< back

California Institute for Telecommunications and Information Technology


(858) 534-4254


< back
Young-Han Kim

Statistical signal processing, information theory, communication, networking, data compression, information processing

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, he 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.

Professor Young-Han Kim received his B.S. degree with honors in
Electrical Engineering from Seoul National University, Korea, where he
was a recipient of the General Electric Foundation Scholarship. After a
three-year stint as a software architect for building Korea's newly
opening Incheon International Airport, he resumed his graduate studies
at Stanford University and received his Ph.D. degree in Electrical
Engineering (M.S. degrees in Statistics and Electrical Engineering) in
June 2006. His Ph.D. thesis elucidates the role of feedback in
communication and in particular, resolves the long standing open problem
of finding the feedback capacity of Gaussian channels. More broadly,
Professor Kim is interested in statistical signal processing and
information theory, with applications in communication, networking, data
compression, and learning.