Communications, Information Theory, and Machine Learning

Faculty within the Communications area focus on modern aspects of information acquisition, processing, dynamics, security, storage, and communication. Beyond problems arising in traditional wireless and wireline communication systems, we are interested in designing and analyzing modern data science techniques for applications in security and privacy in big data analysis, active decision making and control, machine learning for wireless channel modeling and learning, the use of coding and information to mitigate bottlenecks in the training of neural networks, design and implementation of deep neural networks in hardware, design and control of large scale wireless networks, data clustering, and wireless channel prediction.

We offer core courses that cover mathematical and physical foundations, fundamental design principles, and practical algorithms that underlie modern communication, computing, and information systems. These courses include probability and random processes; digital communications and coding; wireless, mobile, and optical communication systems; information theory, estimation theory, and inference. In addition to foundational courses on communications, information theory and random processes, we also teach special topics courses on bleeding edge research areas.  Recent offerings include:  5G wireless systems, quantum information theory, quantum algorithms, social networks, coding for distributed storage, sparse approximation and compressed sensing,  coding for distributed storage and underwater acoustic communication systems.