• Formulate and drive original research directions in graph learning for fraud detection, exploring architectures such as GNNs, graph transformers, and hybrid models • Design scalable approaches for dynamic, heterogeneous, and large-scale fraud graphs • Investigate agentic AI and LLM-augmented systems for automated risk reasoning, investigation workflows, and decision support • Develop robust learning techniques for adversarial and non-stationary environments • Conduct rigorous empirical evaluation on real-world, production-scale datasets • Translate research insights into practical system-level implications in collaboration with data scientists and engineers • Contribute to patent development and preparation of submissions to top-tier academic venues