UWSpace

UWSpace is the University of Waterloo’s institutional repository for the free, secure, and long-term home of research produced by faculty, students, and staff.

Depositing Theses/Dissertations or Research to UWSpace

Are you a Graduate Student depositing your thesis to UWSpace? See our Thesis Deposit Help and UWSpace Thesis FAQ pages to learn more.

Are you a Faculty or Staff member depositing research to UWSpace? See our Waterloo Research Deposit Help and Self-Archiving pages to learn more.

Photo by Waterloo staff

Communities in UWSpace

Select a community to browse its collections.

Now showing 1 - 13 of 13

Arts (Faculty of)
Conrad Grebel University College
Engineering (Faculty of)
Environment (Faculty of)
Health (Faculty of)
Mathematics (Faculty of)
Renison University College
Science (Faculty of)
St. Jerome's University
St. Paul's University College
Stratford School of Interaction Design and Business
University of Waterloo
The University of Waterloo institution-wide UWSpace community.
Waterloo Library

Recent Submissions

Towards Foundation Models for Text-Rich Multimodal Tabular Data
(University of Waterloo, 2026-08-19) Loh, Wei Min
Tabular data has been a central data format in statistics for centuries. In the age of machine learning and artificial intelligence, our set of tools has increased in size yet, many areas related to tabular modalities and tasks remain underexplored. Input representation is an important consideration for improving the current state of tabular approaches. Many successful approaches in purely numeric tabular data do not place much emphasis on input representations, and that may be limiting them to the purely numeric regime. On the other hand, the topic of information transfer is also one that practitioners need to address in the real world, especially when facing limited training data and evolving attributes. Information transfer can be in the form of continuously updating a model from a stream of data, or in the form of leveraging common information from a set of related tasks. This dissertation investigates these two areas, aiming to advance the current state of practical tabular models. The issue of the continual adaptation of a tabular model is addressed by framing it as a multi-armed bandit. The clear advantage of this formulation is that the solution not only adapts to evolving needs at inference time but also has the ability to influence which data points to collect next. In this dissertation, we propose a framework based on Nadaraya-Watson kernel regression and Thompson sampling to continuously improve the existing model without updating model weight at inference time. This framework includes a provable guarantee on the upper bound on error using finite-sample analysis. Empirically, we demonstrated improvements on both the standard benchmark for multi-armed bandits and a newly proposed benchmark for realistic news recommendation tasks. On the question of input representations, we started with the investigation of the current state of tabular models and identified important desiderata when working with tabular data. To the best of our knowledge, there are very few approaches in the current literature that satisfy the desiderata. We propose a transformer-based architecture, called basis transformers from the ground up, and introduce specialized components suited to the idiosyncrasies of tabular data. This architecture was evaluated on multi-task and related task regression experiments, and demonstrated improvements over gradient boosted decision trees, finetuned large language models, and similar deep tabular models. Scaling in terms of the amount of data and the number of learnable parameters is the predominant technique for improving learned representations and performance in contemporary machine learning. In the field of tabular models, scaling is particularly challenging due to the heterogeneous formats and data types, and relatively few publicly available sources of tabular datasets because many tables contain proprietary and sensitive information. With extensive engineering and data processing efforts, we developed a multimodal tabular foundation model, capable of extracting holistic representations and taking column names into account. The experiments demonstrated two main outcomes: the ability to perform zero-shot inference, which remains limited in tabular domains, and improvements over foundation model baselines on text-rich tabular datasets.
A pig as small as a cup? Children’s beliefs about improbable natural items
(University of Waterloo, 2026-08-19) Pabla, Mopreet
Children often say that completely possible improbable events are impossible, and only gradually begin to affirm their possibility. For instance, children often deny that someone could do unusual things, like eat pickle-flavoured ice cream or have a pet peacock. These denials are surprising since we often think of children as fantastical thinkers. Most previous possibility research has been human-centered, focusing on people and what they can do. Our concern was that this human influence may be restricting what we know about children’s possibility judgements, given their knowledge about human social norms and people’s wants and desires. In this experiment (N=160), we attempt to move away from human-centered possibility judgements and instead ask children about the possibility of natural items. Children aged 4-7 judged whether ordinary, improbable, and impossible natural events could or have ever happened. We found that while children affirm the possibility of ordinary events and deny the possibility of impossible events, they also continue to deny the possibility of improbable events. When comparing children’s results to those of adults (N=197), we find that children are significantly less likely than adults to affirm the possibility of improbable events. Taken together, these results show that children’s denials of improbable events can be taken at face value. Whether asked about human-centered or natural events, children continue to deny the possibility of improbable events.
Model Uncertainty with Applications in Finance and Insurance
(University of Waterloo, 2026-08-19) Song, Zhiqiao
This thesis aims to develop rigorous and practically relevant optimization models for risk management in finance and insurance, with particular attention to robust portfolio selection under distributional model uncertainty and optimal reinsurance design. First, in Chapter 1 we introduce the background and motivations for the research questions and models studied in this thesis, provide the preliminaries necessary for understanding and analyzing them, and describe the overall structure of the thesis. In Chapter 2, we propose a reward-penalty mechanism and incorporate it into portfolio management. We consider a robust portfolio selection problem, where the joint distribution of the underlying asset losses is assumed to be uncertain and belongs to a prescribed multivariate distribution set. The objective is to determine optimal portfolios by minimizing the worst-case conditional value-at-risk (CVaR) of the portfolio loss under both distributional uncertainty and the reward-penalty mechanism. We first derive a closed-form expression for the worst-case CVaR, which generalizes several existing results, including those of Jagannathan(1977), Chen et. al (2011), and Cai et. al (2024). Then apply this expression to obtain optimal portfolios under a classical mean-covariance-based distribution set and a generalized mean-covariance-based distribution set. Empirical studies based on real market data show that the proposed models can outperform several related portfolio strategies. The results also demonstrate that incorporating downside risk into portfolio loss can improve risk management and investment performance, while revealing the trade-off between enhancing expected portfolio returns and controlling worst-case portfolio CVaR. In Chapter 3, we study robust enhanced index tracking portfolio selection models under distributional uncertainty. The objective is to construct portfolios that can outperform a benchmark index while controlling the risk of underperformance. Since the joint distribution of asset and index losses is typically unknown in practice, we consider robust models based on partial distributional information. Guided by Pareto optimality, the proposed models balance worst-case mean loss and downside risk, subject to a constraint on worst-case mean return. Two types of uncertainty sets are considered. For the uncertainty set with a known mean vector and covariance matrix, closed-form optimal solutions are derived. For a more general mean-covariance-based uncertainty set, the problem is reformulated as a tractable convex optimization problem. Empirical results based on real market data show that the proposed models outperform the tracked index, the equally weighted portfolio, and several robust benchmark models in terms of cumulative wealth and risk-adjusted performance. The results also show that balancing mean loss and downside risk can lead to better portfolio performance than minimizing downside risk alone. In Chapter 4, we introduce a performance-based premium principle for optimal reinsurance design. Under this premium principle, the reinsurance premium is adjusted according to the realized ceded loss relative to a baseline premium: the insurer receives a reward when the realized ceded loss is below the baseline level, and pays an additional premium when it exceeds the baseline level. Based on this pricing mechanism, we first study optimal reinsurance problems from the insurer’s perspective and derive the optimal retention levels for quota-share and stop-loss contracts under VaR and TVaR minimization. We then consider the joint perspective of the insurer and the reinsurer by maximizing their joint survival probability and obtain the corresponding optimal quota-share and stop-loss retentions. More generally, we investigate optimal reinsurance design under a broad class of reinsurance contracts and a general premium principle for determining the baseline premium. Numerical examples are provided to illustrate how the performance-based premium principle affects the optimal limited quota-share and limited stop-loss retentions. Finally, in Chapter 5, we conclude the thesis by summarizing the key findings and discussing potential directions for future research and development that build upon the models and results presented in this thesis.
Repeat After Me: The Effects of Exclusion on Children’s Language Behavior
(University of Waterloo, 2026-08-19) Asgharizadeh, Salva
Childhood is a unique period of development, characterized by rapid changes in language behavior, especially once children begin attending school and interacting with peers. Some of these changes may be driven by socio-affiliative motivations, including instances of anticipated or felt ostracism. Across two experiments, we examined whether ostracism would affect children’s 1) lexical alignment rates and 2) learning of novel words. In Experiment 1, we did not find significant effects of ostracism on alignment but observed some differences as a function of language background. In Experiment 2, analyses on the preliminary sample also revealed no significant difference as a function of ostracism. This line of work has the potential to provide insight into factors motivating children’s linguistic behaviors in different social contexts.
Perception Stack Design and Fault-Aware Sensing for the EcoCAR Competition
(University of Waterloo, 2026-08-17) Yousefi, Zahra
A major challenge in the development of autonomous driving systems is ensuring that the perception layer reliably interprets and manages sensor data under real-world operating conditions. In competition and research contexts where sensors are integrated onto production vehicles, this challenge is compounded by the vehicle's existing architecture where sensors expose processed detections, rather than raw data, over proprietary CAN buses, shifting the design focus toward robust data ingestion, active health monitoring, and failure management. This thesis presents the design and implementation of a perception stack for the University of Waterloo Alternative Fuels Team competition vehicle, a 2023 Cadillac LYRIQ, developed as part of the EcoCAR Electric Vehicle Challenge. The system operates on the dSPACE AUTERA platform using RTMaps middleware and implements a modular, per-sensor pipeline comprising CAN burst detection and processing, loss-of-communication monitoring, frame decoding, sensor health checking, and data processing. A fail-safe module was designed and implemented as a custom RTMaps Python bridge component using a fault-streak counting strategy that distinguishes sustained fault conditions from transient anomalies before issuing a disable command to the planning module so that features dependent on a compromised sensor are deactivated to ensure safety. The system was validated through the XIL testing framework, progressing from synthetic CAN frame generation and baseline vehicle evaluation data replay through hardware-in-the-loop testing on the AUTERA, demonstrating that a modular, fault-aware perception pipeline can be designed and implemented on a production vehicle platform under competition constraints.