Hongjoon Ahn
PhD Course in
Research topics
Continual Learning
Discrete Signal Denoising
Graph Neural Network
Reinforcement Learning
Education
PhD [2022.03~Current](Advisor: Taesup Moon)
Department of Electrical and Computer Engineering, Seoul National University (SNU)
M.S [2019.09~2022.02](Advisor: Taesup Moon)
Department of Artificial Intelligence, Sungkyunkwan University(SKKU)
B.S [2015.03~2019.08]
Electrical & Computer Engineering, Sungkyunkwan University(SKKU)
News
[2022.09.15] One paper, "Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks", has been accepted at NeurIPS 2022. This is a joint work with Amazon Web Services.
[2021.07.23] One paper, "SS-IL:Separated Softmax for Incremental Learning", has been accepted at ICCV 2021.
[2020.09.26] One paper, "Continual Learning with Node-Importance based Adaptive Group Sparse Regularization", has been accepted at NeurIPS 2020.
[2020.05.15] One paper, "Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty", has been accepted at UAI 2020.
[2019.09.03] One paper, "Uncertainty-based continual learning with adaptive regularization", has been accepted at NeurIPS 2019.
Publications
Conference paper(*=equal contribution)
Hongjoon Ahn, Yongyi Yang, Quan Gan, David Wipf*, and Taesup Moon*
Proceedings of Neural Information Processing Systems (NeurIPS), December 2022
Hongjoon Ahn*, Jihwan Kwak*, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon
Proceedings of International Conference on Computer Vision (ICCV), October, 2021
Preliminary version appeared in CVPR Workshop on Continual Learning in Computer Vision (CLVISION), June 2020
Sangwon Jung*, Hongjoon Ahn*, Sungmin Cha and Taesup Moon
Proceedings of Neural Information Processing Systems (NeurIPS), December 2020
Preliminary version appeared in CVPR Workshop on Continual Learning in Computer Vision (CLVISION), June 2020
Hongjoon Ahn and Taesup Moon
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), August 2020
Hongjoon Ahn*, Sungmin Cha*, Donggyu Lee and Taesup Moon
Proceedings of Neural Information Processing Systems (NeurIPS), December 2019
Journal paper
[J1] Prediction Model for Random Variation in FinFET Induced by Line-Edge-Roughness (LER)
Jinwoong Lee, Taeeon Park, Hongjoon Ahn, Jihwan Kwak, Taesup Moon, Changhwan Shin
Electronics, February 2021
[J2] GAN-Based Framework for Unified Estimation of Process-Induced Random Variation in FinFET
Taeeon Park, Jihwan Kwak, Hongjoon Ahn, Jinwoong Lee, Jaehyuk Lim, Sangho Yu, Changhwan Shin, and Taesup Moon
IEEE Acess, 2022
Experience
Applied Scientist internship
Mentor: David Wipf
Working with Amazon AI Research team in Shanghai
Awards and Scholarship
[2022.07] Youlchon AI Star Fellowship
[2019.09~2021.08] Scholarship for graduated school student (SKKU)
[2020.06~2021.12] Kwanjeong Scholarship (for M.S degree)
[2020.09] Qualcomm Innovation Fellowship Korea
Awarded for the "Continual Learning with Node-Importance based Adaptive Group Sparse Regularization"