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.
User Modeling in Cyberspace
Investigate machine learning methods for improving risk assessment or online recommendations, with special focus on cold-start prediction, model calibrations and delayed feedback modeling.
Reinforcement Learning based Financial Application
Investigate reinforcement learning–based approaches to discover, combine, and optimize formulaic alpha factors for quantitative investment.
