WU Huidong 武晖栋

I am a joint Ph.D. candidate at the University of Chinese Academy of Sciences (UCAS) and the City University of Hong Kong (CityU). I earned my bachelor’s degree from the Central University of Finance and Economics (CUFE) in 2021. My research interests encompass Graph Machine Learning, Trustworthy AI, and Risk Analysis.

Research Interests:

• Trustworthy Graph Learning: I explore graph-structured learning and Graph4LLM methods for improving reasoning, decision-making, and interpretability in AI systems. My interests include reliable inference over relational and knowledge-intensive data.
• Data Distillation, Compression & Fairness: I study data distillation and compression across datasets, graphs, and token sequences. I am particularly interested in how compression affects representation quality, bias, fairness, and downstream model reliability.
• Intelligent Risk Analysis: I develop methods for fraud detection, anomaly detection, and information risk assessment in complex digital environments. This includes identifying misleading claims, unreliable evidence, citation issues, and other signals of low credibility.