The Information Management Lab (IMLab) studies how information flows between people and intelligent systems, and how that information can be made relevant, trustworthy, and useful for human decision-making. Our research spans human-centric recommender systems; the evaluation of AI outputs and explanations through the lens of information quality; and computational modeling of human judgment, confidence, and error. Building on this foundation, we design human- and agent-in-the-loop frameworks in which people and AI agents exchange feedback, supervise, and improve one another. Our work is applied across diverse domains, including e-commerce, fashion, education, healthcare, manufacturing, and public policy. We welcome motivated undergraduate, master's, and Ph.D. students interested in recommender systems, AI evaluation, and human-AI collaboration.
Research Tags: Recommender Systems; Human-in-the-Loop AI; AI Agents; Explainable AI; Information Quality; AI Evaluation; Human Modeling; Multimodal Learning
Open to Ph.D. students
