Biography
My project explores combining neural and symbolic AI to model legal reasoning with foundations in legal theory. This approach aims to enhance the trustworthiness, interpretability, and alignment of Large Language Models (LLMs) with legal reasoning processes.
My research focuses on legal data modelling, structuring, and processing through interdisciplinary approaches. I’m particularly interested in rule-dense domains like tax law, which provide rich opportunities to explore how legal frameworks can be structured for machine interpretation with greater transparency and accountability.
My interest in this topic began in 2021, during my undergraduate studies. I was assigned to analyse requirements for cross-border working researchers. The conventional approach produced a document hundreds of pages long, filled with cross-references and abbreviations—highlighting the complexity of international mobility. To simplify this, I transformed the data format from text to tables, resulting in a portal that allowed the users to input their specific circumstances and access only relevant legal information. The project has since expanded in scope, team size, and scale, securing Horizon Europe funding. The identified complexities of international mobility, and the potential for advanced technologies to manage these challenges, have persisted and continue to drive my PhD research.
Outside academia, I enjoy triathlon and hiking.
