Changho Suh

Changho Suh 

Associate Professor
School of Electrical Engineering
Korea Advanced Institute of Science and Technology

Room 912, N1 building
291 Daehak-ro, Yuseong-gu
Daejeon, South Korea, 34141

Google Scholar Profile


Teaching schedule for Spring 2021: EE210 Probability and Introductory Random Processes, Tuesdays and Thursdays 9:00 – 10:15
Office hours: Tuesdays and Thursdays 10:30 – 11:30 (during academic periods)


(Jan. 2021) Top 10 KAIST Research Highlights of 2020 (KAIST Annual R&D Report):

  • Fair machine learning

(Jan. 2021) Recent papers accepted:

(Jan. 2021) Feedback from EE424 Introduction to Optimization (Fall 2020)

  • Evaluation score: 4.79/5

  • Number of registered students: 102

(Dec. 2020) Media exposure:

(Dec. 2020) Panelist of KAST round table:

  • Qualitative evaulation of faculty (youtube)

(Nov. 2020) Editor for the IEEE Information Theory Newsletter.

(2020) Recognition

(2020) AI and Machine Learning Courses

  • Dec: Hyundai Motor Machine Learning Course (10 days)

  • June ~ Nov: SK-Hynix Machine Learning Course (16 days)

  • Oct: Hyundai Motor Machine Learning Course (10 days)

  • Aug: Seongnam-KAIST Machine Learning Course (2 days)

  • July: AI Camp for middle/high school teachers (1 day)

  • May: Seongnam-KAIST Machine Learning Course (2 days)

(2020) Talks

  • Nov: Inspirational talk at KAIST (“Three stories for freshmen” youtube)

  • Oct: Invited talk at SK-Hynix (“SECDED and multi-bit ECC for DRAM”)

  • Oct: Invited talk at LG Display AI Workshop (“Machine learning with small data”)

  • Sep: Invited talk at Edtech Korea Forum (“AI fundamentals & curriculums” youtube-korean, english)

  • July: Invited talk at SPCOM (“FR-Train: Fair and robust training via information theory”)

  • Mar: Invited talk at University of Minnesota ECE (“Fundamental limits and efficient algorithms for machine learning”)

  • Feb: Invited talk at UC Irvine EECS (“Matrix completion with graph side information”)

  • Feb: Invited talk at ITA (“A fair classifier using mutual information”)

(2020) Papers

(2019) Recognition


Selected publications

  1. Y. Roh, K. Lee, S. E. Whang and C. Suh, “FR-Train: A mutual information-based approach to fair and robust training,” ICML, Vienna, Austria, July 2020.

  2. H. Kim, K. Lee, G. Hwang and C. Suh, “Crash to not crash: Learn to identify dangerous vehicles using a simulator,” AAAI, Honolulu, USA, Jan. 2019 (oral presentation, website, article, media).

  3. K. Ahn, K. Lee, H. Cha and C. Suh, “Binary rating estimation with graph side information,” NeurIPS, Montreal, Canada, Dec. 2018.

  4. C. Suh, J. Cho and D. Tse, “Two-way interference channel capacity: How to have the cake and eat it too,” IEEE Transactions on Information Theory, vol. 64, no. 6, pp. 4259–4281, June 2018.

  5. S. Mohajer, C. Suh and A. Elmahdy, “Active learning for top-K rank aggregation from noisy comparisons,” ICML, Sydney, Australia, Aug. 2017.

  6. Y. Chen, G. Kamath, C. Suh and D. Tse, “Community recovery in graphs with locality,” ICML, New York, USA, June 2016.

  7. Y. Chen and C. Suh, “Spectral MLE: Top-K rank aggregation from pairwise comparisons,” ICML, Lille, France, July 2015 (Bell Labs Prize finalist).

  8. C. Suh, M. Ho and D. Tse, “Downlink interference alignment,” IEEE Transactions on Communications, vol. 59, no. 9, pp. 2616–2626, Sep. 2011 (IEEE Communications Society Stephen O. Rice Prize in 2013).

  9. C. Suh and D. Tse, “Feedback capacity of the Gaussian interference channel to within 2 bits,” IEEE Transactions on Information Theory, vol. 57, no. 5, pp. 2667–2685, May 2011 (IEEE ISIT Best Student Paper Award).

  10. C. Suh and K. Ramchandran, “Exact-repair MDS code construction using interference alignment,” IEEE Transactions on Information Theory, vol. 57, no. 3, pp. 1425–1442, Mar. 2011 (IEEE ISIT Best Student Paper Award finalist).