Google Scholar [Link]

2025

• EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality

Sanghyeok Lee, Joonmyung Choi, Hyunwoo J. Kim

Computer Vision and Pattern Recognition (CVPR), 2025. (Accepted) [Code] 

• Deformable Graph Transformer

Jinyoung Park, Seongjun Yun, Hyeonjin Park, Jaewoo Kang, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, and Hyunwoo J. Kim

Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025. (Accepted). (IF 20.8).

Collaboration with NAVER, Korea Univ.

• Conductive-bridge interlayer contacts for two-dimensional optoelectronic devices

Jisu Jang, Jung Pyo Hong, Sang-Jun Kim, Jongtae Ahn, Byoung-Soo Yu, Jaewon Han, Kihyun Lee, Aelim Ha, Eunki Yoon, Wonsik Kim, Suyeon Jo, Hyun Woo Ko, Seon Kyu Yoon, Takashi Taniguchi, Kenji Watanabe, Hogil Baek, Dae-Yeon Kim, Kimoon Lee, Sungchul Mun, Kyu Hyoung Lee, Soohyung Park, Kwanpyo Kim, Young Jae Song, Seung Ah Lee, Hyunwoo J. Kim, Jae Won Shim, Gunuk Wang, Ji-Hoon Kang, Min-Chul Park and Do Kyung Hwang

Nature Electronics, 2025. (IF 34.5)

Collaboration with KIST, Korea Univ., Changwon National Univ., Yonsei Univ., ibs, and others.

• Latent Bayesian Optimization via Autoregressive Normalizing Flows

Seunghun Lee, Jinyoung Park, Jaewon Chu, Minseo Yoon, Hyunwoo J. Kim

International Conference on Learning Representations (ICLR), 2025. (Oral presentation, top 1.8%). (Accepted)

• Super-class guided Transformer for Zero-Shot Attribute Classification

Sehyung Kim*, Chanhyeong Yang*, Jihwan Park, Taehoon song, Hyunwoo J. Kim

AAAI Conference on Artificial Intelligence (AAAI), 2025. [Code] [Video][Poster][Slide]

• VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning

Ji Soo Lee*, Jongha Kim*, Jeehye Na, Jinyoung Park, Hyunwoo J. Kim

AAAI Conference on Artificial Intelligence (AAAI), 2025. [Code] [Video][Poster][Slide]

2024

• LLaMo: Large Language Model-based Molecular Graph Assistant

Jinyoung Park, Minseong Bae, Dohwan Ko, Hyunwoo J. Kim

Advances in Neural Information Processing Systems (NeurIPS), 2024. [Code][Video][Slide][Poster]

• Constant Acceleration Flow

Dogyun Park, Sojin Lee, Sihyeon Kim, TaeHoon Lee, Youngjoon Hong*, Hyunwoo J. Kim*

Advances in Neural Information Processing Systems (NeurIPS), 2024. [Code][Video][Slide][Poster]

Collaboration with KAIST

• Inversion-based Latent Bayesian Optimization

Jaewon Chu*, Jinyoung Park*, Seunghun Lee, Hyunwoo J. Kim

Advances in Neural Information Processing Systems (NeurIPS), 2024. [Code][Video][Slide][Poster]

• Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation 

Jinyoung Park, Minseok Joo, Joo-Kyung Kim, Hyunwoo J. Kim

Empirical Methods in Natural Language Processing (EMNLP), 2024. [Code][Slide][Poster]

Collaboration with Amazon AGI team

• Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems

Sojin Lee*, Dogyun Park*, Inho Kong, Hyunwoo J. Kim

European Conference on Computer Vision (ECCV), 2024. (Oral presentation, top 2.3%). [Code][Video][Slide][Project][Poster]

• Understanding Multi-compositional Learning in Vision and Language Models via Category Theory

Sotirios Panagiotis Chytas,  Hyunwoo J. Kim, Vikas Singh

European Conference on Computer Vision (ECCV), 2024. 

Collaboration with University of Wisconsin-Madison

• Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis

Juyeon Ko*, Inho Kong*, Dogyun Park, Hyunwoo J. Kim

International Conference on Machine Learning  (ICML), 2024.  [Code][Video][Slide][Poster]

• vid-TLDR: Training Free Token merging for Light-weight Video Transformer

Joonmyung Choi*, Sanghyeok Lee*, Jaewon Chu, Minhyuk Choi, Hyunwoo J. Kim

Computer Vision and Pattern Recognition (CVPR), 2024. [Code][Video][Slide][Poster]

• Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision

Sanghyeok Lee*, Joonmyung Choi*, Hyunwoo J. Kim

Computer Vision and Pattern Recognition (CVPR), 2024. [Code][Video][Slide][Poster]

• Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection

Jongha Kim*, Jihwan Park*, Jinyoung Park*, Jinyoung Kim, Sehyung Kim, Hyunwoo J. Kim

Computer Vision and Pattern Recognition (CVPR), 2024. [Code][Video][Slide][Poster]

• Prompt Learning via Meta-Regularization

Jinyoung Park, Juyeon Ko, Hyunwoo J. Kim 

Computer Vision and Pattern Recognition (CVPR), 2024. [Code][Video][Slide][Poster]

• Retrieval-Augmented Open-Vocabulary Object Detection

Jooyeon Kim*, Eulrang Cho*, Sehyung Kim, Hyunwoo J. Kim

Computer Vision and Pattern Recognition (CVPR), 2024. [Code][Video][Slide][Poster]

• Domain-agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations

Dogyun Park, Sihyeon Kim, Sojin Lee, Hyunwoo J. Kim

International Conference on Learning Representations (ICLR), 2024. [Code][Project][Poster]

• Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble

Juhan Cha*, Minseok Joo*, Jihwan Park, Sanghyeok Lee, Hyunwoo J. Kim

Arxiv, 2024 [arXiv]

• LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding

Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, Zhuang Liu, Hu Xu, Hyunwoo J. Kim, Bilge Soran, Raghuraman Krishnamoorthi, Mohamed Elhoseiny, Vikas Chandra

Arxiv, 2024 [arXiv][Project]

Collaboration with Meta, KAUST

2023

• Large Language Models are Temporal and Causal Reasoners for Video Question Answering

Dohwan Ko*, Ji Soo Lee*, Woo-Young Kang, Byungseok Roh, Hyunwoo J. Kim

Empirical Methods in Natural Language Processing (EMNLP), 2023 [Code][Video][Slide][Poster][Demo]

Collaboration with KakaoBrain

• NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA 

Hyeong Kyu Choi, Seunghun Lee, Jaewon Chu, Hyunwoo J. Kim 

Advances in Neural Information Processing Systems (NeurIPS), 2023 [Code][Video][Slide][Poster]

• Advancing Bayesian Optimization via Learning Smooth Latent Spaces

Seunghun Lee*, Jaewon Chu*, Sihyeon Kim*, Juyeon Ko, Hyunwoo J. Kim

Advances in Neural Information Processing Systems (NeurIPS), 2023 [Code][Video][Slide][Poster]

• Unconstrained Pose Prior-Free Neural Radiance Field

Injae Kim*, Minhyuk Choi*, Hyunwoo J. Kim

Advances in Neural Information Processing Systems (NeurIPS), 2023 [Code][Video][Slide][Poster]

• Open-Vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models

Dohwan Ko, Ji Soo Lee, Miso Choi, Jaewon Chu, Jihwan Park, Hyunwoo J. Kim

International Conference on Computer Vision (ICCV), 2023.

• Distribution-Aware Prompt Tuning for Vision-Language Models

Eulrang Cho*,  Jooyeon Kim*, Hyunwoo J. Kim

International Conference on Computer Vision (ICCV), 2023.

• Semantic-Aware Template Learning via Part Deformation Consistency

Sihyeon Kim, Minseok Joo, Jaewon Lee, Juyeon Ko, Juhan Cha, Hyunwoo J. Kim

International Conference on Computer Vision (ICCV), 2023.

• Read-only Prompt Optimization for Vision-Language Few-shot Learning

DongJun Lee*, Seokwon Song*, Jihee Suh, Joonmyung Choi, Sanghyeok Lee, Hyunwoo J. Kim

International Conference on Computer Vision (ICCV), 2023.

• Robust Camera Pose Refinement for Multi-Resolution Hash Encoding

Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim, Hyunwoo J. Kim*, Jin-Hwa Kim*

In International Conference on Machine Learning  (ICML), 2023. 

Collaboration with NAVER

• MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models

Dohwan Ko*, Joonmyung Choi*, Hyeong Kyu Choi, Kyoung-Woon On, Byungseok Roh, Hyunwoo J. Kim,

Computer Vision and Pattern Recognition (CVPR), 2023. [Supplement] [Code]

Collaboration with KakaoBrain

• Self-positioning Point-based Transformer for Point Cloud Understanding

Jinyoung Park*, Sanghyeok Lee*, Sihyeon Kim, Yunyang Xiong, Hyunwoo J. Kim,

Computer Vision and Pattern Recognition (CVPR), 2023. [Supplement] [Code].

Collaboration with Meta

• Relation-aware Language-Graph Transformer for Question Answering, 

Jinyoung Park*, Hyeong Kyu Choi*, Juyeon Ko*, Hyeonjin Park, Ji-Hoon Kim, Jisu Jeong, Kyungmin Kim, Hyunwoo J. Kim,

AAAI Conference on Artificial Intelligence (AAAI), 2023. [Code][Video]

Collaboration with NAVER

• Self-Powering Sensory Device with Multi-Spectrum Image Realization for Smart Indoor Environments

Tae Hyuk Kim, Byoung-Soo Yu, Hyun Woo Ko, Na Won Park, Muhammad Ahsan Saeed,Jongtae Ahn, 

Suyeon Jo, Dae-Yeon Kim, Seon Kyu Yoon, Kwang-Hoon Lee, Sang Young Jeong, Han Young Woo, 

Hyunwoo J. Kim, Tae Geun Kim,* JaeHong Park,*Min-Chul Park,* Do Kyung Hwang,* and Jae Won Shim*

Advanced Materials, 2023.

Collaboration with KIST, Korea Univ.

• Recurrent DETR: Transformer-based Object Detection for Crowded Scenes

Hyeong Kyu Choi, Chong Keun Paik, Hyun Woo Ko, Min-Chul Park, Hyunwoo J. Kim

IEEE ACCESS, 2023.

Collaboration with KIST, SAMSUNG

• Robust Auxiliary Learning with Weighting Function for Biased Data

Dasol Hwang, Sojin Lee, Joonmyung Choi, Je-Keun Rhe, Hyunwoo J. Kim,

Information Sciences, 2023.

Collaboration with Soongsil Univ.

• Deep Neural Network for Solving Differential Equations Motivated by Legendre-Galerkin Approximation

Bryce Chudomelka, Youngjoon Hong*, John Morgan, Hyunwoo Kim*, Jinyoung Park

International Journal of Numerical Analysis and Modeling, 2023 (accepted).

Collaboration with San Diego State Univ., KAIST

• Deep Learning based Detection for Multiple Cache Side-channel Attacks

Hodong Kim, Changhee Hahn, Hyunwoo J. Kim, Youngjoo Shin, Junbeom Hur 

IEEE Transactions on Information Forensics & Security (TIFS), 2023.

Collaboration with SNU, Korea Univ.

• Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification

Injae Kim*, Jongha Kim*, Joonmyung Choi, Hyunwoo J. Kim

MICCAI 2023 1st International Workshop on Foundation Models for General Medical AI (MedAGI), 2023. (Oral Presentation)

2022

• SageMix: Saliency-Guided Mixup for Point Clouds, 

Sanghyeok Lee*, Minkyu Jeon*, Injae Kim, Yunyang Xiong, Hyunwoo J. Kim, 

Advances in Neural Information Processing Systems (NeurIPS), 2022. [Poster] [Video] [Code] 

Collaboration with Meta

• Invertible Monotone Operators for Normalizing Flows, 

Byeongkeun Ahn, Chiyoon Kim, Youngjoon Hong*, Hyunwoo J. Kim*, 

Advances in Neural Information Processing Systems (NeurIPS), 2022. [Poster] [Video] [Slides]

Collaboration with KAIST

• TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers, 

Hyeong Kyu Choi*, Joonmyung Choi*, Hyunwoo J. Kim, 

Advances in Neural Information Processing Systems (NeurIPS), 2022. [Poster] [Video] [Code] 

• k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment,

Minkyu Jeon, Hyeonjin Park, Hyunwoo J. Kim*, Michael Morley*, Hyunghoon Cho*,

European Conference on Computer Vision (ECCV), 2022. [Supp] [Poster] [Video] [Code]

Collaboration with Broad Institute of MIT and Harvard, Harvard Medical School

• Video-Text Representation Learning via Differentiable Weak Temporal Alignment, 

Dohwan Ko, Joonmyung Choi, Juyeon Ko, Shinyeong Noh, Kyoung-Woon On, Eun-Sol Kim, Hyunwoo J. Kim, 

Computer Vision and Pattern Recognition (CVPR), 2022. [Poster] [Code]

Collaboration with KakaoBrain, Hanyang Univ.

• Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection,

Jihwan Park, SeungJun Lee, Hwan Heo, Hyeong Kyu Choi, Hyunwoo J. Kim, 

Computer Vision and Pattern Recognition (CVPR), 2022. [Poster] [Code]

• Deformable Graph Convolutional Networks, 

Jinyoung Park, Sungdong Yoo, Jihwan Park, Hyunwoo J. Kim, 

AAAI Conference on Artificial Intelligence (AAAI), 2022. [Video]

• Randomly Shuffled Convolution for Self-Supervised Representation Learning,

Youngjin Oh*, Minkyu Jeon*, Dohwan Ko, Hyunwoo J. Kim,

Information Sciences, 2022. 

• Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs, 

Seongjun Yun, Minbyul Jeong , Sungdong Yoo, Seunghun Lee, Sean S. Yi , Raehyun Kim , Jaewoo Kang, Hyunwoo J. Kim,

Neural Networks (NN), 2022.

Collaboration with Korea Univ.

• Explainable Time-series Prediction using a Residual Network and Gradient-based methods,

Hyojung Choi, Chanhwi Jung, Taein Kang, Hyunwoo J. Kim, Il-Youp Kwak, 

IEEE ACCESS, 2022. 

Collaboration with Chung-Ang Univ.

• Single cell lineage reconstruction using distance-based algorithms and the R package, DCLEAR,

Wuming Gong, Hyunwoo J. Kim, Daniel J Garry, Il-Youp Kwak, 

BMC Bioinformatics, 2022.

Collaboration with University of Minnesota, Chung-Ang Univ.

• Deformable Graph Transformer,

Jinyoung Park, Seongjun Yun, Hyeonjin Park, Jaewoo Kang, Jisu Jeong, Kyung-Min Kim, Jung-woo Ha, Hyunwoo J. Kim, 

Arxiv, 2022. [arxiv]

Collaboration with Korea Univ., NAVER

2021

• Metropolis-Hastings Data Augmentation for Graph Neural Networks, 

Hyeonjin Park*, Seunghun Lee*, Sihyeon Kim, Jinyoung Park, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim, 

Advances in Neural Information Processing Systems (NeurIPS), 2021. [Video] [Code]

Collaboration with NAVER

• Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction,
  Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, Hyunwoo J. Kim,
  Advances in Neural Information Processing Systems (NeurIPS), 2021. [Video] [Code]
  Collaboration with Korea Univ.

• Point Cloud Augmentation with Weighted Local Transformations, 

Sihyeon Kim*, Sanghyeok Lee*, Dasol Hwang,  Jaewon Lee, Seong Jae Hwang, Hyunwoo J. Kim, 

International Conference on Computer Vision (ICCV), 2021. [Video] [Code]

Collaboration with University of Pittsburgh

• HOTR: End-to-End Human-Object Interaction Detection with Transformers,
  Bumsoo Kim, Junhyun Lee, Jaewoo Kang, Eun-Sol Kim, Hyunwoo J. Kim,
  Computer Vision and Pattern Recognition (CVPR), 2021. (Oral presentation). [Code]
  Collaboration with KakaoBrain

• Learning Augmentation for GNNs with Consistency Regularization,
  Hyeonjin Park*, Seunghun Lee*, Dasol Hwang, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, and Hyunwoo J. Kim,
  IEEE ACCESS, 2021.
  Collaboration with NAVER

• Learning to Balance Local Losses via Meta-Learning,
  Seungdong Yoa, Minkyu Jeon, Youngjin Oh, Hyunwoo J. Kim,
  IEEE ACCESS, 2021.

• Learning Non-parametric Surrogate Losses with Correlated Gradients,
  Seungdong Yoa*, Jinyoung Park*, Hyunwoo J. Kim,
  IEEE ACCESS, 2021.

• Search-and-Attack: Temporally SparseAdversarial Perturbations on Videos,
  Hwan Heo*, Dohwan Ko*, Jaewon Lee*, Youngjoon Hong, Hyunwoo J. Kim,
  IEEE ACCESS, 2021.

• Self-Supervised Learning for Anomaly Detection with Dynamic Local Augmentation,
  Seungdong Yoa*, Seungjun Lee*, Chiyoon Kim, and Hyunwoo J. Kim,
  IEEE ACCESS, 2021.

• Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning,
  Dasol Hwang*, Jinyoung Park*, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim,
  Arxiv, 2021. [arxiv]
  Collaboration with Pusan National Univ, NAVER

• Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs,
  Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, Sean S. Yi, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim,
  Arxiv, 2021. [arxiv]
  Collaboration with Korea Univ.

2020

• Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs,
  Dasol Hwang*, Jinyoung Park*, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim,
  Advances in Neural Information Processing Systems (NeurIPS), 2020. [Video] [Code]
  Collaboration with Pusan National Univ, NAVER

• UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection,
  Bumsoo Kim, Taeho Choi, Jaewoo Kang, Hyunwoo J. Kim,
  European Conference on Computer Vision (ECCV), 2020.
  Collaboration with Korea Univ.

• Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods,
  Byungjoo Kim, Bryce Chudomelka, Jinyoung Park, Jaewoo Kang, Youngjoon Hong, Hyunwoo J. Kim,
  European Conference on Computer Vision (ECCV), 2020.  [Code]
  Collaboration with San Diego State Univ., Korea Univ.

2019

• Graph Transformer Networks,
  Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim,
  Advances in Neural Information Processing Systems (NeurIPS), 2019. [Code]
  Collaboration with Korea Univ.

• Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging,
  Seong Jae Hwang, Ronak R. Mehta, Hyunwoo J. Kim, Sterling C. Johnson, Vikas Singh,
  Conference on Uncertainty in Artificial Intelligence (UAI), 2019.
  Collaboration with Univ. of Wisconsin-Madison

• Mixed Effects Neural Networks (MeNets) with Applications to Gaze Estimation,
  Yunyang Xiong, Hyunwoo J. Kim, Vikas Singh,
  Computer Vision and Pattern Recognition (CVPR), 2019.
  Collaboration with Univ. of Wisconsin-Madison

• ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Classification,
  Yunyang Xiong, Hyunwoo J. Kim, Varsha Hedau,
  ECV (CVPR Workshop), 2019. (oral presentation)
  Collaboration with Univ. of Wisconsin-Madison, Amazon Inc.

• On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging,
  Yunyang Xiong, Hyunwoo J. Kim, Bhargav Tangirala,Vikas Singh,
  Information Processing in Medical Imaging (IPMI), 2019. (oral presentation)
  Collaboration with Univ. of Wisconsin-Madison

• Localizing differentially evolving covariance structures via scan statistics,
  Ronak Mehta, Hyunwoo J. Kim, Shulei Wang, Sterling C. Johnson, Ming Yuan and Vikas Singh,
  Quarterly of Applied Mathematics, 2019.

2018

• Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families,
  Seong Jae Hwang, Ronak Mehta, Hyunwoo J. Kim, Vikas Singh,
  Arxiv, 2018.

• Efficient Relative Attribute Learning using Graph Neural Networks,
  Zihang Meng, Nagesh Adluru, Hyunwoo J. Kim, Glenn Fung, and Vikas Singh,
  European Conference on Computer Vision (ECCV), 2018.

• Tensorize, Factorize and Regularize: Robust Visual Relationship Learning,
  Seong Jae Hwang, Sathya N. Ravi, Zirui Tao, Hyunwoo J. Kim, Maxwell D. Collins, Vikas Singh,
  Computer Vision and Pattern Recognition (CVPR), 2018.

2017

• Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective,
  Ronak Mehta, Hyunwoo J. Kim , Shulei Wang, Sterling C. Johnson, Ming Yuan, Vikas Singh,
  Arxiv, Nov 2017.

• Statistical Learning Models for Manifold-valued Measurements with Applications to Computer Vision and Neuroimaging,
  Hyunwoo J. Kim, PhD Thesis, University of Wisconsin-Madison, 2017.

• Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification,
  Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim , Johannes Fürnkranz,
  Advances in Neural Information Processing Systems (NeurIPS), Dec 2017, (Spotlight presentation).

• Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging,
  Hyunwoo J. Kim, Nagesh Adluru, Heemanshu Suri, Baba C. Vemuri, Sterling C. Johnson, Vikas Singh,
  Computer Vision and Pattern Recognition (CVPR), Jul 2017.

• Riemannian Variance Filtering: An Independent Filtering Scheme for Statistical Tests on Manifold-valued Data,
  Ligang Zheng, Hyunwoo J. Kim, Nagesh Adluru, Michael A. Newton, Vikas Singh,
  The international Workshop on DIFFerential geometry in Computer Vision and Machine Learning (DIFF-CVML) at CVPR (CVPR Workshop), Jul 2017 (Oral presentation (slide)).

• Cover Song Identification with Metric Learning Using Distance as a Feature,
  Hoon Heo, Hyunwoo J. Kim, Wan Soo Kim, Kyogu Lee,
  The International Society of Music Information Retrieval (ISMIR), 2017.

2016

• Abundant Inverse Regression using Sufficient Reduction and its Applications,
  Hyunwoo J. Kim*, Brandon M. Smith*, Nagesh Adluru, Charles R. Dyer, Sterling C. Johnson, Vikas Singh,
  European Conference on Computer Vision (ECCV), Oct 2016. Both * are joint first authors.

• Latent Variable Graphical Model Selection using Harmonic Analysis: Applications to the Human Connectome Project (HCP),
  Won Hwa Kim*, Hyunwoo J. Kim*, Nagesh Adluru, Vikas Singh,
  Computer Vision and Pattern Recognition (CVPR), Jun, 2016. (Spotlight presentation). Both * are joint first authors.

2015

• Interpolation on the manifold of k-component Gaussian Mixture Models (GMMs),
  Hyunwoo J. Kim, Nagesh Adluru, Monami Banerjee, Baba C. Vemuri, Vikas Singh,
  In International Conference on Computer Vision (ICCV), Dec 2015.

• Manifold-valued Dirichlet Processes,
  Hyunwoo J. Kim, Jia Xu, Baba C. Vemuri, Vikas Singh,
  In International Conference on Machine Learning (ICML), Jul 2015. (Oral presentation). 

• Canonical Correlation Analysis on SPD(n) Manifolds ,
  Hyunwoo J. Kim, Nagesh Adluru, Barbara B. Bendlin, Sterling C. Johnson, Baba C. Vemuri, Vikas Singh,
  Riemannian Computing and Statistical Inferences in Computer Vision (RCCV), 2015 (Book chapter).

• Predicting Unseen Labels using Label Hierarchies in Large-Scale Multi-label Learning,
  Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim , and Johannes Fürnkranz,
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Sep 2015.

2014

• Canonical Correlation Analysis on Riemannian Manifolds and its Applications,
  Hyunwoo J. Kim, Nagesh Adluru, Barbara B. Bendlin, Sterling C. Johnson, Baba C. Vemuri, Vikas Singh,
  European Conference on Computer Vision (ECCV), Sep 2014.

• Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images,
  Hyunwoo J. Kim, Nagesh Adluru, Maxwell D. Collins, Moo K. Chung, Barbara B. Bendlin, Sterling C. Johnson, Richard J. Davidson, Vikas Singh,
  IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2014. (Oral presentation). 

Before 2014

• Evolutionary hypernetworks for learning to generate music from examples,
  H.-W. Kim , B.-H. Kim, and B.-T. Zhang,
  IEEE International Conference on Fuzzy Systems (Fuzz IEEE), Aug 2009.

• Text-to-image cross-modal retrieval of magazine articles based on higher-order pattern recall by hypernetworks,
  J.-W. Ha, B.-H. Kim, H.-W. Kim, W.C. Yoon, J.-H. Eom, and B.-T. Zhang,
  International Symposium on Advanced Intelligent Systems (ISIS), Aug 2009 (Best paper award).