Welcome to the Information Management Laboratory (IMLab) @ UVA School of Data Science!

We study interdisciplinary topics on Information × Humans × AI Agents × Evaluation. Our research aims to manage the information that flows between people and intelligent systems so that it is relevant, trustworthy, and useful for human decision-making. We examine how AI selects and delivers information through recommendation, how the quality of that information can be measured, and how people perceive, trust, and act on it. As AI evolves from single models into autonomous agents, we design human- and agent-in-the-loop frameworks in which humans and AI agents exchange feedback and improve one another across diverse domains. IMLab is part of the School of Data Science at the University of Virginia.

Research

• Human-Centric Recommender Systems — Recommendation models that reflect how people form preferences and make choices, using multimodal, graph-based, and behavioral data.

• Information Quality and AI Evaluation — Principled measures of whether AI outputs and explanations are truthful, useful, and trustworthy to the people who rely on them.

• Human Modeling and Human–AI Interaction — Computational models of human judgment, confidence, and error to predict and improve how people use information from intelligent systems.

• Human- and Agent-in-the-Loop Systems — Frameworks in which humans and AI agents give feedback to, supervise, and correct one another, applied to e-commerce, education, healthcare, manufacturing, public policy, and more.