Graph learning for Financial Applications
Investigate supervised, semi-supervised and unsupervised graph learning methods for temporal, heterogenous and large-scale financial behavioral data, especially for anomaly detection applications.
NLP Applications for Financial Decision
Investigate controlled generative models and LLM-enhanced reasoning abilities for financial scenarios, particularly on controllable text generation, multimodal sentiment analysis, personalized LLMs applications for financial scenarios.
Large Models and Agent Learning: Cognitive Modeling and Sequential Decision-Making
Investigate large model- and agent-based learning methods for cognitive and user modeling, multi-agent simulation, and sequential decision-making in dynamic environments, particularly on personalized interaction, adaptive behavior modeling, reinforcement learning, and decision optimization.
