µSnap: Embracing Traditional Programming Models for Persistent Memory through OS Support. - J. Hyun Kim; Young Je Moon; Hyunsub Song; Jay H. Park; Sam H. Noh (NVMSA, 2018)

Accelerated Training for CNN Distributed Deep Learning through Automatic Resource-Aware Layer Placement. - Jay H. Park; Sunghwan Kim; Jinwon Lee; Myeongjae Jeon; Sam H. Noh (CoRR, 2019)

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism. - Jay H. Park; Gyeongchan Yun; Chang M. Yi; Nguyen T. Nguyen; Seungmin Lee; Jaesik Choi; Sam H. Noh; Young-ri Choi (USENIX ATC, 2020)

On Providing OS Support to Allow Transparent Use of Traditional Programming Models for Persistent Memory. - J. Hyun Kim; Young Je Moon; Hyunsub Song; Jay H. Park; Sam H. Noh (ACM J. Emerg. Technol. Comput. Syst., 2020)

FlashVault: Versatile In-NAND Self-Encryption with Zero Area Overhead. - Seock-Hwan Noh; Hoyeon Lee; Junkyum Kim; Junsu Im; Jay H. Park; Sungjin Lee 001; Sam H. Noh; Yeseong Kim; Jaeha Kung (CoRR, 2025)

Accelerating LLM Inference with Precomputed Query Storage. - Jay H. Park; Youngju Cho; Choungsol Lee; Moonwook Oh; Euiseong Seo (CoRR, 2025)