We develop intelligent systems that discover, retrieve, and integrate knowledge from heterogeneous sources, including text, images, and structured knowledge. Our research focuses on multimodal question answering and retrieval-augmented generation (RAG) to provide accurate and evidence-grounded answers to knowledge-intensive questions. We also explore multi-agent systems that collaboratively retrieve information, reason over multiple sources, and solve complex tasks. 

We develop personalized recommender systems that learn from user preferences, behavioral patterns, and sequential interactions. Our research focuses on generative recommendation, where recommendation is formulated as a generation problem, as well as sequential recommendation for modeling evolving user interests over time. We also investigate next-POI recommendation to predict future user destinations from mobility and contextual information. 

We develop machine learning methods for modeling complex temporal patterns and predicting future events from sequential data. Our research focuses on stock trend prediction and financial time-series forecasting, capturing both short- and long-term dynamics in financial markets. We also study time-series anomaly detection to identify unusual patterns and unexpected events in large-scale temporal data. 

We develop machine learning methods for learning and reasoning over complex graph-structured data. Our research focuses on graph representation learning to capture structural and semantic relationships between entities. We further investigate knowledge graph completion and knowledge graph question answering, enabling models to infer missing relations and reason over structured knowledge for downstream applications. 

We develop efficient and scalable data mining algorithms for analyzing and retrieving information from large-scale datasets. Our research focuses on graph mining, high-dimensional retrieval, and pattern matching, with particular emphasis on reducing computational and memory costs. We aim to design algorithms that enable fast graph processing, similarity search, and pattern discovery while maintaining high retrieval and matching accuracy. 

We develop machine learning methods for predicting clinical outcomes and supporting personalized healthcare using electronic health records and medical data. Our research focuses on clinical risk prediction and disease prediction by modeling patients' longitudinal health trajectories. We also investigate medication recommendation to identify appropriate treatments based on diagnoses, treatment histories, and individual clinical conditions.