The volumes of data produced by humans and machines today significantly outpace humans' ability to absorb, evaluate, and make complicated decisions based on that data. Artificial intelligence/machine learning is the foundation of all computer learning and the future of all complex real-world decision-making under uncertainty.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines reason, learn, predict, plan, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of data driven intelligence. This broad area studies artificial intelligence/machine learning theory (such as algorithms and optimization), big data (data management, computation, and analysis), statistical learning (such as inference, graphical models, and causal analysis), deep learning (such as adversarial learning, explainability, and knowledge representation), reinforcement learning, symbolic reasoning, as well as diverse hardware implementations of machine learning.

Participating Faculty