I am a PhD candidate in the Machine Learning and AI lab of Prof. Mihaela van der Schaar at the University of Cambridge, department of Applied Mathematics and Theoretical Physics. My research aims to make AI systems more data-efficient, adaptive, and aligned with humans. I study how models—especially large language models—can represent human knowledge and make decisions consistent with both empirical data and contextual cues. My work combines ideas from meta-learning, probabilistic ML, and Bayesian experimental design to build learning frameworks that provide principled uncertainty estimates and support more trustworthy decision-making.

I am actively engaged in a collaborative partnership with Eedi, working alongside industry experts to enhance the effectiveness of studying and teaching mathematics among school-age children. This involves the development of novel machine learning models aimed at understanding students’ misconceptions, the evolution of theri knowledge over time, and guiding their learning paths.

Before starting my PhD, I completed Part III of the Mathematical Tripos at the University of Cambridge, specialising in Mathematical Statistics. I obtained my Bachelor’s degree in Mathematics and Statistics at the University of Warwick.