Jang-Hyun Kim

Hello! I am a final year 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 explores critical questions at the boundaries of data-driven learning: How can models efficiently handle infinite streams of data? How can models sustain effective learning in limited labeled-data regimes? These challenges drive my development of practical solutions for efficient, adaptive data processing in AI systems. I am also interested in uncovering novel insights from data, particularly in scientific domains.

Memory-efficient inference and training through data/feature compression (e.g., key-value in Transformers) [Context Memory, Dataset Condensation, Principal Curve].
Synthetic data generation to enhance model training with limited human-labeled data [Puzzle Mix, Co-Mixup].
Building relational structures within data to characterize and clean data [Neural Relation Graph].
Identifying causal relationships among human genes with data-driven learning [Targeted Cause Discovery].

Publications

	Large-Scale 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.