Kim – Research Overview

My research is focused on information theory and statistical signal processing, with applications in communication, control, computation, networking, data compression, and learning. On a less serious note, I am interested in fun mathematical puzzles, which my research group discusses routinely during weekly seminars; see also a course on puzzles I taught in Fall 2011.

Current (ever-evolving) research interests are as follows:

Network information theory

Directed cutset bound: The fundamental limit on network information flow

• A short paper (Kamath–Kim 2014)

• A 20-minute presentation at IEEE ITW 2015

Index coding: The canonical network communication problem

• A short paper on composite coding (Arbabjolfaei et al. 2013)

• A 30-minute presentation at Banff Workshop 2015

• A page on optimal broadcast rates and capacity regions

Optimal relaying: The instruction manual for network building blocks

• A paper on noisy network coding (Lim et al. 2011)

• A short paper on distributed decode–forward (Lim et al. 2014)

• A short paper on interactive relaying (Xiang 2011)

• A 3-hour tutorial at the 2015 European Schools of Information Theory

• A web art on relaying schemes

Communication theory

Monte Carlo decoding: A novel randomized decoding for high performance and low complexity

• A short presentation at the 2018 Allerton Conference

Sliding-window coded modulation (SWCM): A practical interference mitigation technique from information theory

• A paper on theoretical aspects of SWCM (Wang et al. 2017)

• A paper on implementation aspects of SWCM (Kim et al. 2016)

• A 1-hour presentation at the 2015 IEEE CTW

Information theory for cellular wireless

• A 1-hour presentation on coded modulation at the 2018 Tel Aviv ACC Workshop

Data science (information theory + learning theory)

Directed information: The ‘‘right’’ answer to many problems on causality

• A paper on basic interpretations (Permuter et al. 2011)

• A 1-hour presentation at SNU Economics (2013)

Universal information processing: How to get something out of nothing

• A paper on universal estimation of mutual and directed information (Jiao et al. 2013)

• A 3-hour tutorial at the PRML Summer School in Korea (2013)

• A 1-hour presentation at the Munich Workshop on Causal Inference and Information Theory (2016)

• A course on universal information processing

• An application to grayscale image denoising (Ryu–Kim 2018)