Hyungseok Song
Contact me: 7590sok@gmail.com
About Me
I am a research scientist in LG AI Research. I received Ph.D. in the School of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST), advised by Prof. Yung Yi, in 2022. I received M.S. and B.S. in Mathematics advised by Prof. Wanmo Kang from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea, in 2016 and 2014, respectively.
My research interests lie in applying reinforcement learning into recommendation systems. Followings are my recent/past research topics with brief descriptions.
Deep Reinforcement Learning for Large-Scaled Combinatorial Selection - sample efficient learning algorithms based on Deep Q-network when the action space is huge from the combinatorial selection.
Mathematical Analysis for Weight Shared Neural Network - mathematical analysis of weight shared neural networks which proves that they do not lose their expressive power during weight sharing.
Learning and Inference of Graphical Models (with considering communication cost) - structure learning algorithm for graphical models with consideration of communication cost.
Publication
Preprints
[P1] Hyungseok Song, Yung Yi, and Seyoung Yun, "Gambling Multi-armed bandit", submitted for conference review, 2022.
Journal
[J2] Hyeryung Jang, Hyungseok Song, and Yung Yi "On Cost-efficient Learning of Data Dependency" at IEEE/ACM Transactions on Networking (TON), 2022. (Part of this work has been published MOBIHOC 2018)
[J1] Daewoo Kim, Hyojung Lee, Hyungseok Song, Nakjung Choi, and Yung Yi, "Economics of Fog Computing: Interplay among Infrastructure and Service Providers, Users, and Edge Resource Owners" IEEE Transactions on Mobile Computing (TMC), PP(99):1-1, July 2019. (Part of this work has been published ICC 2018)
Conference
[C4] Se-eun Yoon, Hyungseok Song, Kijung Shin and Yung Yi, "How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction", to appear at The Web Conference (WWW), 2020.
[C3] Hyungseok Song, Hyeryung Jang, Hai Hong Tran, Seeun Yoon, Kyunghwan Son, Donggyu Yun, Hyoju Chung, and Yung Yi, "Solving Continual Combinatorial Selection via Deep Reinforcement Learning,", to appear at International Joint Conference on Artificial Intelligence (IJCAI), 2019. (Acceptance rate: 17.8%)
[C2] Hyeryung Jang, HyungSeok Song, and Yung Yi, "Learning Data Dependency with Communication Cost," in Proceedings of ACM Mobile Ad Hoc Networking and Computing (MOBIHOC), 2018. (Acceptance rate: 16.8%) (arXiv preprint version is available at arXiv:1804.10942) [slide]
[C1] Daewoo Kim, Hyojung Lee, Hyungseok Song, Nakjung Choi, and Yung Yi, “On the Economics of Fog Computing: Inter-play among Infrastructure and Service Providers, Users, and Edge Resource Owners”, Proceedings of IEEE ICC 2018 [slide]
Thesis
[T1] Hyungseok Song, "Importance Sampling for multifactor portfolio credit risk in t-copula model" M.S. Thesis in Mathematics from Korea Advanced Institute of Science and Technology (KAIST), 2016. (Advisor: Prof. Wanmo Kang)
History
Career
Research engineer, Data Intelligence Lab, LG AI Research, 2022.03 ~ Present
Education
Ph.D: Electrical Engineering, KAIST, 2016.3~2022.02 (under supervision of Prof. Yung Yi)
M.S: Department of Mathematical Sciences, KAIST, 2014.3~2016.2 (under supervision of Prof. Wanmo Kang)
B.S: Department of Mathematical Sciences, KAIST, 2010.3~2014.2
Highschool: Korea Science Academy (KSA), 2007.3~2010.2
Honors and Awards
Samsung Humantech Thesis Prize Awards: Encouragement Prize, 2018
BK21 Plus Scholarship, Republic of Korea, 2017~Present
Government Scholarship, Republic of Korea, 2014~Present
Project Experiences
2018.09~2019.12: Research on Predicting Economic Indicator from Electronic Power Index, Korea Electric Power Corporation (Kepco), Republic of Korea
Analyze the pattern between the economic index and the electronic power index via machine learning method.
Design sample efficient deep learning algorithm to handle the lack of the economic indexes.
2017.07~2018.08: Research on Learning-based Service Improvement Framework in Large-scale Online Request System, Naver, Republic of Korea
Design and analyze Reinforcement Learning based service improvement framework in large-scale online request system
Implement deep reinforcement learning algorithm for service improvement in large-scale online request system
Teaching Experiences
Teaching Assistant, Special Topics in Electronic Engineering I<My Life and Career in EE> (EE485), KAIST, Fall 2017, Spring 2018
Teaching Assistant, Calculus I<My Life and Career in EE> (MAS101), KAIST, Spring, 2016
Teaching Assistant, Probability and Statistics (CC511), KAIST, Spring, 2016
Teaching Assistant, Probability and Statistics (MAS250), KAIST, Fall 2014, Spring 2015, Fall 2015
Teaching Assistant, Analysis for Engineers (MAS501), KAIST, Spring 2015
Reference Available on Request
Professor Yung Yi: Professor at the Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), South Korea, yiyung at kaist dot edu
Professor Wanmo Kang: Associate Professor at the Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology (KAIST), South Korea, wanmo dot kang at kaist dot edu