Data Intelligence Lab

Undergraduates interested in research internships are encouraged to take AI-related courses before applying. Qualified undergrad interns will have opportunities to experience world-class research and publish papers at top-tier conferences. The followings are examples of research projects, and undergrad interns will receive detailed, step-by-step guidance as they work on the projects. Recommendation systems are crucial for personalizing user experiences across various digital platforms, including e-commerce and content streaming. Our research addresses the fundamental challenges of collaborative filtering, content-based filtering, and hybrid approaches that combine multiple recommendation strategies. We develop deep learning architectures, including neural collaborative filtering, autoencoders for implicit feedback, and graph neural networks that capture complex user-item relationships. Our work addresses critical issues, including the cold start problem for new users and items, scalability for millions of users, and fairness in algorithmic recommendations. Current projects include developing recommendation systems that incorporate temporal dynamics, building explainable recommendation models that provide transparent reasoning, and creating privacy-preserving recommendation systems using federated learning approaches. Physical AI through Vision-Language-Action (VLA) models represents our cutting-edge research in embodied artificial intelligence that bridges perception, language understanding, and robotic control. Our research focuses on developing AI systems that can understand visual scenes, process natural language instructions, and execute physical actions in real-world environments. We are currently developing robotic AI foundation models in collaboration with Samsun