This PhD thesis combines two of the most exciting research areas of the last decades:
quantum computing and machine learning. We introduce dissipative quantum neural …

Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix
of tabular data and achieve success in a variety of applications. However, the empirical …

A project failure can be attributed to complex negative factors that can deviate project
progress from the original schedules, and one of the root causes can be a delay. Hence, the …

Graph Machine Learning (GraphML), whereby classical machine learning is generalized to
irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of …

Abstract Graph Machine Learning (GraphML), whereby classical machine learning is
generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a …

While this work marks the end of my formal education at TU Delft, my journey as a lifelong
learner is boundless. Reflecting on the past year, starting with a broken arm and a blank …

Despite their growing popularity, graph neural networks (GNNs) still have multiple unsolved
problems, including finding more expressive aggregation methods, propagation of …

Sustainability is a term that is becoming increasingly prevalent, as several recent
catastrophic events are often attributed to the impact that modern lifestyle has on the …

Graph Neural Networks are an up-and-coming class of neural networks that operate on
graphs and can therefore deal with connected, highly complex data. As explaining neural …