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    Galaxy evolution across the Virgo cluster environment
    (University of Waterloo, 2026-08-07) Morgan, Cameron Robert
    In this thesis, I present three studies of the properties of galaxies in the Virgo cluster with the goal of providing new insights into how we can measure the effects of the cluster environment on these member galaxies. Throughout this work, I move from analyzing integrated quantities of galaxies toward studying galaxy star formation on a spatially-resolved scale. First, I analyze deep stellar mass functions of galaxies in the Virgo cluster. I split these stellar mass functions according to star formation rate and region within the cluster. I find that the quenched fractions remain high across the cluster, including in the infall region, and especially at low stellar masses. Additionally, the shapes of the mass functions are mostly independent of position within the cluster. Using a simple model of infalling and backsplash galaxy populations, I show that the quenched fractions seen in the cluster outskirts require unrealistically high backsplash fractions, implying that galaxies are being pre-processed outside of the main cluster and are often already quenched upon first infall. The second section of this thesis focuses on quantifying the edges of star-forming disks as a means to analyze truncation and outside-in quenching. I develop a novel method for measuring disk truncation that takes advantage of the depth and spatially-resolved nature of the VESTIGE Hα data. This method involves constructing radial sSFR profiles and identifying the turn-off point where star formation drops off. I compare this edge of the star-forming disk with a measure of the expected size based on normal star-forming galaxies, which gives a measure of the degree of truncation. Ultimately, I find that moderately-severely truncated disks are ubiquitous across the cluster environment, and show only mild correlations with parameters such as stellar mass, distance from the SFMS, and HI-deficiency. I invoke toy models of RPS and starvation to show the effects that each of these mechanisms has on disk truncation. While RPS can rapidly truncate the star-forming disk, it is only particularly effective toward the cluster centre. Starvation can slowly truncate disks much earlier by cutting off a galaxy's supply of fresh gas -- as the gas density decreases, the outer part of the disk falls below the threshold necessary to sustain star formation first, causing a slow outside-in quenching. This again highlights the need for pre-processing to explain quenching in the Virgo environment, and indicates a ``slow-then-rapid'' quenching sequence. Finally, I take advantage of the spatially-resolved and multi-wavelength data available for Virgo cluster galaxies to map signatures of quenching on more local scales. I do this by empirically calculating SFRs based on both Hα and FUV fluxes, thus giving me SFRs on both ~10 and ~100 Myr timescales. Comparing the two helps to identify regions where rapid quenching is occurring. I perform spatially resolved SED fitting on a sample of Virgo galaxies using CIGALE which allows me to map out galaxy parameters. Combining the SED fitting results with emission line ratios from MaNGA, I derive corrections based on dust, NII contamination (in the Hα) and pAGB contribution to the FUV before converting the respective fluxes to SFRs. Taking the ratio of SFRha and SFRfuv allows me to visually identify interesting features in these galaxies. I identify two key features: lopsidedness in the distribution of SFRha/SFRfuv, and outer disk truncation. I quantify these metrics, and find that while a few galaxies that have strong gas asymmetries and have been identified as RPS candidates have a high degree of lopsidedness, there are no strong correlations with other galaxy parameters. I discuss the possibility that these metrics are unique signatures of processes like RPS at certain stages, though drawing strong conclusions about their nature proves difficult. I end off with future perspectives, based on my own work with the CASTOR and GIRMOS science teams, the broader context of new instrumentation and analysis tools, and my own goals for the coming years as I seek to continue work into the study of galaxy evolution. I look forward to pursuing studies with new data, exploring new redshift ranges and galaxy environments, and looking to continue developing novel techniques to help make inferences about galaxies in the broad context of understanding our Universe.
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    Drawing benefits memory and comprehension by establishing and strengthening the link between verbal and visual percepts
    (University of Waterloo, 2026-08-07) Tran, Sophia
    Researchers have long been interested in identifying ways to enhance both the quantity and quality of human memory, and the mechanisms underlying their benefit. The use of drawing as an encoding technique has been shown to reliably improve memory for lists of words, definitions of terms, and autobiographical events. What remains unexplored is how the magnitude of the benefit from drawing compares to that from other encoding techniques, and its mechanism of action. In Experiment 1, I showed that drawing, writing, and reading aloud as encoding techniques yielded better memory than silently reading words, with drawing leading to the largest boost. Importantly, age differences in memory emerged only when drawing was used as the encoding strategy, in line with previously reported age-related deficits in generating imagery, or integrating it with motoric processes. Despite this, concrete relative to abstract words that were drawn during encoding were better retained, demonstrating a stable benefit of visuo-spatial representation, regardless of age. In Experiment 2, I assessed the extent to which personally generating one’s own production was critical to the observed memory benefit. Replicating Experiment 1, drawing at encoding benefitted memory more than writing or reading words. This occurred regardless of whether it was performed or observed in-person or online. In line with common-coding-theory this finding suggests that perceiving an action activates the same motoric processes in the brain as performing it. However, the magnitude of the drawing benefit was greatest when performed rather than observed, suggesting an additional role of personal relevance in modulating the drawing benefit. In Experiment 3, I examined whether repetition by tracing one’s initial drawing, or generating multiple novel related ones, differentially benefitted memory. The former enhanced free recall of target words more than the latter. This suggests that establishing a strong visual percept underlies drawing’s benefit to memory. Finally, in Experiment 4, I assessed the utility of drawing as a cognitive tool in educational contexts. Drawing and paraphrasing of definitions of novel academic terms, during encoding, resulted in better performance on a multiple-choice test of concept comprehension, relative to silently reading. Results suggest that techniques that invoke re-representation of the to-be-remembered text into another format, be it into a picture (drawing) or personally relevant summary (paraphrasing), are particularly effective at improving cognitive task performance. The experiments in this dissertation suggest that a critical component of how drawing improves memory and comprehension is the linking of verbal information to concrete visual referents, which results in more easily retrievable representations.
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    Bidirectional insights between classical and quantum causal inference
    (University of Waterloo, 2026-08-07) Maciel Ansanelli, Marina
    Causal inference is a sub-area of statistics that investigates the causal explanations underlying observed data. The last decade has seen growing recognition of its applications to quantum physics, which include the use of causal inference to certify quantumness through violations of Bell-like inequalities. This connection places quantum physicists in a unique position to contribute to classical causal inference by addressing open problems whose solutions benefit both fields. This thesis highlights this bidirectional interplay of fields, presenting both the study of foundational problems in classical causal inference from the perspective of quantum physics and the application of these results to quantum research. An important problem in causal inference is attesting when two causal structures are in principle indistinguishable from data obtained under a given probing scheme on the visible variables. For example, passive observations on two variables cannot distinguish between a direct causal relation and a shared latent common cause. However, they {\em can} be distinguished by interventional probing schemes. The first question we address here is which causal structures remain indistinguishable even under the most informative interventional probing schemes. We derive a necessary and sufficient condition for such indistinguishability. We then consider the same question under access to passive observations only, compiling all known sufficient rules for indistinguishability and applying them to causal structures with three and four visible nodes, though a complete characterization remains open. Then, we extend these results to scenarios involving selection bias. The works described above also help identify which causal structures impose nontrivial inequality constraints on their classically realizable distributions; an example is the Bell causal structure, that imposes Bell inequalities. This has particular interest for quantum physicists, because inequality constraints are a prerequisite for a causal structure to have a quantum-classical gap (QC gap), that is, for it to explain more distributions when its latent nodes are associated with quantum systems than when its latent nodes are associated with classical variables. This is important because, when we know that a phenomenon is governed by a causal structure that presents a QC gap, observing a probability distribution over the observed variables that cannot be explained using classical latent variables in that structure certifies that the phenomenon involves genuine nonclassicality. Thus, after identifying which causal structures could potentially exhibit a QC gap, we investigate which actually do. Using techniques developed here, we show that hundreds of new causal structures have a QC gap, while previously only around a dozen examples were known.
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    Dimensionality Testing, Shape-Constrained Neural Network Estimation, and Equilibrium Approximation in Team Games
    (University of Waterloo, 2026-08-07) Zhang, Zetian
    Modern advancements in computational technology have catalyzed an explosion in data dimensionality, enabling researchers to model intricate relationships among multitudinous variables simultaneously; however, these advancements also challenge the application of many useful classical theories (for instance, the $F$-test which is one of the most widely used tests fails with increasing dimensions alongside sample growth). Although some modifications of classical theories, such as the corrected $F$-test, have been proposed to mitigate the high-dimensional bias, a more fundamental question about whether the dimension is ``high" has not been addressed. In Chapter 1, we tackle this problem by proposing a test statistic, which is asymptotically normal, to distinguish high from non-high dimensionality for vectors. In large sample sizes, our test has the correct size when the dimension is ``high"; and it is asymptotically powerful against all alternatives with ``low" dimensions. A set of simulation studies demonstrates that our test achieves satisfactory size and power even in small to moderate sample sizes. Deep neural networks (DNNs), as the most modern advancement, have demonstrated exceptional performance across a wide range of domains, such as image classification \citep{NIPS2012_c399862d,he2016deep}, speech recognition \citep{hinton2012deep}, natural language processing \citep{mikolov2013efficient,vaswani2017attention} and game intelligence \citep{silver2016mastering,perolat2022mastering}. DNNs can be conceptualized as a generalization of classical sieve estimators, transitioning from ``single-layer" to ``multi-layer" architectures. This architectural evolution substantially expands the model's parameter space, but diminishing the interpretability of parameters and the model itself. In Chapter 2, we provide the consistency and asymptotic normality of the DNN-based estimator for point estimation and extend these properties the case with prior knowledge of shapes. The incorporation of prior knowledge into neural networks to enhance model performance has evolved into a fundamental methodology, being widely used in real-world applications. The abstract prior knowledge, particularly the shape restrictions such as monotonicity, convexity, and homogeneity, hold critical importance within economic modeling frameworks, where functional specifications like production and utility functions inherently require certain shape restrictions to ensure both mathematical and theoretical validity in economic analyses. However, current methods exhibit fundamental limitations in effectively embedding abstract knowledge, since those methods either implement loss function modifications that fail to guarantee the desired shape, or require specifically designed structures through restricting the number or type of layers to certain forms--a parametric imposition that essentially limits model expressiveness. Our shape-restricted approximator is shown to be consistent and asymptotically normal under mild regularity conditions. Based on case studies, the proposed method outperforms the existing shape-restricted networks. Two-team zero-sum games model a broad range of real-world competitive interactions more effectively than traditional two-player games, yet they remain relatively underexplored. Existing successful approaches for two-player games, including Go, poker, and Stratego, are difficult to extend directly to team settings, as they cannot simultaneously account for inter-team competition and intra-team cooperation. In chapter 3, we investigate two-team zero-sum games under the setting where teammates cannot communicate, with the goal of approximating the team Nash equilibrium. Unlike existing methods such as TMECor \citep{TMECor_Team-PSRO} and GFXP \citep{FXP_team_comp}, which rely on communication or correlation devices by treating each team as a centralized joint player, our method directly tackles decentralized team settings without such coordination mechanisms. We propose a novel extension of Policy-Space Response Oracles (PSRO) \citep{PSRO} that incorporates multi-agent reinforcement learning (MARL) to compute joint cooperative best responses during population expansion. By leveraging shared team rewards, the proposed method captures cooperation within teams while preserving competitive optimization across teams. The proposed algorithm demonstrates a stronger tendency to converge to global Nash equilibria compared with classical PSRO based on some experimental results.
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    Development and Evaluation of Assistive AI Systems for Assessing News Trustworthiness
    (University of Waterloo, 2026-08-04) Zhang, Dake
    Online news shapes how people form opinions on topics such as science, health, and politics, yet the same environment that makes it widely accessible also enables low-quality and deceptive content to propagate at scale. This thesis studies how AI systems can help readers assess the trustworthiness of online news. Rather than predicting whether an article is true or false, we treat trustworthiness assessment as a process to be supported: a useful system should help readers ask the investigative questions that a careful reader would ask, and synthesize the external evidence and context needed to answer them. This framing is grounded in lateral reading, the strategy professional fact-checkers use when evaluating online information by searching beyond the page itself. We pursue system development and evaluation together because the two are inseparable in a new research area. This research program began with ReadProbe, a proof-of-concept retrieval-augmented LLM system that generated investigative questions, retrieved web evidence, and produced attributed answers. A subsequent pilot study, originally intended as a formative step toward a larger human-baseline study of question generation, characterized the kinds of questions university-affiliated readers wrote before and after brief lateral-reading guidance, and revealed useful design lessons. These lessons motivated the TREC 2024 Lateral Reading Track, the first shared benchmark in this area, which formalized question generation and document retrieval as foundational tasks. Results showed that question generation remains challenging even for frontier LLMs, while document retrieval is comparatively mature. A follow-up analysis further revealed that LLM-generated question lists are less diverse than those written by human experts and overlap little with them, suggesting that alignment with expert investigative priorities is the core open problem. These observations led to the TREC 2025 DRAGUN Track, which shifted the benchmark toward a more reader-oriented setting by introducing report generation as the main task and replacing direct grading with expert-authored, importance-weighted rubrics built through open-web research. To support the track, we developed an iterative multi-agent RAG system that simulates a lateral reader by interleaving query generation, multi-stage segment retrieval, information sufficiency evaluation, question generation, and report writing, which served as a strong starter-kit baseline. Finally, to make rubric-based evaluation reusable beyond the originally judged runs, we released an LLM-based AutoJudge system that mirrors the human assessment protocol and preserves run-level rankings well against the official human judgments. Through this research journey, three key findings stand out. First, generating investigative questions that align with expert priorities remains hard: even the strongest system in the DRAGUN track covered only about one-third of the importance-weighted rubric question space on average. Second, given a useful question, document retrieval is no longer the central bottleneck. The harder problems now sit at the planning and synthesis ends of the pipeline. Third, evaluation matters as much as system design, and rubric-based evaluation grounded in expert open-web research is a more useful and interpretable target than direct grading of system outputs. Overall, this thesis contributes a reader-centered framework for assistive AI in news trustworthiness assessment, along with shared tasks, datasets, systems, and a reusable evaluator that together support continued progress toward AI tools that help readers think more carefully about online news rather than think for them.