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Recent Submissions

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    Development of a Coupled Hydro-Economic Model to Support Groundwater Irrigation Decisions
    (University of Waterloo, 2026-03-12) Tian, Boyao
    This research develops an integrated hydro-economic modeling framework to support farm-level irrigation decision-making under hydrologic, economic, and climatic uncertainty. The model couples groundwater dynamics, including analytical representations of groundwater-surface water interactions, with crop yield response, and economic valuation to assess trade-offs between agricultural profitability, water use, and long-term sustainability. Conditional Value-at-Risk (CVaR) is incorporated to evaluate downside risk and capture extreme events often overlooked by traditional risk assessment methods. The framework is applied to two contrasting agricultural systems: the High Plains Aquifer (U.S.) and the Saskatchewan River Basin (Canada), representing unconfined and confined aquifers under differing climatic and hydrologic conditions. The results demonstrate that moderate water use strategies often achieve the best balance between profitability and groundwater sustainability, while excessive pumping leads to significant streamflow depletion and reduced long-term benefits. Multi-objective optimization using NSGA-II identifies Pareto-efficient solutions that balance land value, water depth, and streamflow impacts. The model’s simplicity and adaptability make it accessible to farmers, policymakers, and regulators, providing a practical decision-support tool without requiring intensive data or computational resources. Overall, this research contributes to advancing hydro-economic modeling, integrating risk assessment, and promoting sustainable groundwater irrigation management under increasing climate and market variability.
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    Exploring and Visualizing Fact-Based Software Models to Improve Program Comprehension
    (University of Waterloo, 2026-03-12) Ferreira Toledo, Rafael
    Software engineers dedicate significant time and effort to debugging, analyzing, and understanding large, complex software. Such systems can comprise millions of lines of code that implement the program behaviour. When working on such maintenance tasks, the engineer needs to examine the code involved to understand exactly how the program's behaviour is implemented before they can perform any changes or fixes. Depending on the complexity of the program behaviour, the engineer must navigate dozens of lines of code scattered across multiple files to comprehend a single instance of the analysis results. During this code navigation, they pose program comprehension questions that guide the building of a mental model of the program's behaviour. It is well known that answering such queries can be time-consuming, error-prone and cognitively demanding. These risks and demands increase with the complexity of the software under study, for example, when analyzing software that is a software product line (SPL), where an SPL represents a family of related software product variants (e.g., different models of cellphones or vehicles sold by the same company). Many of the above complexities can be addressed by working with a model of code because models are abstractions that are generally smaller, simpler, and more amenable to automated analyses. A software fact-based model is a collection of program facts that reflect the properties and behaviour of a software system. Program facts include source-code entities (e.g., variables, functions), their attributes (e.g., names, source file), and their relationships (e.g., function calls, class inheritance). Program facts can be automatically extracted from source code with an enhanced parser, and the facts can be linked together into a fact-based model of the software system. The resulting collection of software facts represents the system's properties and behaviour as a graphical model that can be managed and queried using graph database technologies. Graph database systems and their native features enable efficient and optimal storage, querying, and visualization of the software fact-based model. Software queries and analyses can be expressed using the database's query language. However, writing common queries from scratch can be repetitive and time-consuming, and, for large and complex queries, it can be error-prone. This thesis investigates whether fact-based software modelling and analysis can improve program comprehension of software systems, including variable systems. This thesis makes three contributions: (1) identifying the program-comprehension questions that software fact-based models can support, (2) designing a query interface that facilitates program-comprehension questions and supports incremental exploration of query results, and (3) developing an efficient visual encoding of results of queries on an SPL model. We evaluated how well fact-based models can answer program-comprehension questions. Previous studies categorized program comprehension questions, but primarily focused on code-based questions rather than model-based questions. We performed a literature review to identify program-comprehension questions that can be posed to fact-based models. We correlated engineers' information needs with the information that fact-based models supply through a comprehensive analysis of previous works on program comprehension questions and graph visualization. Finally, we demonstrated that 38 program comprehension questions could be answered by a fact-based model by expressing them as Cypher queries over a Neo4j factbase. Secondly, we studied how to improve the engineer's experience in understanding program facts through program-comprehension query templates and follow-up queries. We extended Neo4j Browser to support initial program-comprehension queries and follow-up queries over fact-based model elements, giving users greater control and precision in their exploration of the model. We conducted a user study comparing the use of our enhanced Neo4j Browser with a standard code editor, and it shows significant gains in users' efficiency and reduced mental effort during program-comprehension tasks. Finally, we studied how to improve an engineer's comprehension of variable results from a fact-based analysis of an SPL. Analyzing an SPL model produces variable results, where each result may apply to some product variants and not others (e.g. if the analysis refers to feature-specific code). Variable analysis results are typically represented by annotating each result with a presence condition (PC), where the PC is a propositional formula that represents the product(s) for which the result holds. Thus, interpreting the variable analysis results of an SPL model involves determining the program variant (or group of variants) that applies to specific results, which can be error-prone and cognitively demanding. We developed ^Neo4j Browser, a modified version of Neo4j that provides features for filtering analysis results based on the feature configuration of SPL variants and highlighting the results associated with each filter. ^Neo4j Browser helps users to interpret variable results faster, more accurately, and with less mental effort.
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    Semantic-Aware Active Perception for Next-Best-View Grasp Planning
    (2026) Kweon, Tae Hyeon; Jeon, Soo
    Robotic grasping is a cornerstone of manufacturing automation, and recent advances in deep learning have brought data-driven adaptability to vision-based grasping. However, achieving human-like performance in cluttered environments requires additional capabilities, such as correctly perceiving the object to be retrieved and efficiently planning viewpoints to reconstruct the target object for better grasping under heavy occlusion. To address these challenges, we propose a semantic-aware Next-Best-View (NBV) planning framework that integrates geometric and semantic information gains for targeted exploration. The proposed method maintains a semantic–geometric voxel representation that incrementally accumulates semantic detections across views, guiding viewpoint selection toward regions most likely to reveal graspable target surfaces. We evaluate the framework in simulation and real-world experiments using a Franka Emika Panda arm under heavy occlusion. The proposed approach achieves an 84% success rate in simulation and 10/10 successful grasps in real-world experiments, outperforming baselines in simulation while matching the real-world performance of a geometric NBV method despite requiring no prior knowledge of object locations.
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    Scaling Two-Party Differentially Private Selection
    (University of Waterloo, 2026-03-12) Ni, Haoyan
    We consider the problem of differentially private (DP) selection in the two-party setting. This problem can be solved with excellent utility guarantee in the central setting, but the distributed case is much less studied. Existing solutions use secure multi-party computation (MPC) techniques to simulate computation in the central model, which are not sufficiently scalable to large candidate sets. This work provides a new protocol for two-party DP selection that achieves sublinear runtime in the MPC phase. Our design lets each party locally trim the candidate set before participating in an MPC protocol. Based on this heuristic, we provide two variations, one of which reveals each party’s trimmed candidate set and the other does not. We evaluate our method on public datasets based on review counts and location check-ins. The results demonstrate that the variant hiding the trimmed candidate sets outperforms the other variant in both utility and efficiency. Furthermore, our solution is able to offer competitive utility to the traditional solution at a significantly lower computation cost in lower privacy regimes.
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    Developments in Photon Absorption Remote Sensing Microscopy and Deep Learning–Based Virtual Histochemical Staining
    (University of Waterloo, 2026-03-11) Tweel, James
    Histological staining remains the gold standard of diagnostic pathology, enabling visualization of tissue structure and cellular morphology. However, traditional staining workflows are time-consuming, destructive, and chemically intensive, limiting the number of stains that can be applied to valuable biopsy samples. These processes also introduce delays, variability in stain quality, and high resource demands. To address these limitations, this thesis presents a label-free histology framework that combines Photon Absorption Remote Sensing (PARS) microscopy with deep learning–based virtual staining to replicate commonly used histochemical stains without altering or consuming the tissue. The first component of this work focuses on the development of an automated whole slide PARS system designed for imaging thin, transmissible tissue sections. The system captures sub-micron resolution radiative and non-radiative absorption contrasts using 266 nm UV excitation, targeting endogenous chromophores such as DNA and extracellular matrix components to reveal nuclear and connective tissue structures. Whole slide imaging is achieved through automated focusing, tiling, and contrast leveling, producing gigapixel-scale images directly comparable to standard hematoxylin and eosin (H&E) slides. The second component introduces a deep learning virtual staining pipeline based on the unpaired CycleGAN architecture, with direct comparison to the paired Pix2Pix model. These models are trained on one-to-one whole slide images of PARS data and chemically stained H&E slides. The first masked clinical concordance study is conducted using breast needle core biopsies, where board-certified pathologists independently diagnose and assess the virtual and real H&E slides. The study demonstrates substantial diagnostic agreement, validating the clinical viability of the PARS-based virtual staining approach. The final component expands the PARS imaging system through the integration of a secondary long-wave UV excitation wavelength (355 nm), enabling sensitivity to additional biomolecular absorbers and thereby expanding the captured label-free contrasts. The additional label-free contrast contributes to improved emulation of histochemical stains beyond H&E, including Masson’s Trichrome, Periodic acid–Schiff, and Jones methenamine silver. To further improve performance, a more advanced registration-guided GAN model (RegGAN) is adopted, outperforming both Pix2Pix and CycleGAN. The resulting whole slide virtual images closely match their ground truth counterparts in qualitative appearance, quantitative metrics, and masked pathology review. Together, this work presents a non destructive histology pipeline capable of generating high-resolution, multi-stain images of commonly used stains without chemical labeling, representing a step toward integrating label-free microscopy and deep learning virtual staining into routine pathology workflows.