Changho Suh

Associate 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

Schedule

Teaching schedule for Spring 2022:

• KAIST Life Academy, Friday 14:00 – 18:00

• HSS090 Happy College Life, Thursday 19:00 – 21:00

Office hours: Thursday 15:00 – 17:00 (during academic periods)

News (archive)

(June 2022) Received the Google Initiated Award on:

• Fairness under correlation shifts

(June 2022) Organizer of AI World 2022: Tech & Future:

• past events: AI World 2021, AI World 2020

(June 2022) Acknowledged during the ISIT 2022 Awards Ceremony for Recognition of:

• General Chair of the 2021 IEEE East Asian School of Information Theory

(May 2022) Delivered an invited talk at KAIST AI Graduate School Colloquium:

• AI and fairness

(May 2022) Delivered an invited talk at AI Frontiers Summit:

• Fair AI and applications

(May 2022) A paper accepted in IEEE Transactions on Signal Processing:

• Q. Zhang, G. Suh, C. Suh and V.Y.F. Tan, ‘‘MC2G: An efficient algorithm for matrix completion with social and item similarity graphs’’

(May 2022) Call for Contributions for IEEE Information Theory Newsletter

• Deadline: May 10

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

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

(Apr. 2022) Two papers accepted in ISIT2022:

• G. Suh and C. Suh, ‘‘Graph-assisted matrix completion in a multi-clustered graph model’’

• A. Elmahdy, J. Ahn, S. Mohajer and C. Suh, ‘‘The optimal sample complexity of matrix completion with hierarchical similarity graphs’’

(Apr. 2022) Launched the inaugural issue of the online Information Theory Society Newsletter:

• Welcome message from the Editor

• Questions should be directed to me at newsletter@itsoc.org (or chsuh@kaist.ac.kr).

(Apr. 2022) Will deliver an AI course at KAIST AI Graduate School CAIO program:

• Fair AI and applications

(Mar. 2022) Area Chair of NeurIPS 2022

(Mar. 2022) EE424 Introduction to Optimization (Fall 2021)

• Received the Department Teaching Award (evaluation score: 4.76/5.0)

• Department TA Award (Moonseok Choi)

(Feb. 2022) Delivered an invited talk at KAIST AI Institute:

• AI and fairness: Engineer's perspective (slides)

(Jan. 2022) Guest Editor of the IEEE Journal on Selected Areas in Information Theory

(Jan. 2022) Executive Director of IEIE

(Dec. 2021) KAIST International Symposium on AI and Future Society:

• Chaired a session on AI and fairness (youtube)

(Dec. 2021) Open KAIST: Featured as a representative of School of Electrical Engineering (youtube)

(Dec. 2021) IEEE Information Theory Newsletter December Issue

(Nov. 2021) YTN Science:

• Science Special: Mad for science (youtube, article)

(Oct. 2021) Delivered an invited talk at National Human Resources Development Institute:

• AI and societal issues (slides)

(Oct. 2021) Published an article in KAST next-generation report

• Thinking cars (article)

(Oct. 2021) YTN Science:

• Science Forum: Thinking cars (youtube, article)

(Oct. 2021) Sisajournal:

• Selected as one of the 100 next generation leaders

(Oct. 2021) Guest Editor of the IEEE Journal on Selected Areas in Information Theory (JSAIT):

• Distributed coding and computation

(Oct. 2021) Delivered an invited talk at KAST round table:

• Ethics for self-driving cars (youtube, slides)

(Oct. 2021) A paper accepted in NeurIPS:

• Y. Ro, K. Lee, S. E. Whang and C. Suh, ‘‘Sample selection for fair and robust training’’

Selected funding agencies

• Air Force Office of Scientific Research (AFOSR)

• Google

• National Research Foundation of Korea

• Ministry of Science, ICT & Future Planning

• KaKao

• Samsung

• SKT

• SK-Hynix

• LG

• Hyundai NGV

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).