Young-Han Kim primarily works on two important challenges for today's high-speed, high-volume information processing systems -- how to describe information efficiently and how to transmit it reliably in the presence of noise and interference. With the ultimate goal of providing guidelines that can be put into practice, Dr. Kim explores fundamental principles behind a variety of applications in communication, networking, compression, prediction, and data storage. For example, he studies the role of feedback in communication networks, searching for new ways of utilizing feedback to improve system performance. In his recent work on Gaussian channels, which are the most popular models for real communication channels that can be mathematically analyzed, Young-Han Kim not only solved the long-standing open problem of finding the optimal feedback communication method, but also showed an interesting connection between control, estimation, and communication. In a broader context, Kim approaches problems of efficient and robust information flow in networks using an array of mathematical tools from statistical signal processing, control theory, convex optimization, and information theory. His other research interests include statistical inference, learning theory, and quantum information processing.