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Recent Submissions

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    Optical and Magnetic Control for Type-II MOTs
    (University of Waterloo, 2026-08-19) Jones, Max
    An ultracold mixture of cesium and lithium provides a clean and tunable platform for the study of highly correlated quantum systems. This thesis highlights the progress towards the design, construction, and optimization of our experimental apparatus for producing ultracold matter. I was able to amplify a narrow (∼100 kHz) laser to produce hundreds of mW of usable light using optical injection locking for both cesium-133 and lithium-6. I also developed a magnetic coil winding jig used to create the necessary magnetic coils for our experiments. We were able to achieve a type-II blue-detuned MOT of cesium, being one of the first in the world to do so. We quote axial and radial temperatures of 36(9) μK and 22(5) μK, respectively, with a phase space density (PSD) on the order of 10⁻⁷. We use our preliminary data as a stepping stone towards publication-quality measurements, allowing us to appropriately discuss our findings. The work provides a foundation for further cooling, trapping, and quantum simulation experiments in the future.
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    How Avoidance Shapes the Recall and Reporting of Shame, and Whether a Brief Intervention Can Mitigate Its Effects
    (University of Waterloo, 2026-08-19) Erb, Bailea
    Shame is a self-conscious emotion characterized by a global, negative evaluation of the self and a powerful action tendency toward concealment, avoidance, and withdrawal. This thesis tested the idea that these defining features of shame interfere with its own measurement, and examined whether a brief, approach-oriented intervention could reduce barriers to engaging with and reporting on shame experiences. In Studies 1a (N = 237) and 1b (N = 289), participants recalled and rated memories associated with shame and with comparison emotions (happiness, anger, sadness, anxiety, and, in Study 1b, guilt). Across both samples, participants rated shame memories as harder to recall, less clear and vivid. They also expressed lower confidence that shame was the emotion they had experienced and reported feeling shame less often than other emotions. These differences held relative to both positive and other negative emotions, although shame and guilt differed only in frequency. This suggests that difficulties with accessing and reporting emotional experiences may extend to moral, self-conscious emotions more broadly. Study 2 (N = 233) used a randomized experimental design to test a brief video-based intervention in which a therapist modelled the benefits of attending to shame and framed it as valuable and informative, compared to a validated mindfulness active control. Participants in the intervention condition wrote shame narratives with significantly greater experiential depth and drew on more recent shame memories, whereas self-reported ease of recall, clarity, confidence, and frequency did not differ significantly between conditions. Together, these findings suggest that shame’s avoidant action tendencies may influence the degree to which shame can be accurately captured using self-report measures. Additionally, a brief approach-oriented framing may support individuals’ engagement with their shame experiences, with implications for both measurement and clinical practice.
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    Efficient and Interpretable Natural Image Representation with Structured 2D Gaussians
    (University of Waterloo, 2026-08-19) Chowdhury, SoumyaDeep
    Recent advances in generative modeling, and the representation of high-fidelity visual content have motivated the demand for efficient, interpretable, and geometrically meaningful image representations. This work investigates the use of 2D Gaussian primitives as an explicit representation for images. The central research question explores whether image-derived Gaussian geometry and residual-driven refinement can improve reconstruction relative to random initializations, in low-medium resolution natural images. Unlike dense neural representations or JPEG, a Gaussian image representation describes image content with geometrically interpretable primitives with qualities of position, scale, orientation, and colour parameters. The ability to discern these qualities in the primitive grant physical meaning. This makes the Gaussian representation directly editable (an advantage over more established high-performance codecs), grants the ability to be rendered at any desired resolution, and can be viewed as a structured parameter space for images, while providing meaningful compression. The main contribution of this work is a structure-aware initialization and refinement pipeline for Gaussian primitives to ultimately convert images to Gaussian representations, referred to as GS-Sketch. While pioneering methods utilize random initializations, a drawing-inspired process is formulated to heuristically propose Gaussian locations, scales, and orientations using multiscale image cues in a coarse-to-fine approach. The drawing-inspired approach first captures coarse coverage, then intermediary shading, and lastly fine-detail and high-frequency areas. Residuals, edge maps, Laplacian, and coherence responses are all used in a discrete method to guide primitive placement. Geometric division is proposed between disk-like Gaussians in smooth regions, particularly targeting coverage, and pin-like Gaussians in edge-dense areas and fine details. Fully heuristically defining geometry allows the problem to become linear in Gaussian colour coefficients. Then, the coefficient fitting stage can be posed as a convex optimization problem which can be rapidly solved with an Alternating Direction Method of Multipliers (ADMM) formulation with pixel, gradient, Laplacian, inter-stage consistency, and reconstruction loss terms. This allows for edge fidelity, reconstruction fidelity and resource allocation to aid in later joint optimization. The initialization is treated with hole-patching algorithms to maintain coverage in sparse regions. During training a densification and pruning stage is proposed to reallocate Gaussians towards residual-driven high-error regions. A secondary contribution is an image-parameter codec that maps Gaussian tensors to attribute images that pack the position, scale, rotation, and colour qualities. This pipeline has slower post-training encode times than common learned entropy or codebook encoders; however, it completely avoids per-image post-fit quantization training, significantly reducing first-compress time, which is often relevant in practical applications. To enforce smoothness in the images, and therefore increased storage reduction in final image formats (PNG, and WebP), a sorting contest is conducted by the codec that measures bitrate reductions from reconstruction-safe sorts. In these regimes, WebP offers a lower bitrate, higher encoding cost path, while PNG offers a higher bitrate but lower time. Representation experiments against the GaussianImage benchmark show the structured initialization and refinement pipeline improves PSNR-related reconstruction terms across all tested Gaussian counts against the GaussianImage baseline. Across the Kodak dataset, the evaluated regimes are averaged to be 2.2k, 4.6k, 9.0k, and 12.1k Gaussians, with ranges. GS-Sketch outperforms the baseline in PSNR results with peak PSNR results improving from 27.47 to 28.09 dB, 29.56 to 30.81 dB, 32.04 to 33.49 dB, and 33.40 to 34.67 dB respectively for each budget regime. A sum variant of the pipeline is also proposed, which provides larger full pipeline speedup, and maintains higher PSNR results compared to the baseline at every regime. The overall pipeline with the sum-based variant produces speedups of approximately 6.8 times with PNG storage and 5.5 times with WebP storage. This speedup considers first-time compression of images, the crossing time for equivalent fit PSNR, and compares learned codebook quantization in the baseline and the proposed sorting contest in the proposed pipeline. This is compared against the baseline's final-fit PSNR time, and the subsequent learned compression stage. Low and medium bitrate regimes are designed for the image codec, paired with the final WebP format, denoted as WebP Low and WebP Medium. For GS-Sketch, WebP Low reaches 28.496 dB at 0.731 bpp, and WebP Medium reaches 30.115 dB at 0.800 bpp, compared with native GaussianImage quantization (applied on the GS-Sketch fits) at 26.811 dB and 0.653 bpp. The 30 dB operating point is competitive with the published GaussianImage rate-distortion curve which achieves comparable Kodak PSNR at approximately 1 bpp. Contextual results show the increased performance at these mid-range bpps. The results suggest that 2D Gaussian image representations provide a compact representation, while maintaining an interactive and geometrically interpretable state that can be used as image parameter representations for fast rendering and interactive editing applications.
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    Semantics-Behavior Coupled Bayesian Optimization for Efficient Black-Box Prompt Search
    (University of Waterloo, 2026-08-19) Wu, Ruotian
    Large language models (LLMs) are highly sensitive to prompt design, making prompt optimization important in black-box settings where models are accessible only through API calls. This thesis proposes DualBO, a semantics-behavior coupled Bayesian optimization framework for efficient prompt search. DualBO represents each prompt using two complementary views: a behavioral correctness vector computed on a controlled minibatch, and a semantic embedding of the prompt text. These views are combined in a dynamic Gaussian Process surrogate, where semantic similarity helps reduce uncertainty early in optimization and behavioral evidence provides stronger task alignment as more prompts are evaluated. DualBO also introduces staged strategy-oriented candidate generation, which first generates diverse rewriting strategies and then instantiates them into concrete prompt candidates. This improves search-space coverage compared with direct local rewriting. Experiments on ETHOS, ARC, MMLU-Pro, and HotpotQA using GPT-5.4-mini and DeepSeek-V3.2 show that DualBO consistently improves over semantic-only and behavior-only Bayesian optimization baselines. It is also competitive with reflection-based methods such as Reflexion and ProTeGi while requiring substantially fewer model calls. Overall, this thesis demonstrates that combining semantic smoothness, behavior-grounded task alignment, and diversity-aware candidate generation improves the stability and efficiency of black-box prompt optimization under limited evaluation budgets.
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    Physics-Informed Dynamic 3D Reconstruction: From Gaussian Splatting and Rigid-Body Simulation to Inverse Material Estimation
    (University of Waterloo, 2026-08-19) Ramlal, Adrian
    3D Gaussian Splatting (3DGS) is a leading representation for photorealistic reconstruction and novel view synthesis, with explicit, real-time scene models. Three limitations constrain the standard formulation: it depends on sparse Structure-from-Motion point clouds for initialization, is restricted to static scenes, and is disconnected from the physics simulators that would consume its output. Reviewing dynamic 3DGS methods shows reliance on hand-designed temporal regularizers rather than physical laws, so their motion is plausible but not physically produced. Across four studies of increasing complexity, this thesis brings physical reasoning to the pipeline from 3DGS reconstruction to physics simulation. We first evaluate five point cloud upsampling strategies and a depth-guided point lifting method for static 3DGS initialization. On Mip-NeRF360 and Replica, geometry-aware upsampling consistently improves reconstruction quality, with average best-per-scene PSNR gains of 0.26 dB and 0.65 dB respectively, and yields scene-characteristic selection guidelines. Next we ask whether dynamic reconstructions can be made physics-compatible after the fact. A dual-representation system pairing fixed-topology meshes for collision detection with Gaussians for rendering attains a 4.65× simulation speedup, but converting varying-topology reconstructions to fixed topology incurs 65 to 80% geometric degradation. This negative result means physics compatibility cannot be retrofitted by post-processing and must be expressed in the reconstruction objective. Acting on this finding, PersistGS couples differentiable rigid-body simulation with 4D Gaussian Splatting to maintain object permanence through occlusion. Estimating friction and initial velocity from visible frames and positioning Gaussians along physics-predicted SE(3) trajectories, it achieves +2.46 dB PSNR over the best causal kinematic baseline and comes within 0.19 dB of the ground-truth upper bound. At the continuum, non-differentiable end, the contribution is inverse material estimation. We recover the material parameters of the AnisoMPM food-fracture simulator, whose Lagrangian particle field parallels the 3DGS primitive. A goal-conditioned reinforcement learning policy in a normalizing-flow latent space amortizes estimation across arbitrary targets, achieving 0.642 simulator-validated recovery in a single pass, while a warm-start hybrid reaches the highest overall recovery, 0.828. Together, the studies move reconstruction from photometric fidelity alone toward joint fidelity and physical consistency, and finally beyond 3DGS to continuum-fracture material parameters.