Changho Suh

Changho Suh 

Professor
School of Electrical Engineering
KAIST

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

+82-42-350-7429
chsuh@kaist.ac.kr
Google Scholar Profile

News (archive)

(Jan. 2024) My books on convex optimiztion and information theory are featured in the College of Engineering, KAIST (article)

(Dec. 2023) Appointed as Treasurer for IEEE Information Theory Society Board of Governors (2024 to 2026).

(Dec. 2023) Call for Contributions for IEEE Information Theory Newsletter

  • Deadline: December 15th

  • Share any ideas, initiatives, or potential contributions you may have in mind

  • Send contributions to the Newsletter Editor, Changho Suh <chsuh@kaist.ac.kr>

(Nov. 2023) A new textbook will be published in 2024:

  • C. Suh, ‘‘Probability for Information Technology,’’ Springer.

(Oct. 2023) A paper accepted in the IEEE Transactions on Information Theory:

(Oct. 2023) Delivered a lecture at KAIST AI Graduate School CAIO program:

  • Fair AI and applications

(Sep. 2023) Promoted to Professor.

(Sep. 2023) IEEE Information Theory Newsletter September Issue

(Aug. 2023) Received the Teaching Award from Hyundai Motor.

(July 2023) Invited talk at EIRIC AI seminar:

  • Fair AI and applicaitons

(June 2023) IEEE Information Theory Newsletter June Issue

(June 2023) A new textbook published (Springer):

communication 

Communication Principles for Data Science

(May 2023) Delivered an AI course at KAIST AI Graduate School CAIO program:

  • Fair AI and applications

(Apr. 2023) A paper accepted in ICML 2023:

  • Y. Roh, K. Lee, S. Whang and C. Suh, ‘‘Improving fair training under correlation shifts’’

(Apr. 2023) A new textbook published (Now Publishers):

book_IT 

Information Theory for Data Science
Supported by the Google Education Grant
open access link

(Mar. 2023) IEEE Information Theory Newsletter March Issue

(Dec. 2022 ) KAIST AI Institute published a new book on social AI

  • Participated as a book chapter author

(Dec. 2022) Published an article in KAST next-generation report:

(Dec. 2022) IEEE Information Theory Newsletter December Issue

(Nov. 2022) Elevated to IEEE Fellow:

  • For contributions to interference management and distributed storage codes

(Nov. 2022) A new textbook published (Now Publishers):

convex 

Convex Optimization for Machine Learning
Supported by the Google Education Grant
open access link

(Nov. 2022) Delivered a lecture at KAIST AI Graduate School CAIO program:

  • Fair AI and applications

(Nov. 2022) Delivered an invited talk at Hyundai Motor AI conference:

  • AI for manufacturing

(Aug. 2022) Received the Google Research Award on:

  • Fairness under correlation shifts

Selected funding agencies

Selected publications

  1. J. Cho, G. Hwang and C. Suh, “A fair classifier using kernel density estimation,” NeurIPS, Dec. 2020 (Top 10 KAIST Research Achievements).

  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, IEIE Haedong Young Engineer Award).

  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).