|
donghoonlee [at] kaist.ac.kr I am a Ph.D. candidate at KAIST, advised by Prof. Seunghoon Hong, expected to graduate in February 2026. I am broadly interested in making vision models more efficient—faster, lighter, and cheaper—by reducing data requirements and computational costs. I am also interested in representation learning without supervision. Google Scholar / Github / CV |
|
|
Whie Jung, Dong Hoon Lee, Seunghoon Hong Advances in Neural Information Processing Systems (NeurIPS), 2025. pdf / code |
|
Kiet T Nguyen, Chanhyuk Lee, Donggyun Kim, Dong Hoon Lee, Seunghoon Hong Advances in Neural Information Processing Systems (NeurIPS), 2025. pdf / code |
|
Dong Hoon Lee, Seunghoon Hong Advances in Neural Information Processing Systems (NeurIPS), 2024. pdf / code |
|
Dong Hoon Lee, Sungik Choi, Hyunwoo Kim and Sae-Young Chung Advances in Neural Information Processing Systems (NeurIPS), 2022. pdf / code |
|
Dong Hoon Lee and Sae-Young Chung Proceedings of the 38th International Conference on Machine Learning (ICML), 2021. pdf / code |
|
2018~ current: Ph.D. student in the School of AI, KAIST, Daejeon, Korea 2016~2018: M.S. in Electrical Engineering, KAIST, Daejeon, Korea 2012~2016: B.S. in Electrical Engineering, KAIST, Daejeon, Korea 2009~2011: Korea Science Academy, Busan, Korea |
|
Research Intern, LG AI Research, Seoul, Korea, 2022 |
|
NeurIPS 2022 Scholar Award, 2022 Qualcomm Innovation Fellowship, 2021 South Korea Finalist, 2021 Korea Government Fellowship, March 2021 to present |
|
2019 fall: TA, EE807 Special Topics in Electrical Engineering. Deep Reinforcement Learning and AlphaGo, KAIST. 2019 spring : TA, EE405 Electronics Design Lab. Network of Smart Things, KAIST. 2018 fall: TA, EE405 Electronics Design Lab. Robocam, KAIST. 2018 spring: TA, EE807 Special Topics in Electrical Engineering. Mathematical Foundation of Reinforcement Learning, KAIST. 2017 spring: TA, EE210 Probability and Introductory Random Processes, KAIST. |
|
Website template from here. |