Humans can see the world through the eyes of other humans and imagine what they know, want, and intend. This competence is known as Theory of Mind.

Human language is constantly evolving with its linguistic structure being shaped by language users at both individual and population levels. Focusing on the interplay between processes of language acquisition and communicative need in shaping human languages, this thesis introduces a novel computational…

This thesis investigates the application of deep learning techniques in image analysis across various domains, focusing on four main themes: feature extraction, classification, segmentation, and integration, demonstrating the transformative potential of these technologies.

Healthcare Information Systems (HIS) are essential for modern healthcare delivery, yet their development faces significant challenges including heterogeneous data formats, regulatory compliance, and the growing demand for AI-driven decision support.

Classical statistical methods, such as p-values, are difficult for researchers to apply correctly. They for example do not allow drawing conclusions from a study early, or for extending a study with extra research groups that want to make their data available later.

This dissertation explores how Large Language Models (LLMs) can effectively and responsibly contribute to complex decision-making processes. By combining AI and human intelligence, Hybrid Intelligence (HI) emerges, allowing the strengths of both humans and machines to be utilized.

Data Science & AI-student Nataliia Bagan weet wiskunde, taal en kunstmatige intelligentie op een bijzondere manier te combineren. Haar bachelorscriptie over het verbeteren van het redeneervermogen van grote taalmodellen leverde haar een nominatie op voor de Leiden Science Young Talent Award 2025.