Job description

I focus on advance representation learning techniques, with a particular emphasis on developing novel similarity measures and clustering methods. My research explores how relationships—extracted from complex datasets such as spatial omics—can capture intricate dependencies beyond pairwise interactions, offering a richer understanding of medical and biological data. Given the challenges posed by varying structures and labelled samples, my work aims to incorporate underlying semantic relationships through knowledge embeddings and non-Euclidean geometries. By leveraging these advanced techniques, seeking to develop more meaningful similarity measures that enhance the analysis of medical and healthcare data, ultimately contributing to improved predictive modelling and decision-making in clinical and biomedical applications.