Changho Suh

Changho Suh is a Faculty member in the School of Electrical Engineering at KAIST. He holds adjunct professor positions at NYU and the Kim Jaechul Graduate School of AI, as well as the Semiconductor System Engineering department at KAIST. His research interests are aligned with the Electrical Engineering department, and he is affiliated with the KAIST AI Institute. Suh has contributed to various academic publications and has delivered lectures at multiple programs, including the KAIST AI Business Transformation Program and the CAIO program. He has also been recognized with a Department Teaching Award and has published several textbooks on topics such as probability, communication principles, and convex optimization. Additionally, he has been appointed as Treasurer for the IEEE Information Theory Society Board of Governors.

Changho Suh is a Professor of Electrical Engineering at KAIST, specializing in Information Theory and Machine Learning. His research focuses on network coding for distributed storage, interference alignment in cellular networks, and exact-repair MDS code construction. He has also explored high-dimensional coded matrix multiplication and fairbatch for model fairness. Additionally, his work includes resource allocation for multicast services, feedback capacity of Gaussian interference channels, and spectral MLE for rank aggregation. Suh's contributions span both theoretical and applied aspects of communication systems and machine learning, with an emphasis on improving efficiency and fairness in data transmission and model training.