Rendering volumes using path tracing can produce stunning images, but the process is notoriously expensive. For some volumes such as clouds, there are cases where the volume has no interaction with other objects in the scene. For these cases, combined with an isotropic phase function, unidirectional path tracing from the camera computes many similar paths regardless of camera position. Computing these light paths beforehand to determine the full radiance transport through the volume speeds up the rendering stage drastically, with the result still being physically accurate.

In this thesis, we present a two-stage method designed for the aforementioned cases. The first stage is a precomputation stage, in which light paths are traced through the volume and stored in a space-efficient manner using a graph structure. Through this graph, radiance transport is then computed. In the rendering stage, the graph structure with radiance values can then be used to render the volume. The results show that our method can produce renders with negligible bias. They also show that the method is heavily constrained by space requirements as volume size increases, which results in an increase in bias for larger volumes. The main advantage of the method is its efficiency in rendering multiple images of the same object.

The design of data collection scenarios is critical for evaluating intelligent systems for social intention recognition in aviation. Identical aircraft behaviors can generate multiple equally plausible intention interpretations depending on situational context and the observer’s professional perspective, yet existing research offers limited guidance for constructing scenarios that preserve this interpretive open-endedness. This study addresses this gap through an exploratory, literature-based investigation of how contextual factors shape intention interpretation across aviation roles. An integrated framework combining the 3Cs model of situational analysis and script theory is proposed to identify contextual dimensions influencing interpretation. Through qualitative synthesis of aviation literature, the framework demonstrates how variations in cues, classes, characteristics, and internal-external script configurations can produce divergent but valid intention narratives for the same observable behavior. The resulting scenario-first methodology provides structured guidance for designing aviation scenarios that support role-dependent intention annotation and evaluate intelligent systems. As a conceptual contribution, the framework requires empirical validation by aviation professionals.

High-Dose-Rate brachytherapy is a critical component in the treatment of locally advanced cervical cancer. While automated treatment planning systems, such as BRIGHT, have demonstrated the ability to generate high-quality plans, their clinical adoption is hindered by the complexity of their configuration. Deploying such a system in a new hospital requires the manual definition of a clinical protocol that accurately reflects the local institution’s specific standard of care. This "cold start" problem is time-consuming for both doctors and researchers.

This thesis proposes a novel framework for the Automated Discovery of Clinical Protocols. By formulating the protocol configuration as a bi-level optimization problem, we employ the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm to autonomously extract implicit expert knowledge from a repository of historical clinical plans. The system evolves a set of protocol parameters that, when fed into BRIGHT, reproduce radiation dose distributions as preferred by human experts.

We validate this approach using anonymized patient data from Virginia Commonwealth University. Through a series of experiments with incrementally increasing complexity, ranging from optimizing simple dose thresholds to evolving the definitions of dosimetric metrics, we demonstrate that the proposed framework can successfully identify protocols that generate treatment plans that are quantitatively similar to the clinical ground truth. This research serves as a proof-of-concept, offering a pathway to rapidly deploy automated planning systems while ensuring alignment with local clinical expertise.

Intelligent systems are being increasingly deployed alongside humans, yet their ability to understand human intentions in order to perform tasks better is far from perfect. In this paper a literature survey will be performed to obtain existing examples of narratives, with and without intelligent systems,that take place in a Hospital - a setting with a variation in situation openness, where a framework of cues, characteristics, classes, internal scripts, external scripts, and memory and associative triggers all play an equally important role in determining an individual’s intention. Dimensions were extract from these scenarios and were analysed based on the three roles: Patients, Clinicians, and Family Members. Results and findings were used to develop a new case study involving an intelligent system assisting a doctor with diagnosing a patient and communicating it to them. Variations of contextual cues, amid other dimensions, were made to demonstrate multiple plausible narratives and its effects on intention.

Intelligent systems in autonomous driving increasingly require the ability to infer social intentions to ensure safe and fluid interactions with human road users. However, current approaches typically frame this problem as objective trajectory prediction or fixed classification, ignoring the open-ended nature of human interpretation where a single physical behaviour can generate multiple plausible narratives. To address the gap between trajectory forecasting and narrative understanding, this research investigates how to systematically map the dimensions of variation in driving situations to the range of intention narratives they generate. A literature survey was conducted to distinguish between foundational human social norms and current algorithmic approaches. By integrating script theory with the 3Cs framework (Cues, Characteristics, Classes), this study developed a dimension extraction framework to analyse where objective observations diverge into subjective interpretations. Through comparative analysis of prototypical scenarios (lane merging and pedestrian negotiation), results revealed that current intelligent systems operate predominantly in geometric space, optimizing for physical feasibility, whereas human drivers operate in social space governed by normative scripts. The research concludes that narrative open-endedness is inversely proportional to the strength of physical and social constraints. That is, when constraints are weak, human internal scripts diverge from machine logic, leading to critical prediction errors. Consequently, future systems must shift from raw trajectory output to semantic narrative understanding to explicitly model this uncertainty and align machine reasoning with human expectations.

Virtual Reality (VR) offers the possibility to explore and interact with complex digital worlds, yet natural locomotion is constrained by the limits of physical space. Hyperbolic geometry provides a compelling solution by embedding infinite virtual environments within finite areas, creating novel opportunities for research and design. This thesis investigates how embodied training and haptic feedback can enhance navigation and user experience in such non-Euclidean spaces. Twenty-eight participants took part in a between-subjects user study, using Holonomy VR, a hyperbolic VR application instrumented with the SenseGlove Nova 1 for force feedback and vibrotactile interaction. Participants were trained with either a drag-based embodied interface or a conventional button-based control scheme before completing matched navigation tasks. Performance was measured through speed, path efficiency and sequencing, while user experience was assessed through established questionnaires and interaction behaviour. The study finds that embodied training affords a practical advantage in subsequent navigation, and that perceived engagement with haptic elements is a strong predictor of positive usability, beyond the effects of task duration alone. Together, these results demonstrate that embodied practice and meaningful tactile interaction can help users adapt more effectively to non-Euclidean environments, offering both methodological contributions for VR research and design implications for the creation of more intuitive and engaging virtual worlds.

Recent advances in generative AI have enabled high-quality video generation from text prompts. However, the majority of existing approaches rely exclusively on prompts, making it difficult for an artist to control the generated scene layout and motion. In this thesis, we propose a novel method for geometry-guided Text to Video generation. Our method takes as input an animated mesh sequence and a text prompt and generates a video following both the text prompt and input geometry. Our pipeline consists of two main stages: Firstly, we use an existing text-driven texture generation method to create an initial rough texture for the geometry. Next, a depth-conditioned T2I model is used to generate video frames following the guidance animation, using the generated texture to enforce temporal consistency across frames. By generating video frames rather than directly using the result of the texture generation, our method supports generating deformations from the guidance geometry and variable lighting and by using the texture for feature alignment, we acheive significantly stronger robustness to occlusions and camera motion than existing controllable video-generation approaches. We begin by identifying the failure modes of existing methods through a set of initial experiments, we then use these findings to propose our method and finally evaluate it through a series of comparisons and ablations.

Distinguishing between benign and malignant ovarian cysts is a challenging task that depends on subjective visual markers in ultrasound scans. Current manual methods remain prone to costly misdiagnoses and the application of these methods depend heavily on the clinician's level of expertise. Recent research demonstrates promising applications of Convolutional Neural Networks (CNNs) for ovarian tumor classification; however, we observed that their performance is limited when applied to a diverse and complex dataset. To address this, we propose, implement, and evaluate three improvements to a baseline classifier.

First, we use a deep learning-based approach to remove burned-in medical annotations and introduce a weighted mean squared error (MSE) loss to improve its effectiveness by emphasizing relevant regions. This aims to better recover the original image content prior to annotation and remove annotations which can act as confounders. Second, we enhance classification by fusing image features with two readily available clinical factors at an intermediate stage of the network. Third, and central to this study, we incorporate a segmentation path that acts as a regularizer, encouraging the shared encoder to learn lesion-specific features that benefit the classification head.

These three contributions are informed by domain-specific knowledge of ovarian lesions and collectively demonstrate promising directions for improving deep learning-based models in this setting.

Single-cell RNA sequencing (scRNAseq) is a measuring technique of gene expressions in single cells that has allowed researchers to tackle Alzheimer’s disease (AD) in many ways. Single-cell data has been joined with machine learning to classify brain cells as affected by AD. However, not much is known regarding the usage of such classification models in a spatial setting. This paper analyzes how models trained on scRNAseq data can be used to find AD properties of single cells when measuring them with spatially resolved transcriptomics. With that we study the hypothesis that cells labeled as affected by the disease should appear closer to amyloid plaques, than those that are unaffected. To find out if this holds, three models are used to classify single cells spatially and their predictions are analyzed. Two single-cell datasets are used for training, each giving a drastically different classification outcome. The models do not come to a consensus on the hypothesis’ validity either, as the analysis finds no significant correlation between the variables.

Alzheimer’s Disease is a complex neurodegenerative disorder marked by the abnormal build-up of proteins in the brain. As no cure currently exists, understanding the disease’s cellular mechanisms is essential for advancing diagnostics and treatment. To this end, single-cell RNA sequencing (scRNA-seq) is a method that offers detailed information about the gene activity of individual cells but lacks their spatial context. Conversely, spatial transcriptomics technology preserves the localization of the cells but provides more limited transcriptomic information. To resolve this, we provide a model that predicts a cell’s distance to pathology from single-cell RNA-sequencing data. Additionally, we identify APOE, LYVE1, and SLC17A7 as genes potentially associated with AD-related microglial clustering around plaques.

Alzheimer's Disease (AD) is a complex heterogeneous disease and is the leading cause of dementia around the world. Treatment options remain limited and the underlying mechanisms are not yet fully understood. To get more insight on this celular level, single-cell gene expression data can be used. It has proven to be effective with machine learning for tasks like cell type classification. While prior studies have explored AD classification using scRNA-seq, this has only been a binary classification. Severity of AD is classified using multiple measures, ranging from cognitive ability scores, to neuro pathological measures. This research explores the possibility of expanding the binary prediction of AD by including these measures for AD severity. In addition, given that these measures are associated, we also investigate if Multi Task Learning (MTL) models can improve the predictions by learning multiple AD related data points. If successful, this approach can give additional analysis into key tasks, genes and/or cells (sub)types that drive the models, which would lead to more possibilities for personalized treatment options, alongside more insight into the development of AD in the brain. We used a three-layer neural network architecture alongside a translation from cellular level to individual level to make individual-level predictions. Results show that Cognitive Ability can be classified best, but overal performance is only slightly above Naive Bayes. Furthermore, MTL does not appear to have any measurable positive effect on scores compared to single task models. A link to the github repository is available at \url{https://github.com/WillemDieleman/ADseverityCSE3000}.

This study tackles an important issue in evaluating the reliability of confidence intervals in causal forests by examining how data characteristics and hyperparameters influence actual coverage rates compared to theoretical benchmarks. Using synthetic data sets with polynomial treatment effects, Sobol sampling, High-Dimensional Model Representation (HDMR), and comprehensive grid searches, the study assesses causal forest performance in different data contexts.

A primary discovery is the identification of a practical limit for reliable confidence interval coverage: When the sum of confounders and effect modifiers exceeds 4, coverage rates drop considerably below 80%, even for simple treatment effect functions. This limitation remains steady despite substantial increases in computational resources.

The examination of hyperparameters revealed that the most influential parameters are the maximum tree depth and the balance tolerance in splits, which demonstrate substantial changes in performance, both of which performed best at their maximums (unlimited and 0.5, respectively). Other key suggestions involve increasing the training data fraction per tree from 0.45 to 0.5, keeping the minimum impurity decrease threshold at 0.0, and utilizing at least ≈ 2400 trees to meet theoretical expectations.

In addition, this paper did not identify any noteworthy interaction between tree count and sample size. As a result, both of these characteristics can be optimized independently of each other.

These findings provide systematic guidelines for practitioners to assess when causal forest confidence intervals are reliable and how to optimize them, bridging the gap between theoretical guarantees and practical performance.

Estimating the Conditional Average Treatment Effect (CATE) with neural networks adapted for causal inference, like TARNet, is a promising approach, yet the impact of model architecture on performance remains underexplored.
This paper systematically investigates how the depth and width of TARNet affect the CATE estimation in diverse simulated data environments. The research investigates two central questions: how TARNet's performance varies across data regimes (e.g., confounding strength, sample size), and how its optimal architecture changes in response to these conditions.
A comprehensive set of simulation-based experiments is conducted using the CATENets framework, isolating and varying factors such as sample size, feature dimensionality, confounding strength, and the presence of noise. The results demonstrate that deeper architectures generally yield better performance in complex or high-dimensional scenarios, whereas narrower networks are preferable in small-sample or high-noise settings due to their regularizing effect. Furthermore, the findings suggest that there is no universally optimal architecture. The best configuration depends on the specific characteristics of the data. The study concludes with practical recommendations for architecture selection based on the experiments conducted.

Causal Multi-task Gaussian Processes (CMGPs) provide a Bayesian approach for estimating in-
dividualized treatment effects by modeling potential outcomes as correlated functions. However,
they struggle under high-dimensionality and treatment imbalance, leading to overfitting and unre-
liable uncertainty estimates. This study examines two failure modes: poor generalization in high-
dimensional spaces and overconfident predictions in low-overlap regions. To address these, two
data-aware enhancements are proposed: an overlap-adaptive kernel that scales similarity based on
local treatment density, and a regularized prior that down-weights unstable features using marginal
treatment effect variance. Evaluations on synthetic data and the IHDP benchmark show improved
effect estimation, credible interval calibration, and robustness in challenging settings. These find-
ings highlight practical strategies for enhancing CMGPs in real-world causal inference tasks.

Causal inference, particularly the estimation of the Conditional Average Treatment Effects (CATE), is necessary for understanding the impact of interventions beyond simple predictions. This study analyzes the influence of key hyperparameter choices, specifically maximum tree depth and minimum leaf size, on the accuracy and generalization of CATE estimates derived from honest and adaptive causal trees. The research explores how these hyperparameters affect the bias-variance trade-off and the model's tendency to overfit or underfit across various simulated data scenarios and a real-world dataset.

The results reveal that optimal hyperparameter configurations are dependent on the data characteristics, such as dimensionality, noise levels, and the complexity of the true causal effects. Honest causal trees demonstrate a better performance in high-dimensional and noisy environments due to their effective variance control. Conversely, in simpler, low-noise settings or complex CATE structures, adaptive causal trees or baseline models frequently achieve better results by reducing bias. The study also highlights the challenges of using moderately sized datasets, where the sample splitting limitations can lead to higher estimation errors. This work provides thorough suggestions for hyperparameter selection, emphasizing the fact that tuning based on the underlying characteristics of the data is needed for achieving the best CATE estimates possible.

Reflectance fields have a limited number of discrete lighting directions that can be used for lighting design. Multiple methods for interpolating these reflectance fields have been proposed to get an approximation of the missing lighting directions. However, these methods can struggle with specular highlights, because they are more sensitive to changes in lighting direction. We propose a method that solves this problem by representing the specular highlights as Gaussians and interpolating them, framing the problem as a Lagrangian formulation. We interpolate these Gaussians by optimizing their parameters twice: first for each lighting direction, then for every neighboring lighting direction. By doing this, we can efficiently interpolate specular highlights in real-time. This enables the designer to design the specular highlights and their movements with immediate feedback.

The process of knowledge elicitation is crucial to the field of artificial intelligence because of the lack of data on commonsense knowledge. This paper explores the potential of using large language models (LLM) to enhance knowledge elicitation in games with a purpose (GWAP). By analyzing the capability of LLMs to play games and generate game content, this research shows how LLMs can increase accessibility, efficiency and engagement in GWAPs for knowledge elicitation. The findings show that LLMs can play games with great performance and show much promise in generating game content that facilitates knowledge elicitation. Through a comprehensive literature survey, this research highlights potential ways of enhancing knowledge elicitation in GWAPs using LLMs and offers recommendations for future research in this field.

The swift growth of artificial intelligence has led to the development of large language models, revolutionising various scientific domains and professional fields. This research explores collaborative, cooperative, and competitive game designs, that enhance knowledge elicitation using LLMs in games with a purpose. Through a systematic literature review using the PRISMA workflow, we identify that collaborative and cooperative game designs are more effective for knowledge elicitation than competitive designs. We also address the potential and limitations of incorporating LLMs into GWAPs, such as their use as Non-Player Characters to create engaging interactions. This study provides design principles for GWAPs leveraging LLMs, offering insights for researchers and developers, and discusses ethical considerations and future research directions.