Robotics

Enable robots and other agents to develop broadly intelligent behavior through learning and interaction. Exploring the intersection of machine learning and robotic control, including end-to-end learning of visual perception and robotic manipulation skills, deep reinforcement learning of general skills from autonomously collected experience, and meta-learning algorithms that can enable fast learning of new concepts and behaviors.

Information Systems & Science (ISS): Broadly construed, research in ISS focuses on the development and application of mathematical models, techniques, and algorithms for information processing. In addition to work in the core disciplines of information theory and coding, communications and networking, control and optimization, signal processing, and learning and inference, IS research spans several application areas, including biomedical imaging, optical communications, wireless communications and networks, multimedia communications, Internet, energy systems, transportation systems, financial systems, and computational imaging and display.

Subareas

• Control & Optimization
• Information Theory & Applications
• Machine Learning
• Communications Systems
• Societal Networks
• Signal Processing & Multimedia
• Biomedical Imaging
• Data Science

Control & Optimization: Optimal design and engineering systems operation methodology is applied to things like integrated circuits, vehicles and autopilots, energy systems (storage, generation, distribution, and smart devices), wireless networks, and financial trading. Optimization is also widely used in signal processing, statistics, and machine learning as a method for fitting parametric models to observed data. Examples include:

• Languages and solvers for convex optimization,
• Distributed convex optimization,
• Robotics,
• Smart grid algorithms,
• Learning via low rank models,
• Approximate dynamic programming,
• Methods for sparse signal recovery,
• Dynamic game theory,
• Control theory,
• Decentralized control,
• Imaging systems.

Machine Learning: Machine learning theory and applications. Examples include:

• Supervised learning,
• Unsupervised learning,
• Reinforcement learning,
• Applications