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Recent Submissions

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    Improving Robustness to Unknown Disturbances by Expanding the Region of Attraction Near the Current State
    (University of Waterloo, 2026-09-16) Liu, Elin
    Physical systems often experience unknown disturbances that can disrupt their safe operation. For example, consider a drone experiencing a wind gust, a power system experiencing a voltage spike, or an autonomous vehicle encountering a sudden obstacle. The normal or safe operating point of these systems can be represented by an equilibrium point. The RoA is the set of all states for which the system can return to the standard operating point. However, calculating a system's RoA is often very difficult and computationally intractable. Expanding the RoA is an effective method to increase a system's robustness against such disturbances. Expanding the RoA is also very challenging. Because of this, instead of expanding the true RoA, existing methods often expand estimates of the RoA, which can be overly conservative or computationally intractable for higher-dimensional systems. Additionally, these methods are limited to systems and/or controllers that adhere to specific specialized structures, and do not generalize to many nonlinear systems of practical importance. Furthermore, these methods work to expand the RoA in all directions as opposed to near the current state. Expanding the RoA near the current state maximizes the stability of the system given the current conditions, whereas expanding the RoA in all directions imposes unnecessary constraints and could lead to less stability given the current conditions in exchange for more robustness somewhere less relevant. In this thesis, we first use results from dynamical systems theory to re-express the challenging and abstract problem of expanding the RoA near the current state as a concrete max-min numerical optimization problem. This increases robustness against unknown disturbances under current conditions. To do so, we exploit properties of trajectory sensitivities, which measure how susceptible the system's behaviour is to changes in initial conditions and parameters. Trajectory sensitivities can be efficiently computed numerically. The inner optimization finds the closest point on the RoA boundary from the current state for given parameter values, and the outer optimization varies parameter values so as to maximize the distance to the RoA boundary. We then develop the ERA algorithm, which solves this problem efficiently using a particular choice of successive approximations that avoids the need for computationally intensive second derivatives, which are required for many common bi-level optimization solvers. Next, we provide a local convergence guarantee for the ERA algorithm for a large class of nonlinear dynamical systems, using a contraction argument to show convergence. Thus, the proposed ERA algorithm is applicable to a wide variety of practical engineered systems. Finally, we apply the ERA algorithm to two drone simulations in which the drone must take off, fly to a target destination, and then hover at its target despite unknown wind gusts which can disrupt its flight. The first drone uses an integral backstepping controller, and the second drone uses a cascaded PID controller modelled after the default Crazyflie controller as seen in the firmware. We show that, in simulation, with the nominal parameter values, the wind gust knocks the drone out of flight, whereas after the ERA algorithm is run to select new controller parameter values, the drone is able to fly safely to its destination despite experiencing the same wind gust. We then see this increased robustness against unknown disturbances after running the ERA algorithm, replicated in a physical experiment using a Crazyflie 2.1 and a hairdryer.
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    Form-Fitting Mass Timber Connections: Experimental Investigation of the Effect of Tenon Flange Angle on Structural Performance
    (University of Waterloo, 2026-09-16) Daviau, Maxime
    Mass timber construction has expanded rapidly as a lower-carbon alternative to conventional structural systems. However, connection design remains a critical factor affecting constructability, cost, and structural performance. Contemporary mass timber connections rely on steel hardware, proprietary connectors, and mechanical fasteners. While these systems are reliable and code-supported, advances in digital fabrication create opportunities to reconsider form-fitting connections. Despite their historical precedent in heavy timber structures, the structural behaviour of form-fitting glulam connections remains underexplored, particularly how geometry influences load transfer, deformation capacity, and failure mode. This thesis investigates form-fitting glulam connections for purlin-to-girder applications, with an emphasis on tenon flange angle in mortise-and-tenon geometries. The research program included a literature review, two stages of rapid prototyping, material characterization, full-scale experimental testing, and development of a preliminary mechanical model. The literature review established that wood anisotropy, especially its low tensile strength perpendicular-to-grain, and geometry govern load transfer and failure mode. It also identified inclined bearing surfaces as a potential means of redistributing load into compression and promoting embedment mechanisms. The rapid prototyping phase first evaluated traditional single-tenon, multiple flat-tenon, and multiple triangular-tenon configurations. The traditional tenon exhibited low capacity and brittle mortise splitting, while multiple tenons improved strength and deformation capacity through progressive engagement. The triangular-tenon configuration further improved performance, demonstrating that geometry can improve both capacity and failure response. A second prototyping phase then extended this concept to open-top wedge geometries compatible with vertical installation, supporting their selection for full-scale testing. Twelve full-scale glulam wedge-tenon specimens were tested under static loading. Three full-depth series varied the tenon flange angle, while one partial-depth series examined the influence of reduced tenon height. The results showed that flange angle significantly affected yield load, peak load, stiffness, post-yield behaviour, and failure mode. Larger angles produced higher yield and peak loads, whereas smaller angles reduced stiffness and capacity but increased the role of wedging, horizontal thrust, and progressive deformation. The partial-depth series behaved differently from the full-depth specimens, transitioning from side-face wedging to a hybrid wedge/notch mechanism once bottom bearing developed. A mechanical model was developed to estimate the yield load from inclined-face bearing, friction, and compression perpendicular-to-grain embedment. The model more closely reproduced the experimentally observed yield load trends than the CSA O86 fracture-shear provisions and a rounded dovetail-based prediction method. The proposed method applies to the onset of connection softening; ultimate failure mechanisms require separate consideration. Overall, this thesis showcases the promise of form-fitting connections
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    A Study of First-Order Primal-Dual Algorithms for Linear Optimization
    (University of Waterloo, 2026-09-16) Khan, Amaan
    interior point method large-scale linear programming quasi-Newton method first-order method low-rank update
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    HostFinder: Large-scale statistical detection of virus-host interactions across the Sequence Read Archive
    (University of Waterloo, 2026-09-16) Mascarenhas, Shyan
    The Sequence Read Archive (SRA) is the largest public sequencing repository and represents an unparalleled record of global biodiversity. Despite the success of prior studies in discovering novel viruses through the SRA, the development of reliable methods for inferring virus–host interactions remain an important unmet need. Here, we present HostFinder, a computational framework that infers virus-host associations at scale by analysing co-occurrence patterns across the entire SRA. HostFinder generates taxonomic profiles using STAT (SRA Taxonomic Analysis Tool), quantifies host and viral abundances per dataset, and calculates association strength through k-mer thresholds and log2-fold enrichment scores. Using a curated virus-eukaryote interaction dataset as well as VirusHost DB, we evaluated whether co-occurrence/enrichment scores could distinguish validated biological associations from false ones. Based on the enrichment score alone, the method achieved 0.93 ROC-AUC on the smaller 50 curated virus-host interaction dataset, whereas on the larger 8,508 Virus-HostDB dataset it achieved a 0.73 ROC-AUC with a 0.79 AUPRC. Scaling across the entire SRA, we analysed 3.78 billion virus-host pairs for potential interactions. Our analysis identified 7.8 M high-confidence interactions (log2-fold enrichment score > 3, FDR < 1%) involving 53,025 viral species and 15,143 host species. As a case study, we discovered novel Partitiviridae associations with multiple insect hosts that are unreported in existing literature. These predictions were independently validated through viral genome assembly and phylogenetic analysis. HostFinder demonstrates that large-scale co-occurrence analysis of public sequencing repositories can reveal the hidden structure of virus-host networks and accelerate discovery of novel viral associations.
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    Vertical Federated Learning as a Social Choice Problem
    (University of Waterloo, 2026-09-16) Pulyassary, Sreepriya
    How should a central server aggregate predictions from multiple agents, each holding partial or overlapping information, into a single collective decision — without exposing any individual agent's internal model or reasoning? Existing Federated Learning methods which satisfy our low-information requirements do not sufficiently address the online aspect of our setting. Online Learning supplies exactly this kind of guarantee, but its aggregation methods assume that some single agent is competitive on its own — an assumption that fails in the partial-feature setting Vertical Federated Learning requires. This thesis aims to close this gap by proposing methods for principled aggregation under partial feature coverage, framing this as a social choice problem. We propose a novel server strategy Trust-Based Perpetual Voting (TBPV) , which aggregates agent ballots through a scoring rule whose weights are learned online from the history of collective outcomes.