High-dimensional, multi-view datasets, in which the relationships between the same objects are described by multiple distinct feature sets, are increasingly common across scientific domains, yet existing dimensionality reduction methods offer limited support for their simultaneous visualization. Currently, multi-view extensions of t-SNE typically blend information across views into a single embedding, sacrificing the individual structure of each subspace, and generally rely on the assumption that the different views that are being analyzed still represent the same relationship within the data, while the question of how to create an embedding that can visualize simultaneously multiple dissimilarity matrices corresponding to different relationships on the same data set is largely left unanswered.
Multi-Perspective, Simultaneous Embedding (MPSE) and ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE both address this limitation by generalizing their respective base algorithms - MultiDimensional Scaling and t-SNE - to simultaneously embed multiple distinct distance matrices. However, as a consequence of their base algorithms, the former efficiently preserves global structure while the latter focuses on local neighborhoods. Yet, neither preserves both, and neither allows the use of additional information to improve embedding and visualization.
I present Multi-View Constrained t-SNE (MVC t-SNE), a supervised, multi-view dimensionality reduction algorithm that addresses this limitation by combining ENS-t-SNE with Class-Constrained t-SNE, a supervised extension of t-SNE which encodes label information through the use of landmarks, iconic representations of the classes.
The resulting algorithm produces a 3D embedding and a set of 2D projections, each optimized for a different feature subspace, while incorporating class information and structure.
The trade-off between preserving feature structure and enforcing class separation is controlled through the user-defined parameter α.
I propose two different versions of the algorithm: in the first, 3D embedding and 2D projections share the same set of landmarks, while the second version allows the use of different sets of labels for each projection. The evaluation shows empirically that MVC t-SNE produces viable embeddings, including class information without sacrificing data structure quality. ... Read more

Learning curves describe how model performance changes as more labeled data becomes available and can help estimate whether collecting additional labels is worthwhile. However, it remains unclear which mathematical functions best represent and extrapolate learning curves for graph neural networks. This study compares power-law and exponential models for learning curves generated by a graph neural network on node-classification datasets with different graph characteristics. The models are evaluated separately on how well they describe observed performance and how accurately they predict performance at larger, unseen labeling budgets. The results show that neither model family is universally preferable. Exponential models provide better descriptive fit on some datasets, while power-law models provide better descriptive fit on others. In the extrapolation experiments, power-law models often give more accurate predictions at larger labeled-node budgets, although the preferred model still depends on the dataset and fitting range. These findings indicate that descriptive fit and extrapolation accuracy should be treated as separate objectives. Overall, power-law behaviour appears to be a useful modelling assumption for some GNN learning curves, especially for extrapolation, but it should not be assumed to hold universally.
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Graph Neural Networks (GNNs) achieve strong performance on node classification tasks, but their effectiveness often depends on the quality of the supervision, and real-world labels are often noisy. Learning curves—which describe how test performance scales with the number of labelled training nodes—have been extensively studied in classical machine learning, but their behaviour under realistic annotation noise in GNNs remains poorly explored.

We present a systematic empirical study of how three label noise protocols—symmetric random flipping, feature-dependent asymmetric flipping, and structure-dependent flipping—affect the learning curve shape of ChebNet across four benchmark graphs spanning homophilic and heterophilic structure, at noise rates η ∈ {0.1, 0.3, 0.5}.

The central finding is that noise does not simply shift the learning curve downward: above a moderate noise rate it reduces the effective slope, so the gap between clean and noisy performance widens as the label budget grows. Feature-dependent asymmetric noise is consistently the most harmful protocol across all datasets and budgets for η ≥ 0.3, while structure-dependent noise is the least harmful on homophilic graphs. On graphs where the model already operates near its performance limit, noise type has little practical effect.

These findings suggest that beyond a moderate noise rate, cleaning existing labels yields greater returns than acquiring more noisy ones, and that the nature of annotation error interacts with graph structure in ways that single-budget evaluations cannot detect. ... Read more

In this thesis, we address the problem of learning mesh-specific, impulse-dependent fracture patterns in real time. Our approach is based on regressing a distance field over the mesh surface, encoding the proximity of each vertex to fracture lines, which is subsequently segmented into distinct pieces using graph-based methods such as watershed segmentation. The goal is to achieve real-time performance, which is something the current approach does not achieve for large meshes.

We evaluate different neural architectures, comparing a multilayer perceptron to DeltaConv, a graph convolutional model, and find that the MLP provides superior performance. In addition, we assess multiple segmentation strategies and identify watershed as the most effective, followed by hierarchical segmentation. We also find that the segmentation algorithms do not achieve real-time performance for large meshes.

These results highlight the potential of machine learning-based fracture simulations, but also indicate that distance field segmentation is not capable of real-time performance using our tested algorithms. This suggests that future work should focus on directly learning the labels rather than relying on distance fields as an intermediary representation in real-time scenarios.
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Water flows through every aspect of life, yet the story of its delivery is only as reliable as the data that records it. In global benchmarking, such data is often uneven, incomplete, and rarely subjected to systematic validation, allowing anomalies to shape perceptions of performance before they are critically examined. This thesis addresses that gap by developing and evaluating a multi‐stage, data‐driven anomaly detection framework within the World Bank’s New International Benchmarking Network for Water and Sanitation Utilities (NewIBNET), situated at the intersection of data science, water governance, and digital ethics.

The framework weaves together four complementary layers – structural validation, rule‐based logical checks, peer comparison, and weighted prioritisation – transforming anomaly detection from a surface‐level cleaning task into a structured process of active quality assurance. Developed through an iterative, expert‐informed process, it is reproducible and adaptable, balancing statistical rigour with the contextual realities of the water sector so that each flag raised carries both analytical credibility and practical relevance.

Applied to the 2022–2024 NewIBNET dataset, the framework is assessed through robustness checks, a national case study of Indonesian utilities, and an expert survey. Results show that it improves anomaly interpretability, limits the propagation of flawed data into comparative analyses, and reduces review time from 75 hours to under 2 minutes – earning unanimous expert endorsement for operational deployment.

By translating the principles of automated, ethically grounded validation into a scalable methodology, this work advances the state of practice in anomaly detection for data‐scarce sectors. In shifting from red flags to real solutions, it demonstrates how automated validation can turn detection into action, building trust where data meets water, and enabling more transparent, equitable decisions in global water governance. ... Read more

Over the past decade, model-based reinforcement learning (MBRL) has become a leading approach for solving complex decision-making problems. A prominent algorithm in this domain is MuZero, which integrates Monte Carlo Tree Search (MCTS) with deep neural networks and a latent world model to predict future states and outcomes. Despite its effectiveness, MuZero is inherently limited by the sequential nature of its search-tree construction during planning. In this work, we address this limitation by introducing TransZero-Parallel, the first model capable of constructing MCTS without any sequential constraints. This method replaces MuZero’s recurrent dynamics model with a transformer-based network, enabling the computation of a sequence of latent future states in parallel. We combine this with the MVC evaluator, which allows the search tree to be built without depending on the inherently sequential visitation counts. Together with small modifications to the MCTS algorithm, this enables the parallel expansion of entire subtrees within the search tree. Experiments in MiniGrid and LunarLander environments demonstrate that this combined approach yields up to an eleven-fold reduction in wall clock time while maintaining sample efficiency. These results highlight the potential of TransZero-Parallel to improve planning performance and reduce training time in model-based RL—bringing the field closer to real-time, real-world applications. The code is available through GitHub.

Github footnote: https://github.com/emalmsten/TransZero ... Read more

Automatic Speech Recognition (ASR) systems perform well for typical adult speech but remain challenged by children’s speech, especially that of children with Developmental Language Disorder (DLD). This study investigates how ASR performance can be enhanced for DLD speech while maintaining accuracy on typical child speech. Two state-of-the-art ASR models, a conformer-based model and Whisper-large-v3, were evaluated using Dutch typical (Jasmin) and atypical (Auris) child speech. The experiments examine data augmentation methods, including speed perturbation and vocal tract length perturbation, and transfer learning through fine-tuning. Results show that both techniques improve DLD speech recognition without degrading typical speech accuracy. The best performance was achieved by combining augmentation and fine-tuning with domain-matched DLD data, reaching 53.2% WER on the Auris test set, while mismatched fine-tuning reduced gains, particularly for Whisper. Overall, the findings demonstrate that integrating data augmentation and fine-tuning offers an effective, balanced approach toward inclusive and robust ASR for children with DLD. ... Read more

In a growing and highly competitive online grocery market, online grocer Picnic must prioritize optimizing the efficiency of its logistics chain to achieve profitability. Last-mile delivery is a major contributor to operational costs, making its optimization essential. This thesis presents a study on optimizing last-mile delivery operations for Picnic through the application of a Hybrid Genetic Search (HGS) algorithm. The research addresses the Vehicle Routing Problem with Time Windows (VRPTW), factoring in Picnic’s unique operational constraints, including multi-compartment vehicles, a heterogeneous fleet, vehicle time windows, prioritized vehicles, and varied service times.

The study evaluates the performance of the HGS algorithm against Picnic's existing VROOM algorithm in real Picnic instances. Results demonstrate that, on average, HGS consistently outperforms VROOM, achieving significant reductions in total route duration. However, while HGS excels in optimizing route duration, it does not always minimize the number of vehicles used.

This research extends the state-of-the-art Hybrid Genetic Search (HGS) algorithm to tackle the specific challenges of Picnic’s vehicle routing problem. By applying this enhanced algorithm to a real-world setting, it bridges the gap between theoretical optimization advancements and practical industry implementation. ... Read more

The evolution of malware presents an ever-growing challenge to cybersecurity, impacting individuals, organisations, and nations alike. As malicious actors continue to adapt their tactics to bypass security measures, it becomes imperative to understand the evolutionary patterns of malware to stay ahead in the ongoing arms race between defenders and attackers. The complexity and sophistication of modern malware pose significant difficulties in detection and mitigation, making it crucial to unravel its evolving nature to enhance the existing defensive capabilities.

This research focuses on studying the evolutionary dynamics of malware, examining how variants emerge to circumvent existing security measures. Understanding the mechanisms through which malware evolves makes it possible to identify common patterns and develop strategies to predict the behaviour of certain malware. This work mainly encompasses Windows ransomware, particularly the Conti family, with an additional examination of the WannaCry and Ryuk families. The analysis was conducted primarily by applying dynamic malware analysis techniques to the samples. A total of 143 true-positive Conti samples, alongside 75 WannaCry and 21 Ryuk samples, were collected from reputable sources such as VX-Underground and Malware Bazaar. By utilising the ANY.RUN interactive sandbox for dynamic behavioural analysis, malware samples can be executed in a controlled environment and real-time behaviours, such as file modifications or registry changes, can be collected to discern the malware's underlying functionality and potential impact. In addition, the results obtained from Virustotal, a widely-used online malware scanning platform, are considered to get insights into the detection status of the analysed samples across multiple antivirus engines. Finally, Microsoft Defender Antivirus is utilised to classify the variants and eliminate false positives as much as possible. The tactics and techniques outlined in the MITRE ATT&CK Matrix are used to assess sample behaviour. This framework provides valuable insights into the observed behaviour of samples and the methods employed to achieve specific objectives.

The results answer the question "How do different variants of the malware families succeed in bypassing security measures?" and split the answer into three smaller ones. Overall, it can be observed that different ransomware share common traits, but differences over time and between variants and families can be seen. Some differences exist between the version of the operating system in which the malware is executed. Malware evolves, and the changes of the malware authors are reflected in their malware's behaviour and structure. Some changes persist, whereas new ways quickly replace others. By understanding the evolution and analysing the patterns that emerge, we can build our defences in a way that predicts incoming threats and creates a safer space for everyone. ... Read more

Low-dimensional datasets, for which each datapoint contains no more than three attributes, are straightforward to visualize with common visualization idioms, such as scatterplots. In order to visualize high-dimensional datasets with potentially thousands of attributes, their dimensionality will need to be reduced. t-SNE is a widely used, state-of-the-art algorithm for non-linear dimensionality reduction. It produces embeddings of the high-dimensional data onto two or three dimensions by using gradient descent to minimize the discrepancy between the probability distributions of the high-dimensional and low-dimensional datapoint similarities, iteratively adjusting the low-dimensional embedding. However, since it is impossible to capture all neighbourhoods over all high-dimensional axes in two or three dimensions, the resulting clustering is merely virtual, only meant to offer an intuition of the dataset's high-dimensional distribution. Our work proposes a number of tools that allow a user to interactively impose constraints based on prior knowledge or assumptions about the dataset and manipulate the underlying probability model with the aim of enhancing an embedding's interpretability, for a better intuition about the data's high-dimensional structure. We demonstrate the effectiveness and limitations of these tools with examples and case studies. ... Read more

In recent years, significant progress has been made in the field of natural language processing (NLP) through the development of large language models (LLMs) like BERT and ChatGPT. These models have showcased remarkable abilities across a range of NLP tasks. However, effectively harnessing their potential requires meticulous prompt engineering and a comprehensive understanding of their limitations.

Additionally, LLMs have attracted attention in the educational domain for their potential to enhance learning and teaching experiences, particularly in fostering the development of computational thinking skills.
This paper aims to explore the potential of leveraging NLP and prompt engineering techniques to generate successful solutions to coding problems following initial failures. Furthermore, the research explores the potential applications of NLP techniques in teaching and learning practices involving LLMs and their potential drawbacks in this context. ... Read more

Code generation is becoming one of the most important tools in an Integrated Development Environment (IDE) for programmers, be they novices or experts. It allows them to produce code faster, avoid typos and other mistakes, be more efficient and easily turn ideas into code. As such, there are many in-IDE code generation models to choose from, each with their own promises, functionalities and implementation techniques. This report presents the results of a literature survey into the code generation models that have been integrated into programming environments and serves as an overview and analysis of them. The results of this paper provide a set of guidelines for code generation that have been summarized from the selected literature. ... Read more

Using AI to support programming has recently gained a lot of popularity. Researchers have been developing tools to support programming activities using GPT models such as ChatGPT and Codex In this paper, we present the most common pro-gramming activities that these models can support. We show that they have a varying range of success
across solving Code Generation, Code Explana-tion,and Data Visualisation problems, but are often able to solve around 50% of problems on the first try. Multiple tries can raise these averages to 75%. Additionally, specialized tools using GPT models have seen promising results regarding Data Visuali-sation, Software Vulnerability Detection, and Gen-
eral Programming Support. This shows a promis-ing trend, and can mean we will all be pair pro-gramming with AI in the near-future. ... Read more

The recent emergence of AI-driven code generation models can potentially transform programming education. To pinpoint the current state of research on using AI code generators to support learning and teaching programming, we conducted a systematic literature review with 21 papers published since 2018. The review presents the teaching and learning practices in programming education that utilize these models, the characteristics and performance indicators of the code generation models, and aspects to be considered when utilizing the models in programming education, including the risks and challenges of using code generation models for educational practices. AI code generators can be an assistive tool for both learners and instructors if the risks are mitigated. ... Read more

Since large language models (LLMs) have emerged, they have taken a prominent role in today’s society. From society, they have also found their way into the field of education; that is why in this research paper, we looked into assignments and exams from the TU Delft Computer Science and Engineering bachelor’s programme and assessed which problems Generative Pre-trained Transformer (GPT) version 3.5, the current version used by ChatGPT, performs well on (i.e. at least above a pass rate) and on which problems it performs less well (i.e. below pass rate). For our research, we collected assignments by asking professors for consent to make sure our research was ethically correct. Upon receiving consent, professors had the option to send material, which allowed a deeper analysis, or they could also allow a Brightspace (site where TU Delft courses are hosted) course page scraping. Once all the questions were gathered, we processed them by prompting them into ChatGPT. We gathered the results and categorized them as wrong or right. We did this all with as few modifications to the questions as possible. The only modifications we made were corrections of copy errors from a PDF, for example: C becoming e after copying. From the results, we found that ChatGPT has its limitations, particularly in large code understanding and complex mathematical reasoning. However, the model performed well in defining concepts and connecting different ideas. We suggest that GPT lacks a comprehensive understanding of coding principles, which hinders its ability to comprehend code. Future work could include exploring other LLMs like GPT-4 and comparing their performance. Further work could also look at assignments from other universities, possibly in different educational fields. Additionally, investigating different prompting techniques to enhance the model’s accuracy and reliability could also be done. ... Read more

This paper introduces and studies the Multi-Level Bin Packing Problem with Time Windows. This is a NP-hard problem with real-world applications in logistics. This problem states that items with certain sizes need to be packed inside bins without exceeding their capacity. These bins then again need to be assigned to other bins while minimizing the cost of the used bins. At the same time, each item has a time interval and there must be a common time point between all the items that are packed in the same bin that should be as early as possible. Not much research has been done regarding this problem. In this paper, we suggest multiple models that can be solved using Integer Programming and compare their performance. The results show slight differences in performance between the models. ... Read more

The Multi-Level Bin Packing problem is a generalisation of the widely-known NP-hard Bin Packing problem. This work aims to investigate approaching this problem, as well as a version including Class Constraints, through integer programming. By modelling the problems in two ways: standard, and with a network flow approach. The studying of the performance of these formulations on various instances, provides a greater insight into the possibilities of integer programming for this purpose. Furthermore, evaluating the two approaches gives an initial suggestion for developing effective models for solving the aforementioned problems. ... Read more

The multi-level bin packing problem (MLBP) and its variant problem with partial orders (MLBPPO) are NP-Hard problems that can be applied in a logistical setting to improve efficiency in packing. However, despite their possible use cases, there is little to no literature on how to solve these problems in a reasonable amount of time. In this work, we propose two Integer Programming (IP) models for each problem, one of which is an adaptation from an already existing bin packing implementation, and another with network flow constraints for stronger LP relaxations. The comprehensive experimentation conducted on varying sizes of problem instances suggests that the presented models for the MLBP are highly effective at solving instances with up to 50 items and 5 levels, while neither of the models outperforms the other decisively. For the MLBPPO, the model based on the BP implementation is potent at solving up to 20 items and 5 levels depending on the number of partial orders, while the network flow model cannot compete. ... Read more

The Multi-Level Bin Packing problem and its variants are some of the most popular combinatorial optimization problems. They have a wide range of real-life applications yet, they are some of the harder problems we know of. In this paper we solve the Multi-Level Bin Packing Problem and a variant of it, Multi-Level Bin Packing Problem with Conflict Constraints, using Integer Programming. We propose two Integer Programming models, a standard one and one with additional flow optimizations. We hypothesized that the second model will have a smaller solution space and consequently an improved performance for both of the problems. However, we find that the second model’s performance is worse and that although it may have a smaller solution space, the comparison of the BnB nodes points towards inconclusiveness. ... Read more

Multi-Level variants of classic optimisation problems are becoming more noteworthy as the complexity of real life applications increases. In this research we investigate the Multi-Level Bin Packing optimisation problem, which models, for example, global logistics and part manufacturing. We will look at the performance of solving Integer Linear Programming formulations of the Multi-Level Bin Packing problem using IBM ILOG CPLEX Optimization Studio, and compare these results to the performance of simple heuristic-based algorithms to reach conclusions about the usefulness of optimal-solution algorithms for NP-Hard problems. We ultimately find that the simple heuristics leave a large gap in optimality, while the solving time for finding optimal solutions is still too large for practical instances. As such, we conclude that more specialised algorithms are needed that can balance the time cost and optimality, depending on the application. ... Read more