Muah Kim
ABOUT ME
I am a Ph.D. student working with Prof. Dr.-Ing Rafael F. SchÀfer in the Chair of Information Theory and Machine Learning at Technische UniversitÀt Dresden. Before starting my Ph.D. program, I worked at Korea Institute of Science and Technology in Europe (KIST-Europe) as a research assistant (related work). I received B.S. and M.S. degrees in electrical engineering from KAIST in 2017 and 2019 respectively. For my master's thesis, I studied coding for distributed computing with Prof. Jaekyun Moon. My research interests are how communications and machine learning enhance each other. The recent focus is to make use of deep learning for communications.
E-mail: muah.kim@tu-dresden.de / muah.kim@tu-berlin.de
Publications and News
M. Kim, R. Fritschek, R. F. Schaefer, "Applications of Diffusion Models in Communications" accepted to IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Stockholm, Sweden, May 2024.Â
M. Kim, T. Jahani-Nezhad, S. Li, R. F. Schaefer, G. Caire, "Short-Length Code Designs for Integrated Sensing and Communications using Deep Learning" accepted to IEEE International Conference on Communications (ICC), Denver, Colorado, Jun. 2024.
M. Kim, R. Fritschek, R. F. Schaefer, "Robust Generation of Channel Distributions with Diffusion Models" accepted to IEEE International Conference on Communications (ICC), Denver, Colorado, Jun. 2024.
M. Kim, R. Fritschek, R. F. Schaefer, "Learning End-to-End Channel Coding with Diffusion Models," accepted to ITGÂ Workshop on Smart Antennas and Conference on Systems, Communications, and Coding, Braunschweig, Germany, Mar. 2023. (Paper đ)
M. Kim, O. GĂŒnlĂŒ, R. F. Schaefer, "Effects of Quantization in Federated Learning with Local Differential Privacy," accepted to IEEE Global Communications Conference (Globecom), Rio de Janeiro, Brazil, Dec. 2022. (Paper đ)
O. GĂŒnlĂŒ, P. Trifonov, M. Kim, R. F. Schaefer, V. Sidorenko, "Privacy, Secrecy, and Storage with Nested Randomized Polar Subcode Constructions," accepted to IEEE Transactions on Communications, 2021. (Paper đ)
M. Kim, O. GĂŒnlĂŒ, R. F. Schaefer, "Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communications," accepted to IEEE International Conference on Acoustics, Speech, and Signal Processing  (ICASSP), Toronto, Canada, June 2021. (Paper đ, Full Version đ, Presentation Materials đȘđ°)
O. GĂŒnlĂŒ, P. Trifonov, M. Kim, R. F. Schaefer, V. R. Sidorenko, "Randomized Nested Polar Subcode Constructions for Privacy, Secrecy, and Storage," accepted to International Symposium on Information Theory and Applications (ISITA), Kapolei, Oahu, Hawaiâi, Oct. 2020. (Paper đ, Full Version đ)
M. Kim, J-y. Sohn and J. Moon, âCoded matrix multiplication on a group-based model,â accepted to IEEE International Symposium on Information Theory (ISIT), Paris, France, June 2019. (Paper đ, Full Version đ)
Teaching Experience
Dep. of Electrical Engineering and Computer Science, Technical University of Dresden, Germany
Seminar in Information Theory and Machine Learning (Obersemiar)
Intoduction to Machine Learning (EinfĂŒhrung in das maschinelle Lernen)
Dep. of Electrical Engineering and Computer Science, University of Siegen, Germany
 DKT1 Digital Communication Technology I
DMFS Digital Mobile Network
Dep. of Electrical Engineering, KAIST, South Korea
EE326 Introduction to Information Theory and Coding
EE202 Signals and Systems
Dep. of Humanity and Social Science, KAIST, South Korea                                    Â
HSS193, HSS491, HSS587 Korean for International Students Â
HSS143 Introduction to Contemporary China                   Â
HSS250 Geopolitics and Development in East Asia
HSS252 Issues in Urban and Regional Development
HSS258 Geography of International Development        Â
Honor
Korean Governmental Scholarship 2012 ~ 2013, 2017 ~ 2018
National Science and Technology Excellence Scholarship 2014 ~ 2015