive experiences by seamlessly merging the physical and digital worlds. Supporting such experiences requires real-time and high-quality rendering of virtual content to generate video frames, which is computationally intensive and poses a challenge for resource-constrained XR devices. To overcome this limitation, a promising approach is to offload rendering tasks to nearby edge servers with powerful computing resources. In an edge-assisted XR system, interdependent tasks, including video frame rendering, encoding, and transmission, need to be executed in a pipeline, which consumes substantial communication, computing, and caching resources. The efficiency of network resource provisioning has a direct impact on users' quality of experience (QoE), which reflects the presence and immersive of a user during virtual content viewing and is measured by the weighted sum of visual quality, quality variation, and round-trip latency. Our objective is to efficiently manage multi-dimensional network resources for the XR service to improve user QoE under dynamic network environments. However, the technical challenges are as follows: 1) given the spatiotemporally varying service demand caused by user mobility, how to proactively provision edge resources for the service while achieving satisfactory user QoE; 2) how to adaptively allocate edge resources for individual users to accommodate demand fluctuations caused by dynamic viewing behavior; and 3) in the presence of task dependencies in the pipeline, how to jointly coordinate task processing parameters (e.g., rendering quality, frame encoding type) to improve user QoE. In this thesis, we design efficient resource management schemes for an edge-assisted XR system to address the above challenges. First, a mobility-aware resource provisioning scheme is proposed to enhance resource utilization while satisfying user QoE on a large timescale. Specifically, we present a mobility model tailored for XR users to capture both user spatial movements and interaction features. Then, we estimate user-specific model parameters and adopt a sample average approximation method to model the relationship between user QoE and the consumption of communication and computing resources. A coordinate descent algorithm is designed to make resource reservation decisions, where a deep neural network provides a valuable initial point to accelerate convergence. Simulation results demonstrate that the proposed resource provisioning scheme is more efficient in reducing network resource consumption while satisfying user QoE, compared with benchmark schemes. Second, we develop an adaptive volumetric video caching and rendering scheme to enhance real-time user QoE by considering dynamic user viewing behaviors. Particularly, volumetric videos of different quality levels need to be cached, rendered, and delivered to XR devices for different viewing distances within a time latency. Given limited resources for the service, we formulate a user QoE maximization problem to jointly optimize volumetric video caching and rendering decisions based on users’ real-time locations and viewing distances. To solve this problem, we first design an online regularization-based optimization algorithm to obtain caching decisions. We then present a low-complexity binary search algorithm to determine optimal rendering quality. Simulation results demonstrate that the proposed scheme achieves higher real-time user QoE in comparison with benchmark schemes. Third, we design a scheme for joint selection of rendering quality and encoding type by considering the interdependency among edge processing tasks to enhance long term user QoE. To cope with network dynamics, the rendering quality of frames can be dynamically adjusted, which in turn triggers an intra-frame encoding and leads to a sudden transmission burst. To capture such task interdependency, we formulate a long-term QoE maximization problem under edge computing and communication resource constraints, which jointly selects the rendering quality and either intra- or inter-frame encoding for each frame. To solve this problem, we theoretically analyze the impact of per-frame decisions on long-term QoE and present an online algorithm for decision-making. Simulation results demonstrate that the proposed joint rendering quality and encoding type selection scheme can further enhance resource utilization and long-term user QoE compared with benchmark schemes. In summary, we have proposed a mobility-aware resource provision scheme, an adaptive volumetric video caching and rendering scheme, and a task dependency-aware rendering quality and encoding type selection scheme for an edge-assisted XR system. This research should provide useful insights for network operators to deliver immersive XR services at low operational costs.
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    The Contributions of ESRP1 to the Functions of the Intestinal Epithelium
    (University of Waterloo, 2026-01-22) Francis, Jordan
    Epithelial Splicing Regulatory Proteins 1 and 2 (ESRP1 and ESRP2) are RNA binding proteins expressed exclusively in epithelial cells. They direct a splicing program necessary for maintaining important epithelial cell characteristics, including cell-cell adhesion, anchorage to the basement membrane, and cell-cell communication. ESRP1 and 2 have been studied for their importance in development. The loss of ESRP1 causes a series of craniofacial defects called cleft lip and cleft palate, while the loss of both ESRP1 and ESRP2 result in more severe versions of these defects, several epithelial organ formation defects, and a skin barrier defect which causes significant water loss. ESRP1 is known for its role in craniofacial development, epidermal barrier development, and cancer progression. However, its role in other epithelial organs where it is highly expressed, such as the large intestine, remains understudied. Mice with a hypo-morphic mutation of Esrp1, termed triaka, exhibited decreased intestinal wound healing and increased intestinal permeability. Thus, we hypothesized that ESRP1 contributes to intestinal homeostasis and intestinal barrier integrity by sustaining tight junction localization and intestinal cell proliferation. This thesis project sought to investigate the functions of ESRP1 in maintaining intestinal homeostasis using mouse colon organoids and mouse colon organoid-derived monolayers as a model. Upon the deletion of ESRP1 and subsequent mechanical dissociation of the organoids, we observed a significant decrease in organoid re-formation. Esrp1 KO organoids exhibited no change in organoid cell proliferation. However, they did exhibit a decrease in the expression of Lgr5, an intestinal stem cell marker and receptor for R-spondin. LGR5 helps to maintain the stem cell niche by promoting the Wnt signalling cascade through binding to R-spondin, a Wnt agonist. Thus, its downregulation in Esrp1 KO organoids suggests that ESRP1 helps sustain of the intestinal stem cell niche by maintaining the response of the intestinal epithelium to the Wnt signalling cascade. As R-spondin is produced by subepithelial stromal cells, this would suggest that ESRP1 is necessary for proper epithelial-mesenchymal communication in the intestine. This is ultimately similar to its observed role in craniofacial development. Through investigating the role of ESRP1 in maintaining intestinal barrier integrity, ZO-1 staining showed that tight junctions are still able to assemble properly in Esrp1 KO organoids but become more diffuse in Esrp1 KO monolayers. The loss of ESRP1 resulted in a slight decrease in the barrier integrity of the organoid-derived monolayers. This contrasts with published findings in other cell lines, suggesting that the dependence of epithelial barrier integrity on ESRP1 may vary based on tissue and the chosen model of the epithelium. These findings will provide a solid foundation for further investigations into the role of ESRP1 in maintaining intestinal homeostasis, which will enable future research to uncover its connection to pathological conditions such as Inflammatory Bowel Disease.
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    Practically Efficient Protocols for Private Computation using Homomorphic Encryption
    (University of Waterloo, 2026-01-22) Akhavan Mahdavi, Rasoul
    Digital services have become an indispensable part of our daily lives, particularly services that interact with our most private and sensitive data. With the abundance of such services, users are left to make the difficult choice: can I safely use digital services and products, or does it necessarily come at the cost of my privacy. Private computation techniques empower service providers to perform computation over private data, without the need to observe the data. This not only provides privacy for clients while the data is being used but reduces the risk of incidents such as data leaks for service providers. One commonly used tool for private computation is Homomorphic Encryption (HE), which is a form of encryption that allows computation on data in encrypted form. While homomorphic encryption in theory permits arbitrary computation over encrypted data, in practice, a naive implementation of a desired functionality rarely yields a practical result. For example, one common obstacle when using homomorphic encryption is the high computation time and the large ciphertexts that incur high network costs. However, communication and computation costs are not the only metrics that need to be considered. In my work, we describe problems that arise when homomorphic encryption is used in applications and address these limitations by proposing new techniques and novel protocols. In these new constructions, we not only improve the performance compared to prior work in terms of communication and computation costs but also address additional problems that arise in the deployment of these protocols. Throughout the process, we draw insights on how to design protocols that can be applicable for developers, practitioners, and future researchers. For example, we enable homomorphic comparison of encrypted numbers with higher precision than previous work, using novel representation of numbers that is more suitable for homomorphic encryption. Using this and other building blocks, we propose efficient protocols for decision tree evaluation and private set intersection. Moreover, through our work on private information retrieval, we identify the challenges of using such a protocol in practice and propose novel protocols that are suited for deployment in real-world applications.
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    Bayesian Inference for Partial Differential Equations via Neural Network Surrogates
    (University of Waterloo, 2026-01-22) Zhen, Zihao
    Partial differential equations (PDEs) provide the fundamental framework for describing physical systems; yet, in many practical applications, these equations contain unknown parameters that must be inferred from experimental observations. Solving such inverse problems using traditional mesh-based numerical methods is often computationally intensive; furthermore, because these solvers cannot be easily differentiated with respect to model parameters, they create significant bottlenecks for gradient-based inference. To address these challenges, we train parameterized Physics-Informed Neural Networks (PINNs) for two distinct systems: the Allen–Cahn and Cahn–Hilliard (AC–CH) phase field equations and diffusion models for cyclic voltammetry (CV). These surrogates demonstrate strong generalizability across continuous parameter spaces and serve as differentiable components for gradient-based Bayesian parameter estimation via the No-U-Turn Sampler (NUTS). This work verifies the feasibility of a unified PINN-surrogate-Bayesian workflow for parameter estimation, offering a promising complement to existing methods for solving inverse problems with uncertainty quantification.