Techniques for efficient analysis of complex data.

The AI Group conducts research on machine learning, reasoning and planning. We study fundamental limits and principles of learning from data, relationships between machine learning problems, development of efficient algorithms for a range of machine learning tasks, connections between machine learning and computer vision, reasoning about spatial data, and automated planning and scheduling.

Research at the boundary between hardware and software, including the design of better hardware and software to meet the performance and energy requirements of the future.

The availability of massive amount of real-time data is revolutionizing scientific discovery, our understanding of human behavior and complex systems, decision making, and policy development. The Data-Intensive Computing group explores the mathematical and computational foundations underlying this revolution. It carries out multi-disciplinary research at the intersection of computing, the medical, physical, and social sciences, and engineering. It draws from multiple disciplines within computer science including computational game theory, computer vision, high-performance computing, machine learning, network science, and software systems. One of Its main goals is to understand, simulate, predict, and optimize the behavior of complex systems.

Research the interface between humans, data and computers: to build systems which extract humanly comprehensible patterns from data and systems which better understand people so they are more naturally responsive and interactive with us.

Logical reasoning is a fundamental aspect of our everyday lives and is a vital part of our intelligence. The core theme of the Logic and Computation Group is to discover, invent and implement practical automated or interactive reasoning techniques using logic, and to apply them to reason formally about the real world.

Greatly increasing the productivity of people developing software.