My research interests broadly span machine learning, graph representation learning, and their applications in finance and recommender systems. I am particularly interested in developing learning models that capture complex relational structures, heterogeneity, and dynamics in large-scale networks.

Some of the research problems I am currently interested in include:

• Graph neural networks and representation learning for structured and relational data;

• Higher-order, heterogeneous, and temporal graph modelling;

• Trustworthiness, robustness, and interpretability in machine learning systems;

• Machine learning methods for financial markets, including asset pricing, risk modelling, and market prediction;

• Multimodal and knowledge-aware learning models integrating structured and unstructured data;

• Applications of graph learning and AI in financial technology and recommendation systems;

I am actively involved in supervising PhD and MSc students in machine learning, data science, and financial technology. I welcome enquiries from prospective PhD students with strong backgrounds in mathematics, computer science, or related disciplines, who are interested in graph-based learning, AI for finance, or applied machine learning.