Changho Suh
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Professor Room 912, N1 building +82-42-350-7429 |
News (archive)
(Oct. 2023) A paper accepted in the IEEE Transactions on Information Theory:
J. Ahn, A. Elmahdy, S. Mohajer and C. Suh, ‘‘On the fundamental limits of matrix completion: Leveraging hierarchical similarity graphs’’
(Oct. 2023) Delivered an AI course 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):
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(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):
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Information Theory for Data Science |
(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:
‘‘Responsible AI’’ (website)
(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):
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Convex Optimization for Machine Learning |
(Nov. 2022) Delivered an AI course 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
J. Cho, G. Hwang and C. Suh, “A fair classifier using kernel density estimation,” NeurIPS, Dec. 2020 (Top 10 KAIST Research Achievements).
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).
K. Ahn, K. Lee, H. Cha and C. Suh, “Binary rating estimation with graph side information,” NeurIPS, Montreal, Canada, Dec. 2018.
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.
S. Mohajer, C. Suh and A. Elmahdy, “Active learning for top-K rank aggregation from noisy comparisons,” ICML, Sydney, Australia, Aug. 2017.
Y. Chen, G. Kamath, C. Suh and D. Tse, “Community recovery in graphs with locality,” ICML, New York, USA, June 2016.
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).
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).
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).
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).
