Flattening the Abdominal Aortic Tree for Effective Visualization. - Joong-Ho Won; Geoffrey D. Rubin; Sandy Napel (EMBC, 2006)

ROC convex hull and nonparametric maximum likelihood estimation. - Johan Lim; Joong-Ho Won (Mach. Learn., 2012)

Massively Parallel Energy Space Exploration for Uncluttered Visualization of Vascular Structures. - Yongkweon Jeon; Joong-Ho Won; Sungroh Yoon (IEEE Trans. Biomed. Eng., 2013)

Uncluttered Single-Image Visualization of Vascular Structures Using GPU and Integer Programming. - Joong-Ho Won; Yongkweon Jeon; Jarrett Rosenberg; Sungroh Yoon; Geoffrey D. Rubin; Sandy Napel (IEEE Trans. Vis. Comput. Graph., 2013)

Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke. - Youngwon Choi; Yongchan Kwon; Han-Byul Lee; Beomjoon Kim; Myunghee Cho Paik; Joong-Ho Won (BrainLes@MICCAI, 2016)

Cross-sectional design with a short-term follow-up for prognostic imaging biomarkers. - Joong-Ho Won; Xiao Wu 003; Sang Han Lee; Ying Lu 006 (Comput. Stat. Data Anal., 2017)

Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization. - Seung-Jean Kim; Johan Lim; Joong-Ho Won (AISTATS, 2018)

Comparative study of computational algorithms for the Lasso with high-dimensional, highly correlated data. - Baekjin Kim; Donghyeon Yu; Joong-Ho Won (Appl. Intell., 2018)

HiComet: a high-throughput comet analysis tool for large-scale DNA damage assessment. - Taehoon Lee 001; Sungmin Lee 001; Woo Young Sim; Yu Mi Jung; Sunmi Han; Joong-Ho Won; Hyeyoung Min; Sungroh Yoon (BMC Bioinform., 2018)

Correction to: HiComet: a high-throughput comet analysis tool for large-scale DNA damage assessment. - Taehoon Lee 001; Sungmin Lee 001; Woo Young Sim; Yu Mi Jung; Sunmi Han; Joong-Ho Won; Hyeyoung Min; Sungroh Yoon (BMC Bioinform., 2018)

A peeling algorithm for multiple testing on a random field. - Joungyoun Kim; Donghyeon Yu; Johan Lim; Joong-Ho Won (Comput. Stat., 2018)

hc-OTU: A Fast and Accurate Method for Clustering Operational Taxonomic Units Based on Homopolymer Compaction. - Seunghyun Park 001; Hyun-Soo Choi; Byunghan Lee; Jongsik Chun; Joong-Ho Won; Sungroh Yoon (IEEE ACM Trans. Comput. Biol. Bioinform., 2018)

Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator. - Seyoon Ko; Joong-Ho Won (AISTATS, 2019)

Projection onto Minkowski Sums with Application to Constrained Learning. - Joong-Ho Won; Jason Xu; Kenneth Lange (ICML, 2019)

Ornstein Auto-Encoders. - Youngwon Choi; Joong-Ho Won (IJCAI, 2019)

Principled learning method for Wasserstein distributionally robust optimization with local perturbations. - Yongchan Kwon; Wonyoung Kim; Joong-Ho Won; Myunghee Cho Paik (ICML, 2020)

Proximity Operator of the Matrix Perspective Function and its Applications. - Joong-Ho Won (NeurIPS, 2020)

DistStat.jl: Towards Unified Programming for High-Performance Statistical Computing Environments in Julia. - Seyoon Ko; Hua Zhou 001; Jin Zhou; Joong-Ho Won (CoRR, 2020)

Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. - Yongchan Kwon; Joong-Ho Won; Beomjoon Kim; Myunghee Cho Paik (Comput. Stat. Data Anal., 2020)

A real-time 3D video analyzer for enhanced 3D audio-visual systems. - Sangoh Jeong; Hyun-Soo Kim; KyuWoon Kim; Byeong-Moon Jeon; Joong-Ho Won (Multim. Syst., 2020)

Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET. - Ernest K. Ryu; Seyoon Ko; Joong-Ho Won (SIAM J. Sci. Comput., 2020)

Learning from Nested Data with Ornstein Auto-Encoders. - Youngwon Choi; Sungdong Lee; Joong-Ho Won (ICML, 2021)

Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx. - Seyoon Ko; Ginny X. Li; Hyungwon Choi; Joong-Ho Won (Briefings Bioinform., 2021)

Orthogonal Trace-Sum Maximization: Applications, Local Algorithms, and Global Optimality. - Joong-Ho Won; Hua Zhou 001; Kenneth Lange (SIAM J. Matrix Anal. Appl., 2021)

Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert. - Yoonhyung Lee; Sungdong Lee; Joong-Ho Won (ICML, 2022)

Impact of Diffusion-Perfusion Mismatch on Predicting Final Infarction Lesion Using Deep Learning. - Sungdong Lee; Leonard Sunwoo; Youngwon Choi; Jae Hyup Jung; Seung Chai Jung; Joong-Ho Won (IEEE Access, 2022)

Orthogonal Trace-Sum Maximization: Tightness of the Semidefinite Relaxation and Guarantee of Locally Optimal Solutions. - Joong-Ho Won; Teng Zhang 002; Hua Zhou 001 (SIAM J. Optim., 2022)

Wasserstein Geodesic Generator for Conditional Distributions. - Young-geun Kim 002; Kyungbok Lee; Youngwon Choi; Joong-Ho Won; Myunghee Cho Paik (CoRR, 2023)

t3-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence. - Juno Kim; Jaehyuk Kwon; Mincheol Cho; Hyunjong Lee; Joong-Ho Won (CoRR, 2023)

A unified analysis of convex and non-convex ℓ p-ball projection problems. - Joong-Ho Won; Kenneth Lange; Jason Xu (Optim. Lett., 2023)

$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence. - Juno Kim; Jaehyuk Kwon; Mincheol Cho; Hyunjong Lee; Joong-Ho Won (ICLR, 2024)

StrWAEs to Invariant Representations. - Hyunjong Lee; Yedarm Seong; Sungdong Lee; Joong-Ho Won (ICML, 2024)

On the Correctness of the Generalized Isotonic Recursive Partitioning Algorithm. - Joong-Ho Won; Jihan Jung (CoRR, 2024)

Learning Massive-scale Partial Correlation Networks in Clinical Multi-omics Studies with HP-ACCORD. - Sungdong Lee; Joshua Bang; Youngrae Kim; Hyungwon Choi; Sang-Yun Oh; Joong-Ho Won (CoRR, 2024)