Sangjin Choi

About

I am a Ph.D. student advised by Youngjin Kwon. My research focuses on building efficient ML systems.

Pruning Speculative Decoding Sparse Attention Energy Efficient Training GPU Driver

Education

Experience

Publications

MLSys '26
BEAM: Joint Resource–Power Optimization for Energy-Efficient LLM Inference under SLO Constraints
Hyunjae Lee, Sangjin Choi, Seungjae Lim, Youngjin Kwon
EuroSys '26
MTTM: Dynamic Fast Memory Partitioning with Bandwidth Optimization for Multi-tenant Cloud
Changjun Lee, Sangjin Choi, Youngjin Kwon
Findings of NAACL '25
Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding
Sukmin Cho, Sangjin Choi, Taeho Hwang, Jeongyeon Seo, Soyeong Jeong, Huije Lee, Hoyun Song, Jong C. Park, Youngjin Kwon
ICCAD '23
PRIMO: A Full-Stack Processing-in-DRAM Emulation Framework for Machine Learning Workloads
Jaehoon Heo, Yongwon Shin, Sangjin Choi, Sungwoong Yune, Jung-Hoon Kim, Hyojin Sung, Youngjin Kwon, Joo-Young Kim
USENIX ATC '23
EnvPipe: Performance-preserving DNN Training Framework for Saving Energy
Sangjin Choi, Inhoe Koo, Jeongseob Ahn, Myeongjae Jeon, Youngjin Kwon
USENIX ATC '22
Memory Harvesting in Multi-GPU Systems with Hierarchical Unified Virtual Memory
Sangjin Choi*, Taeksoo Kim*, Jinwoo Jeong, Rachata Ausavarungniurn, Myeongjae Jeon, Youngjin Kwon, Jeongseob Ahn (*equal contribution)