Changho Suh
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Associate Professor Room 912, N1 building +82-42-350-7429 |
Schedule
Teaching schedule for Spring 2020: EE321 Communication Engineering, Wednesdays and Fridays 13:00 - 14:15
Office hours: Wednesdays and Fridays 14:30 - 15:30 (during academic periods)
News
(Mar. 2020) Elevated to IEEE Senior member.
(Mar. 2020) Organizer of Seongnam-KAIST Machine Learning Course (to be held in May):
Day 1: Machine learning with sckitlearn
Day 2: Deep learning with tensorflow
Day 3: Convolutional neural networks
Day 4: Recurrent neural networks
(Mar. 2020) Invited talk at University of Minnesota ECE:
“Matrix completion with graph side information”
(Feb. 2020) Delivered an invited talk at UC Irvine EECS:
“Matrix completion with graph side information”
(Feb. 2020) Delivered an invited talk at ITA:
“A fair classifier using mutual information”
(Jan. 2020) Received 4.86/5.0 evaluation score for EE623: Information Theory (Fall 2019)
(Jan. 2020) Received the KAIST Breakthroughs Readers’ Choice Award (article).
(Dec. 2019) Elected as a Member of Young Korean Academy of Science and Technology (Y-KAST).
(Dec. 2019) Director of SK-Hynix Machine Learning Course 2020.
(Nov. 2019) Served as an SPC member of IJCAI 2020 and a TPC member of ISIT 2020.
(Nov. 2019) Received the Google Education Grant.
(Oct. 2019) Appointed as a Distinguished Lecturer for the IEEE Information Theory Society (2020-21)
(Oct. 2019) A paper accepted in NeurIPS Workshop:
D. Kim, K. Lee and C. Suh, “Improving model robustness via automatically incorporating self-supervision tasks”
(Sep. 2019) Won the Department Teaching Award:
EE523: Convex Optimization (Spring 2019, evaluation score: 4.63/5)
(Sep. 2019) Delivered two-day lectures for Samsung Machine Learning Course:
Day 1: Convolutional neural networks
Day 2: Recurrent neural networks
(Aug. 2019) A paper accepted in Allerton:
J. Cho and C. Suh, “Wasserstein GAN can perform PCA”
(July 2019) A paper accepted in IT:
S. Shin and C. Suh, “Two-way function computation”
(June/July 2019) Delivered three-day lectures for SK-Hynix Machine Learning Course:
Day 1: Machine learning and optimization
Day 2: Deep learning and optimization
Day 3: Robustness and fairness
(June 2019) Delivered invited talks at NUS:
“Learn to identify dangerous vehicles using a computer game”
“Matrix completion with graph side information”
(May 2019) Delivered an invited talk at LSIT (video):
“Matrix completion with graph side information”
(May 2019) A paper accepted in IT:
K. Ahn, K. Lee and C. Suh, “Community recovery in hypergraphs”
(Apr. 2019) Received a two-year grant from Air Force Office of Scientific Research (AFOSR):
“Validating simulator-based learning via interpretation”
(Mar. 2019) Delivered an invited talk at Samsung Medical Center:
“Search engine for medical images”
(Mar. 2019) Our work on self-driving systems is recognized as:
Feature article in KAIST Breakthroughs
A paper award at JCCI
(Mar. 2019) A paper published in Quantum Information Processing:
K. Lim, C. Suh and J.K.K. Rhee, “Longer distance continuous variable quantum key distribution protocol with photon subtraction at receiver”
(Feb. 2019) Delivered an invited talk at AOARD:
“Interpretable collision avoidance systems for autonomous driving”
(Feb. 2019) Delivered an invited talk at ITA:
M. Jang, S. Kim and C. Suh, “Match prediction from group-wise comparisons”
(Dec. 2018) Received KAIST Technology Innovation Award
(Nov. 2018) A paper accepted in AAAI (oral presentation):
H. Kim, K. Lee, G. Hwang and C. Suh, “Crash to not crash: Learn to identify dangerous vehicles using a simulator” (website)
(Sep. 2018) A paper accepted in NeurIPS:
K. Ahn, K. Lee, H. Cha and C. Suh, “Binary rating estimation with graph side information”
Acknowledgments
Selected publications
H. Kim, K. Lee, G. Hwang and C. Suh, “Crash to not crash: Learn to identify dangerous vehicles using a simulator”, AAAI, Jan. 2019 (oral presentation, website).
K. Ahn, K. Lee, H. Cha and C. Suh, “Binary rating estimation with graph side information”, NeurIPS, 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, June 2018.
S. Mohajer, C. Suh and A. Elmahdy, “Active learning for top-K rank aggregation from noisy comparisons”, International Conference on Machine Learning, Sydney, Australia, Aug. 2017.
Y. Chen, G. Kamath, C. Suh and D. Tse, “Community recovery in graphs with locality”, International Conference on Machine Learning, New York, USA, June 2016.
Y. Chen and C. Suh, "Spectral MLE: Top-K rank aggregation from pairwise comparisons”, International Conference on Machine Learning, Lille, France, July 2015 (Bell Labs Prize finalist).
V. Cadambe, S. Jafar, H. Maleki, K. Ramchandran and C. Suh, “Asymptotic interference alignment for optimal repair of MDS codes in distributed storage”, IEEE Transactions on Information Theory, vol. 59, no. 5, pp. 2974–2987, May 2013.
C. Suh, M. Ho and D. Tse, “Downlink interference alignment”, IEEE Transactions on Communications, vol. 59, no. 9, pp. 2616–2626, Sep. 2011 (won the 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 (The conference version won the Best Student Paper Award of the conference).
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 (The conference version won the Best Student Paper Award finalist of the conference).
A. G. Dimakis, K. Ramchandran, Y. Wu and C. Suh, “A survey on network codes for distributed storage”, Proceedings of the IEEE, vol. 99, no. 3, pp. 476–489, Mar. 2011.