Specializes in Spatial Information Science with Deep Learning, leveraging big data for innovative applications
Academic background in Geographic Information Science, aiming to integrate advanced computer science technologies to enhance spatial research capabilities
Ph.D. thesis focuses on causality analysis using spatio-temporal data
Currently exploring the potential of large language models (LLMs) to empower urban computing in scenarios such as trajectory generation and geo-related fake information detection
Research interests: urban computing, causal inference, LLMs, human mobility prediction