Jang-Hyun Kim

Hello! I am a PhD student in computer science at Seoul National University, advised by Hyun Oh Song. I was a visiting scholar at New York University in 2024, hosted by Kyunghyun Cho. Before that, I interned at NAVER AI in 2018, where I worked on speech enhancement. I completed BSc with Mathematics at Seoul National University in 2019. My PhD studies are supported by the Korea Foundation for Advanced Studies (KFAS).

Email / CV / Scholar / Github / Twitter

Research (Research Statement)

My research focuses on optimizing data usage in AI systems to develop sustainable systems capable of (1) efficient memory management during inference and (2) effective learning from limited human-labeled data. This goal entails leveraging model feedback to compress data, generate synthetic data, and identify relationships among data—ultimately reducing reliance on human intervention.

Compression of data or features (e.g., key-value in Transformers) to reduce training and inference costs [Context Memory, Dataset Condensation, Principal Curve].
Generation of synthetic data to enhance model training with limited human-labeled data [Puzzle Mix, Co-Mixup].
Identifying causal relationships among variables through data-driven learning [Targeted Cause Discovery].
Constructing relational structures of data to detect outliers and label errors [Neural Relation Graph].

Publications

	Targeted Cause Discovery with Data-Driven Learning Jang-Hyun Kim, Claudia Skok Gibbs, Sangdoo Yun, Hyun Oh Song, Kyunghyun Cho arXiv, 2024 Paper \| Code \| LM podcast \| Bibtex
	Compressed Context Memory For Online Language Model Interaction Jang-Hyun Kim, Junyoung Yeom, Sangdoo Yun^†, Hyun Oh Song^† ICLR, 2024 Paper \| Code \| Project Page \| Bibtex
	Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data Jang-Hyun Kim, Sangdoo Yun, Hyun Oh Song NeurIPS, 2023 Paper \| Code \| Bibtex
	Dataset Condensation via Efficient Synthetic-Data Parameterization Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song ICML, 2022 Paper \| Code \| Bibtex
	Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song NeurIPS, 2021 Paper \| Code \| Bibtex
	Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song ICLR ( Oral Presentation ), 2021 Paper \| Code \| Bibtex
	Spherical Principal Curves Jongmin Lee, Jang-Hyun Kim, Hee-Seok Oh TPAMI, 2021 \| R Journal, 2022 Paper \| R Journal \| Code \| Bibtex
	Puzzle Mix: Exploiting Saliency and Local statistics for Optimal Mixup Jang-Hyun Kim, Wonho Choo, Hyun Oh Song ICML, 2020 Paper \| Code \| Bibtex
	Phase-Aware Speech Enhancement with Deep Complex U-Net Hyeong-Seok Choi, Jang-Hyun Kim, Jaesung Huh, Adrian Kim, Jung-Woo Ha, Kyogu Lee arxiv, 2019 Paper \| Bibtex
	Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement Jang-Hyun Kim, Jaejun Yoo, Sanghyuk Chun, Adrian Kim, Jung-Woo Ha arxiv, 2018 Paper \| Code \| Bibtex \| Demo

Projects

Google's Speaker Verification
Code | Kaggle

	Caricature Generation Code
	Image Mosaic via Mixed Integer Programming Code

Notes

Mathematical Backgrounds for Machine Learning, An undergraduate dissertation (in Korean, 2018) | Paper

Academic Services

Workshop Program Committee

First Workshop on Interpolation Regularizers and Beyond (NeurIPS 2022) | Website
Workshop on ImageNet: Past, Present, and Future (NeurIPS 2021) | Website

Reviewing Activities

NeurIPS (2021-), ICLR (2022-), ICML (2022-), TMLR (2022-)

Template based on Jon Barron's website.