Changwoo Lee

I am a fifth year Ph. D. student in the department of Electrical and Computer Engineering at the University of Michigan (U of M), advised by Prof. Hun-Seok Kim. Before I join U of M, I received my M.S. and B.S. at Hanyang University, Seoul, Republic of Korea.

My research is centered around efficient Deep Learning and Machine Learning algorithms, including model compression, structured matrices, and efficient multi-modal temporal and sensor fusion models.

Some fun facts about myself: I love running and brewing coffee. I also enjoy listening to Jazz music.

[CV] [Google Scholar]

news

May 12, 2025	I joined Google DeepMind as a Research Intern during Summer 2025 in Mountain View. Excited to do research with amazing people!
Sep 25, 2024	A paper “BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference” is accepted to NeurIPS24.
May 28, 2024	I joined Samsung Research America at Mountain View as NLP/ML Research Intern during Summer 2024.
Jan 16, 2024	Paper on “Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks” is accepted to ICLR24.
Jan 20, 2023	Paper on “Deep Joint Source-Channel Coding with Iterative Source Error Correction” is accepted to AISTATS23.

latest posts

Jan 22, 2024	Gaudi-GBLR: Differentiable, and Generalized Block Low-Rank Matrix Format for Efficient DNNs

selected publications

NeurIPS24

BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference

Changwoo Lee, Soo Min Kwon, Qing Qu, and Hun-Seok Kim

In The 38th Annual Conference on Neural Information Processing Systems, 2024

arXiv Bib Code

@inproceedings{lee2024blast,
  title = {BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference},
  author = {Lee, Changwoo and Kwon, Soo Min and Qu, Qing and Kim, Hun-Seok},
  booktitle = {The 38th Annual Conference on Neural Information Processing Systems},
  year = {2024},
}

ICLR24

Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks

Changwoo Lee, and Hun-Seok Kim

In The Twelfth International Conference on Learning Representations, 2024

arXiv Bib Code

@inproceedings{lee2023differentiable,
  title = {Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks},
  author = {Lee, Changwoo and Kim, Hun-Seok},
  booktitle = {The Twelfth International Conference on Learning Representations},
  year = {2024}
}

ISCA23

TaskFusion: An Efficient Transfer Learning Architecture with Dual Delta Sparsity for Multi-Task Natural Language Processing

Zichen Fan, Qirui Zhang, Pierre Abillama, Sara Shoouri, Changwoo Lee, David Blaauw, and 2 more authors

In Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023
TCOM

Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission

Chenghong Bian, Chin-Wei Hsu, Changwoo Lee, and Hun-Seok Kim

IEEE Transactions on Communications, 2023

arXiv

AISTATS23

Deep Joint Source-Channel Coding with Iterative Source Error Correction

Changwoo Lee, Xiao Hu, and Hun-Seok Kim

In International Conference on Artificial Intelligence and Statistics, 2023

arXiv Bib Code

@inproceedings{lee2023deep,
  title = {Deep Joint Source-Channel Coding with Iterative Source Error Correction},
  author = {Lee, Changwoo and Hu, Xiao and Kim, Hun-Seok},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  year = {2023}
}

VLSI

Audio and Image Cross-Modal Intelligence via a 10TOPS/W 22nm SoC with Back-Propagation and Dynamic Power Gating

Zichen Fan, Hyochan An, Qirui Zhang, Boxun Xu, Li Xu, Chien-Wei Tseng, and 5 more authors

In 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), 2022