기계학습 기반의 음성 분석으로 자살 위험 예측

Jaeyong Lee and Steven N. MacEachern. (2020). A New Proof of the Stick-Breaking Construction of Dirichlet Processes. JKSS.
Kyoungjae Lee, Jaeyong Lee and Lizhen Lin. (2019.12) Minimax Posterior Convergence Rates and Model Selection Consistency in High-dimensional DAG Models based on Sparse Cholesky Factors. Annals of Statistics, 47(6), 3413-3437.
Kyoungjae Lee and Jaeyong Lee.(2018).  Optimal Bayesian Minimax Rates for Unconstrained Large Covariance Matrices. Bayesian Analysis, 13(4), 1215-1233.
Seongil Jo, Jaeyong Lee, Peter Muller, Fernando A. Quintana & Lorenzo Trippa. (2017). Dependent Species Sampling Models for Spatial Density Estimation. Bayesian Analysis, 12(2), 379-406.
Sarat C. Dass, Jaeyong Lee, Kyoungjae Lee & Jonghun Park. (2017). Laplace based approximate posterior inference for differential equation models. Statistics and Computing, 27(3), 679-698.
인공지능과 빅데이터 분석을 위한 베이즈 추론의 수학적 기반 이론 연구. 과학기술정보통신부. 2018.09.01-2023.08.31.
신뢰도 검사시 불량발생 리스크, 추가 샘플링 확보에 따른 리스크 감소 대책 등에 대한 통계적 확률적 연구. 삼성전자. 2018.09.01-2023.08.31.
카드 거래 자료를 이용한 카드 고객 거래 패턴 분석. 코나아이(주). 2019.01.01-2019.05.31.

쉽게 배우는 알고리즘, 2018, 한빛미디어
Sungjoo Ha, Sangyeop Lee, Byung-Ro Moon, "Investigation of the Latent Space of Stock Market Patterns with Genetic Programming,"  Genetic and Evolutionary Computation Conference, pp. 1254-1261, 2018
Seung-Hyun Moon, Yong-Hyuk Kim, Yong Hee Lee, Byung-Ro Moon, "Application of machine learning to an early warning system for very short-term heavy rainfall," Journal of Hydrology, 2019
Seung-Hyun Oh, Byung-Ro Moon, "Automatic Reproduction of a Genius Algorithm: Strassen's Algorithm Revisited by Genetic Search," IEEE Transactions on Evolutionary Computation, 14, 2, pp. 246-251, 2010
Il-Seok Oh, Jin-Seon Lee, Byung Ro Moon, "Hybrid Genetic Algorithms for Feature Selection," IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 11, pp. 1424-1437, 2004
게임로그 기반 최적화 매칭 알고리즘 도출, 넷마블, 2018.12~2019.7
팽이버섯 생산 최적화 및 자문, 대흥농산, 2019.3~2019.8
LMS 고반응 요건 선별 및 자문, 현대카드, 2016.7~2017.4

Ahn, W.-Y., Kishida, K. T., Gu, X., Lohrenz, T., Harvey, A. H., Alford, J. R., Smith, K. B., Yaffe, G., Hibbing, J. R., Dayan, P., & Montague, P. R. (2014) Nonpolitical images evoke neural predictors of political ideology. Current Biology, 24, 1-7.
Ahn, W.-Y., Haines, N., & Zhang, L. (2017) Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry, 1:1.
Ahn, W.-Y. & Vassileva, J. (2016) Machine learning identifies substance-specific behavioral markers for heroin and amphetamine dependence. Drug and Alcohol Dependence, 161, 247-257.
Aylward, J., Valton, V., Ahn, W. Y., Bond, R. L., Dayan, P., Roiser, J. P., & Robinson, O. J. (2019). Altered learning under uncertainty in unmedicated mood and anxiety disorders. Nature Human Behaviour, 3(10), 1116-1123.
Yang, J., Pitt, M. A., Ahn, W. Y., & Myung, J. I. (in press) ADOpy: A Python Package for Adaptive Design Optimization. Behavior Research Methods.
  기계학습을 이용한 빠르고 높은 신뢰도의 멀티모달 금연 표지 개발, 한국연구재단 (연구책임자), 2017-2022년
작업기억-의사결정 상호작용의 뇌신경망에 관한 통합적 연구, 한국연구재단 (공동연구자), 2018-2020년
뇌영상기법과 기계학습을 이용한 비정상적 의사결정의 신경계산학적 기전 연구, 서울대학교 창의선도 신진 연구자 지원사업 (연구책임자), 2019-2022년

김청택 (2019). 빅데이터를 이용한 심리학 연구 방법. 한국심리학회지: 일반, 38(4), 519-548. DOI : 10.22257/kjp.2019.12.38.4.519
Noh, Y., Lee, D. D., Yang, K., Kim, C., & Zhang, B. (2015). Molecular Learning with DNA Kernel Machines, Biosystems, 137, 73-83.
Preacher, K. J., Zhang, G., Kim, C., & Mels, G. (2013). Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behavioral Research, 48, 28-56.
Suh, Y. , Yu, J., Mo, J, Song, L, & Kim (2017).A Comparison of Oversampling Methods on Imbalanced Topic Classification of Korean News Articles, Journal of Cognitive Science, 18, 391-437.
김청택, 이태헌(2002). 뇌와 인지모형: 잠재의미분석을 사용한 문서분류. 한국심리학회지:실험 및 인지, 14(4), 309-320.

Answerer in questioner's mind: Information theoretic approach to goal-oriented visual dialog, S.-W. Lee, Y.-J. Heo, B.-T. Zhang, The 32nd Annual Conference on Neural Information Processing Systems (NIPS 2018), (Spotlight)
Bilinear attention networks, J.-H. Kim, J. Jun, B.-T. Zhang, The 32nd Annual Conference on Neural Information Processing Systems (NIPS 2018)
Overcoming catastrophic forgetting by incremental moment matching, S.-W. Lee, J.-H. Kim, J. Jun, J.-W. Ha, and B.-T. Zhang, The 31st Annual Conference on Neural Information Processing Systems (NIPS 2017)
Multimodal residual learning for visual QA, J.-H. Kim, S.-W. Lee, D.-H. Kwak, M.-O. Heo, J. Kim, J.-W. Ha, and B.-T. Zhang, In The 30th Annual Conference on Neural Information Processing Systems (NIPS 2016)

Ahn JR, Lee HW, and Lee I (2019). Rhythmic pruning of perceptual noise for object representation in the hippocampus and perirhinal cortex in rats. Cell Reports 26
Jung MW, Lee H, Jeong Y, Lee JW, Lee I (2018). Remembering rewarding futures: A simulation‐selection model of the hippocampus. Hippocampus 28
Ahn JR, Lee I (2017) Neural correlates of both perception and memory for objects in the rodent perirhinal cortex. Cerebral Cortex 27
Lee I, Lee CH (2013) Contextual behavior and neural circuits. Frontiers in Neural Circuits 7
Lee I, Yoganarasimha D, Rao G, Knierim J (2004) Comparison of population coherence of place cells in hippocampal subfields CA1 and CA3. Nature 430

PRISM: Methylation Pattern-based, Reference-free Inference of Subclonal Makeup (ISMB 2019, Bioinformatics)
mirTime: Identifying Condition-Specific Targets of MicroRNA in Time-series Transcript Data using Gaussian Process Model and Spherical Vector Clustering (Bioinformatics 2019)
GABA-stimulated adipose-derived stem cells suppress subcutaneous adipose inflammation in obesity (PNAS, 2019)
DeepFam: Deep learning based alignment-free method for protein family modeling and prediction (ISMB 2018, Bioinformatics)
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification (IJCAI 2017)
과제명: 멀티오믹스 분석 알고리즘 및 플랫폼 개발
연구비 지원기관: 한국연구재단 (과학기술정보통신부)
연구수행 기간: 2014.11.01 - 2022.10.31 (96개월)
과제명: 실험 및 문헌정보 거대 복잡형 데이터 통합분석추론 연구
연구비 지원기관: 한국연구재단 (과학기술정보통신부)
연구기간: 2017.9.1 - 2020.12.31 (40개월)

Kwon, D., & Oh, S. (2019). The number of letters in number words influences the response time in numerical comparison tasks: Evidence using Korean number words. Attention, Perception, & Psychophysics, 81(8), 2612-2618.
Ryu, D., & Oh, S. (2018). The effect of good continuation on the contact order judgment of causal events. Journal of Vision, 18(11), 5-5.
Lee, H., & Oh, S. (2016). How directional change in reading/writing habits relates to directional change in displayed pictures. Laterality: Asymmetries of Body, Brain and Cognition, 21(1), 1-11.
Oh, S. (2013). Eyes can switch finger stroke. Perception, 42(6), 681–684.
Oh S. (2011). The eyeglass reversal. Attention, Perception & Psychophysics. 73, 1336-1343.
Baby mind: 아기 모사형 인공지능 개발
서울대학교 얼굴 데이터베이스 구축
미술감상에서 감상자 자세

세상을 읽는 새로운 언어, 빅데이터, 조성준, 21세기북스, 2019.08.28, ISBN 9788950982737
Fault Detection and Diagnosis Using Self-Attentive Convolutional Neural Networks for Variable-length Sensor Data in Semiconductor Manufacturing, Eunji kim, Sungzoon Cho, Byeong eon Lee, Myoungsu Cho, IEEE Transactions on Semiconductor Manufacturing, Volume: 32 , Issue: 3 , Aug. 2019, Page(s): 302 - 309
Champion-challenger analysis for credit card fraud detection: hybrid ensemble and deep learning, Eunji Kim, Jehyuk Lee, Hunsik Shin, Hoseong Yang, Sungzoon Cho, Seung-kwan Nam, Youngmi Song, Jeong-a Yoon, Jong-il Kim, Wooho Chung, Kyungmo La, Kangshin Ko, Expert Systems with Applications, Volume 128, 15 August 2019, Pages 214-224
Stock Price Prediction through Sentiment Analysis of Corporate Disclosures Using Distributed Representation, Misuk Kim, Eunjeong Lucy Park, and Sungzoon Cho, Intelligent Data Analysis Journal, Vol. 22(6) pp. 1395-1413 December, 2018
"Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data", Taehoon Ko, Je Hyuk Lee, Hyunchang Cho, Sungzoon Cho, Wounjoo Lee, Miji Lee, Industrial Management & Data Systems, Vol. 117 Issue: 5, 2017, pp.927-945
기업 공시 데이터를 활용한 기업 네트워크 구축, 연구재단, 2018~2021
기계학습을 활용한 데이터 기반 진단, 고장예지 및 내구성 평가, 삼성전자, 2016~2021
예방품질능력 강화 위한 컨버터 지능형 진단 기술 개발, 현대차, 2018~2019

Predicting acute kidney injury in cancer patients using heterogenous and irregular data/PLoS One, 2018,13:e0199839/Park N, Kang E, Park M, Lee H, Kang HG, Yoon HJ, Kang U/JMIR mHealth uHealth, 2019,7:e13327/Kwon SB, Ahn JW, Lee SM, Lee J, Lee D, Hong J, Kim HC, Yoon HJ
Unobtrusive estimation of cardiopulmonary fitness with daily activity in healthy young men/J Korean Med Sci, 2017, 32:1947-1952/Ahn JW, Hwang SH, Yoon C, Lee J, Kim HC, Yoon HJ
Predicting acute kidney injury in cancer patients using heterogenous and irregular data/PLoS One, 2018,13:e0199839/Park N, Kang E, Park M, Lee H, Kang HG, Yoon HJ, Kang U
개인정보보호 강화 DisTIL 알고리즘 개발/보건산업진흥원/2019~2021
암흑데이터 극한활용 연구센터/연구재단/2018~2025

Hyeonwoo Noh, Seunghoon Hong, Bohyung Han: Learning Deconvolution Network for Semantic Segmentation. ICCV 2015
Hyeonseob Nam, Bohyung Han: Learning multi-domain convolutional neural networks for visual tracking. CVPR 2016
Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, Bohyung Han: Large scale image retrieval with attentive deep local features. ICCV 2017
Paul Hongseok Seo, Geeho Kim, Bohyung Han: Combinatorial Inference against Label Noise. NeurIPS 2019
Minsoo Kang, Jonghwan Mun, Bohyung Han: Towards Oracle Knowledge Distillation with Neural Architecture Search. AAAI 2020
Distributed Combinatorial Deep Learning. Google AI Focused Research Award
Privacy Preserving Semantic Image Compression. Kakao Brain
Neural Processing Research Center (대규모 인공 신경망에서의 학습 및 추론 알고리즘 개발), SAIT

Hyeong-Seok Choi, Changdae Park, and Kyogu Lee, “From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech”, to appear in Proceedings of International Conference on Learning epresentations (ICLR), 2020
Jie Hwan Lee, Hyeong-Seok Choi, and Kyogu Lee, “Audio query-based music source separation”, in Proceedings of International Society for Music Information Retrieval Conference (ISMIR), 2019
Juheon Lee, Hyeong-Seok Choi, Chang-bin Jeon, Junghyun Koo, and Kyogu Lee, “Adversarially Trained End-to-end Korean Singing Voice Synthesis System”, in Proceedings of Interspeech, 2019, Best Student Paper Award
Juheon Lee, Sungkyun Chang, Sangkeun Choe, and Kyogu Lee, “COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK”, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Yoonchang Han, Jaehun Kim, Kyogu Lee, “Deep convolutional neural networks for predominant instrument recognition in polyphonic music”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 25(1), pp. 208-221, 2017
"자기회귀적 적대적 생성 신경망 기반의 가창 합성 및 평가 모델 연구", 미래창조과학부/전략과제, 2017.11.01~2020.10.31
"심상의 소리: 적대적 신경망을 활용한 크로스-모달 오디오 합성/변환 연구", 과학기술정보통신부/원천기술개발사업,  2017.11.01~2020.12.31
"뇌·인지 발달과정의 기초-영아단계 모사형 실세계 상호작용 경험 기반 객체 관련 개념의 기계학습 기술, 과학기술정보통신부/정보통신방송연구개발사업", 2019.04.01~2022.12.31

Barend Harris, Inpyo Bae, and Bernhard Egger. "Architectures and algorithms for on-device user customization of CNNs." In Integration, the VLSI Journal, Volume 67, July 2019.
Inpyo Bae, Barend Harris, Hyemi Min, and Bernhard Egger. "Auto-Tuning CNNs for Coarse-Grained Reconfigurable Array-based Accelerators." Presented at the 2018 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES'18) and in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Volume 37, Issue, 11; November 2018.
Younghyun Cho, Surim Oh, and Bernhard Egger. "Performance Modeling of Parallel Loops on Multi-Socket Platforms using Queueing Systems." In IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 31, Issue 2; February 2020.
Younghyun Cho, Camilo A.C. Guzman, and Bernhard Egger. "Maximizing System Utilization via Parallelism Management for Co-Located Parallel Applications." In Proceedings of the the 2018 International Conference on Parallel Architectures and Compilation (PACT'18), Limassol, Cyprus, November 2018.
Changyeon Jo, Youngsu Cho, and Bernhard Egger. "A Machine Learning Approach to Live Migration Modeling." In Proceedings of the 2017 ACM Symposium on Cloud Computing (SoCC'17), Santa Clara, USA, September 2017.
Efficient Mapping and Scheduling of Resource and Dataflow for NPU Architecture Search, 삼성전자, 2020
Exploring the Effect of Data Compression on Runtime and Accuracy of DNNs, SK텔레콤, 2018-2020
H/W–컴파일러 수직적 통합 최적화된 임베디드 DNN 프로세서연구, 삼성전자, 2017-2020

Ohn, Ilsang, and Yongdai Kim. "Nonconvex sparse regularization for deep neural networks and its optimality." Neural Computation, 2022
Kim, Dongha, and Yongdai Kim. "Understanding Effects of Architecture Design to Invariance and Complexity in Deep Neural Networks." IEEE Access, 2021.
Kim, Yongdai, Ilsang Ohn, and Dongha Kim. "Fast convergence rates of deep neural networks for classification." Neural Networks,  2021.
Kim, Minjin, Young-geun Kim, Dongha Kim, Yongdai Kim & Myunghee Cho Paik. "Kernel-convoluted Deep Neural Networks with Data Augmentation." Association for the Advancement of Artificial Intelligence (AAAI), 2021.
Kim, Dongha, Jaesung Hwang, and Yongdai Kim. "On casting importance weighted autoencoder to an EM algorithm to learn deep generative models." International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
Ohn, Ilsang, and Yongdai Kim. "Smooth function approximation by deep neural networks with general activation functions." Entropy , 2019.
정책 변화를 유연하게 반영하여 준수하는 인공지능 기술 개발, 정보통신기획평가원, 2022.04.01. ~ 2026.12.31.
인공지능 모델과 학습데이터의 편향성 분석-탐지-완화·제거 지원 프레임워크 개발, 정보통신기획평가원, 2021.01.01. ~ 2022.12.31.
기계학습을 위한 성긴 방법론에 대한 연구, 한국연구재단, 2020.03.01. ~ 2025.02.28.

Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs. NSDI 2019.
Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks. EuroSys 2019.
Apache Nemo: A Framework for Building Distributed Dataflow Optimization Policies. ATC 2019.
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. OSDI 2018.
Improving the Expressiveness of Deep Learning Frameworks with Recursion. EuroSys 2018.
(SW 스타랩) 다양한 분석을 고속 수행하는 단일화된 빅데이터 스택 개발
[뉴럴 프로세싱 시스템 연구/17세부] 대규모 클러스터에서 딥러닝 학습을 자동 분산하는 시스템
비디오 튜링 테스트를 통과할 수준의 비디오 스토리 이해 기반의 질의응답 기술 개발

컨텐츠사용 형태 및 구매데이터 분석, 카카오페이, 2019
생체 건강나이 기반의 심뇌혈관질환 발생 위험 측정모델을 통한 노후필요 자금설계, NIA, 2018

Combinatorial Neural Bandits. Taehyun Hwang, Kyuwook Chai, Min-hwan Oh, International Conference on Machine Learning (ICML), 2023

Model-based Offline Reinforcement Learning with Count-based Conservatism. Byeongchan Kim, Min-hwan Oh, International Conference on Machine Learning (ICML), 2023

Model-based Reinforcement Learning with Multinomial Logistic Function Approximation. Taehyun Hwang, Min-hwan Oh, AAAI Conference on Artificial Intelligence (AAAI), 2023

Sparsity-Agnostic Lasso Bandit. Min-hwan Oh, Garud Iyengar, Assaf Zeevi, International Conference on Machine Learning (ICML), 2021

Thompson Sampling for Multinomial Logit Contextual Bandits. Min-hwan Oh, Garud Iyengar Neural Information Processing Systems (NeurIPS), 2019

Interfacial thermodynamics of spherical nanodroplets: Molecular understanding of surface tension via hydrogen bond network
GCIceNet: A Graph Convolution Network For Deep Learning Of Ice Phases
AI-based atomic force microscopy
리더연구과제 0차원 나노플루이딕스

Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation
Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation
AI기반 의료영상 데이터 Labeling 기술연구, (주)인피니트헬스케어, 2020-04-01~2021-03-31
인공지능기반 파노라마, CT영상 병변 인식 기술개발, (주)오스템임플란트, 2019-12-16~2021-02-15
인공지능 기반 CT 영상 분할 기술 개발, (주)오스템임플란트, 2019-02-01~2019-10-31

"The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children." Human Brain Mapping
"Association of Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide." JAMA Network Open
"Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth." Human Brain Mapping
"Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data." NPJ Digital Medicine
"Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes." Neuroimage-Clinical
"Associations between brain structure and connectivity in infants and exposure to selective serotonin reuptake inhibitors during pregnancy. " JAMA pediatrics
"The Effects of Obstructive Sleep Apnea Syndrome on the Dentate Gyrus and Learning and Memory in Children.'" The Journal of Neuroscience
"Clinically anxious individuals show disrupted feedback between inferior frontal gyrus and prefrontal-limbic control circuit."  Journal of Neuroscience
"Neural correlates of aggression in medication-naive children with ADHD: multivariate analysis of morphometry and tractography."  Neuropsychopharmacology
"Hyper-reactive human ventral tegmental area and aberrant mesocorticolimbic connectivity in overgeneralization of fear in generalized anxiety disorder."  Journal of Neuroscience
"Circuit-wide structural and functional measures predict ventromedial prefrontal cortex fear generalization: implications for generalized anxiety disorder."  Journal of Neuroscience

Kang MS, Choi TY, Ryu HG, Lee D, Lee SH, Choi SY*, Kim KT*. Autism-like behavior caused by deletion of vaccinia-related kinase 3 is improved by TrkB stimulation. J Exp Med. 2017 Oct 2;214(10):2947-2966. doi: 10.1084/jem.20160974.
Choi TY, Lee SH, Kim YJ, Bae JR, Lee KM, Jo Y, Kim SJ, Lee AR, Choi S, Choi LM, Bang SH, Song MR, Chung J, Lee KJ, Kim SH, Park CS, Choi SY. Cereblon maintains synaptic and cognitive function by regulating BK channel. J Neurosci. 2018 Apr 4;38(14):3571-3583. doi: 10.1523/JNEUROSCI.2081-17.2018.
Noh K, Lee H, Choi TY, Joo Y, Kim SJ, Kim H, Kim JY, Jahng JW, Lee S*, Choi SY*, Lee SJ*. Negr1 modulates anxiety- and depression-like behaviors via interaction with the LIF receptor and Lcn-2 expression. Mol Psychiatry. 2019 Aug;24(8):1189-1205. doi: 10.1038/s41380-018-0347-3.
Park SW, Kim J, Kang M, Lee W, Park BS, Kim H, Choi SY, Yang S, Ahn JH, Yang S. Epidermal electrotherapy for epilepsy. Small. 2018 Jul;14(30):e1801732. doi: 10.1002/smll.201801732.
An H, Cha KM, Choi SY, Shin HC. Data compression of excitatory postsynaptic potentials. Electronics Lett. 2015 Aug 31; 51(18):1407-1409. doi: 10.1049/el.2015.1133.
대뇌피질 신경회로의 기능적 다양성 및 발달기전 규명을 통한 뇌기능 이상 해법 도출 (과학기술정보통신부/중견연구자지원사업) 2016.06.01.-2019.05.31
Ex vivo 기반 해마체 신경망 다채널 분석 기술 개발 (과학기술정보통신부/뇌과학원천기술개발사업) 2013.05.01-2016.04.30

Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization. Sangwon Jung, Taeeon Park, Sanghyuk Chun, and Taesup Moon. The 11th International Conference on Learning Representations (ICLR), May 2023

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. Hongjoon Ahn, Youngyi Yang, Quan Gan, David Wipf, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2022

GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training. Jaeseok Byun, Taebaek Hwang, Jianlong Fu, and Taesup Moon. European Conference on Computer Vision (ECCV), October 2022

Learning Fair Classifiers with Partially Annotated Group Labels. Sangwon Jung, Sanghyuk Chun, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning. Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2021

SS-IL: Separated Softmax for Incremental Learning. Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon. International Conference on Computer Vision (ICCV), October 2021

Fair Feature Distillation for Visual Recognition. Sangwon Jung, Donggyu Lee, Taeeon Park, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise. Jaeseok Byun, Sungmin Cha, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2020

Uncertainty-based continual learning with adaptive regularization. Hongjoon Ahn, Sungmin Cha, Donggyu Lee and Taesup Moon. Proceedings of Neural Information Processing Systems (NeurIPS), December 2019

Fooling neural network interpretations via adversarial model manipulation. Juyeon Heo, Sunghwan Joo, and Taesup Moon. Proceedings of Neural Information Processing Systems (NeurIPS), December 2019

Seowoo Jang, Kang G. Shin, and Saewoong Bahk "Post-CCA and Reinforcement Learning based Bandwidth Adaptation in 802.11ac Networks," IEEE Transactions on Mobile Computing, vol. 17, no. 2, pp. 419-432, Feb. 2018.
Jeongyoon Heo, Byungjun Kang, Jin Mo Yang, Jeongyeup Paek, and Saewoong Bahk, "Performance-Cost Tradeoff of Using Mobile Roadside Units for V2X Communication," IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 9049-9059, Sep. 2019.
Hyung-Sin Kim, JeongGil Ko, and Saewoong Bahk, "Smarter Markets for Smarter Life: Applications, Challenges and Deployment Experiences," IEEE Communications Magazine, vol. 55, no. 5, pp. 34-41, May. 2017.
Sung-Guk Yoon, Young-June Choi, Jong-Keun Park and Saewoong Bahk, "Stackelberg Game based Demand Response for At-Home Electric Vehicle Charging," IEEE Transactions on Vehicular Technology, vol. 65, issue 6, pp. 4172-4184, June 2016.
Jongwook Lee and Saewoong Bahk, "On the MDP-based Cost Minimization for Video-on-Demand Services in a Heterogeneous Wireless Network with Multi-Homed Terminals," IEEE Transactions on Mobile Computing, vol. 12, issue. 9, pp. 1737-1749, Sep. 2013
One of Top 100 Research results in 2018 - Disaster Communication Convergence Technology Research (2016.04.01-2017.12.31.) IITP

Genet Med. 2019 Dec;21(12):2695-2705. doi: 10.1038/s41436-019-0588-9.
Oncogenic effects of germline variants in lysosomal storage disease genes.
Sci Rep. 2019 Mar 5;9(1):3465. doi: 10.1038/s41598-019-39706-0.
Interpretation of EBV infection in pan-cancer genome considering viral life cycle: LiEB (Life cycle of Epstein-Barr virus).
Blood Cancer J. 2018 May 23;8(5):43. doi: 10.1038/s41408-018-0083-6.
RTK-RAS pathway mutation is enriched in myeloid sarcoma.

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning, CVPR 2017
A Comprehensive Overhaul of Feature Distillation, ICCV 2019
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning, ICCV 2019
Associative Variational Auto-encoder with Distributed Latent Spaces and Associators, AAAI 2020
Context-aware Deep Feature Compression for High-speed Visual Tracking, CVPR 2018
예지형 시각 지능 원천 기술 개발, 과학기술정보통신부, 2014.  4.  1 ~ 2024.  2. 29
Thinking Machine: 다중 감각 심층 신경망 기반 연상 작용의 실현 및 영상 생성 연구, 과학기술정보통신부, 2017. 09. 01 - 2020. 12. 31
실외 무인 경비 로봇을 위한 멀티모달 지능형 정보분석 기술 개발, 과학기술정보통신부, 2017. 04. 01 ~ 2021. 12. 31

Sungkyu Jung, Myung Hee Lee and Jeongyoun Ahn (2018). “On the number of principal components in high dimensions,” Biometrika 105(2), 389-402.
Sungkyu Jung, Jeongyoun Ahn, and Yongho Jeon (2019). “Penalized Orthogonal Iteration for Sparse Estimation of Generalized Eigenvalue Problem” Journal of Computational and Graphical Statistics. 28(3) 710-721.
Gen Li and Sungkyu Jung (2017). “Incorporating Covariates into Integrated Factor Analysis of Multi-View Data,” Biometrics 73 (4), 1433-1442.
Byungwon Kim, Stephan Huckemann, Joern Schulz, and Sungkyu Jung (2019). “Small sphere distributions for directional data with application to medical imaging”, Scandinavian Journal of Statistics. 46(4) 1047-1071.

Jisoo Jeong, Seungeui Lee, Jeesoo Kim and Nojun Kwak, "Consistency-based Semi-supervised Learning for Object detection", Thirty-third Conference on Neural Information Processing Systems (NeurIPS2019), Vancouver, Canada, Dec. 2019.
Simyung Chang, SeongUk Park, John Yang and Nojun Kwak, "Sym-Parameterized Dynamic Inference for Mixed-Domain Image Translation, International Conference on Computer Vision (ICCV2019), Seoul, Korea, Oct. 2019
Simyung Chang, John Yang, Jaeseok Choi and Nojun Kwak, "Genetic-Gated Networks for Deep Reinforcement Learning", Thirty-second Conference on Neural Information Processing Systems (NeurIPS2018), Montreal, Canada, Dec. 2018.
Jangho Kim, SeoungUK Park and Nojun Kwak, "Paraphrasing Complex Network: Network Compression via Factor Transfer", Thirty-second Conference on Neural Information Processing Systems (NeurIPS2018), Montreal, Canada, Dec. 2018.
Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee and Nojun Kwak, "Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams", The Thirtieth IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2018), Salt Lake City, UT, June 2018.
Thinking Machine: 다중 감각간 관계 지식을 활용한 통합 사고 신경망 연구, 약 18억, 한국연구재단, 2017.11 - 2020.12

BANMo: Building Animatable 3D Neural Models from Many Casual Videos, CVPR 2022
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization.CVPR 2020
Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction, CVPR 2019
Panoptic Studio: A Massively Multiview System for Social Interaction Capture, TPAMI 2019
Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies, CVPR 2018

2015 WHO classification of Lung tumours and Pleura, 2015 IARC press (공저자)
2020 WHO classification of Lung tumours and Pleura, 2020 IARC press (공저자 집필중)
Atlas of ALK and ROS1 testing in lung cancer 2nded. IASLC 2016 (공저자)
NCCN guidelines : Non-Small Cell Lung Cancer Asian Consensus Statements 2018
Targeted sequencing analysis of pulmonary adenocarcinoma with ultiple synchronous ground-glass/lepidic nodules J Thorac Oncol 2018;13:1776-83

Ham, S., H. Park, E. Kim, S. Kho, and D. Kim. “Investigating the Influential Factors for Practical Application of Multiclass Vehicle Detection for Images from Unmanned Aerial Vehicle Using Deep Learning Models.” Proceedings of the 99th Annual Meeting of the Transportation Research Board, Washington, D. C., U.S.A., January 12-16, 2020.
Kim, E., H. Park, S. Kho, and D. Kim*. (2019.12) “A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed.” Journal of Advanced Transportation, Vol. 2019, Article ID 3958127. https://doi.org/10.1155/2019/3958127.
Kim, E., H. Park, S. Ham, S. Kho, and D. Kim*. (2019.04) “Extracting Vehicle Trajectories Using Unmanned Aerial Vehicles in Congested Traffic Conditions.” Journal of Advanced Transportation, Vol. 2019, Article ID 9060797. https://doi.org/10.1155/2019/9060797.
Park, H., D. Kim*, and S. Kho. (2018.12) “Bayesian Network for Freeway Traffic State Prediction.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 2672, No. 45, pp. 124-135. DOI: 10.1177/0361198118786824.
영상 기반 딥 생성 모델을 활용한 차로별 미시교통정보 생성, 과학기술정보통신부, 2019.09-2020.08
무인항공기와 루프검지기 기반의 통합차량검지시스템을 이용한 교통혼잡 관리전략 개발, 과학기술정통부, 2016.06-2019.05

Fogarty, C. B., Lee, K., Kelz, R. R., and Keele, L. (2021) Biased encouragements and heterogeneous effects in an instrumental variable study of emergency general surgical outcomes. Journal of the American Statistical Association.

Lee, K., Small, D. S., and Dominici, F. (2021) Discovering heterogeneous exposure effects using randomization inference in air pollution studies. Journal of the American Statistical Association.

Lee, K., and Small, D. S. (2019). Estimating the malaria attributable fever fraction accounting for parasites being killed by fever and measurement error. Journal of the American Statistical Association.

Lee, K., Lorch S. A., and Small, D. S. (2019). Sensitivity analyses for average treatment effect when outcome is censored by death in instrumental variable models. Statistics in Medicine.

Lee, K., Small, D. S., and Rosenbaum, P. R. (2018). A powerful approach to the study of moderate effect modification in observational studies. Biometrics. (Statistics in Epidemiology (SIE) Young Investigator Award)

Lee, K., Small, D. S., Hsu, J. Y., Silver, J. H. and Rosenbaum, P. R. (2018). Discovering effect modification in an observational study of surgical mortality at hospitals with superior nursing. Journal of the Royal Statistical Society, Series A.

Soomin Kim, Jinsu Eun, Changhoon Oh, Bongwon Suh & Joonhwan Lee (2020) Bot in the Bunch: Facilitating Group Chat Discussion by Improving Efficiency and Participation with a Chatbot. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20). Association for Computing Machinery, New York, NY, USA, Paper 654, 1-13.
Dongwhan Kim & Joonhwan Lee (2019) Designing an Algorithm-Driven Text Generation System for Personalized and Interactive News Reading, International Journal of Human–Computer Interaction, 35:2, 109-122, DOI: 10.1080/10447318.2018.1437864
Soomin Kim, Joonhwan Lee, and Gahgene Gweon (2019) Comparing Data from Chatbot and Web Surveys: Effects of Platform and Conversational Style on Survey Response Quality. In Proceedings of the 2019 CHI Conference on Human ctors in Computing Systems (CHI ’19). Association for Computing Machinery, New York, NY, USA, Paper 86, 1–12. DOI:https://doi.org/10.1145/3290605.3300316
SoHyun Park, Jeewon Choi, Sungwoo Lee, Changhoon Oh, Changdai Kim, Soohyun La, Joonhwan Lee & Bongwon Suh (2019) Designing a Chatbot for a Brief Motivational Interview on Stress Management: Qualitative Case Study, Journal of medical Internet research 21 (4), e12231, https://www.jmir.org/2019/4/e12231/
Jieun Wee, Sooyeun Jang, Joonhwan Lee & Woncheol Jang (2017) The influence of depression and personality on social networking, Computers in Human Behavior, 74, 45-52, https://doi.org/10.1016/j.chb.2017.04.003
로봇 저널리즘 기반의 방송 뉴스 콘텐츠 제작 기술 개발, 과학기술정보통신부, 2017.4.1 ~ 2019.12.31
성범죄 피해자 진술 지원 시스템 연구, 과학정보기술통신부, 2018/08/01 ~ 2020/12/31

Choi, Daeyoung, and Wonjong Rhee. "Utilizing class information for deep network representation shaping." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 2019.
Shin, Changho, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, and Wonjong Rhee. "Subtask gated networks for non-intrusive load monitoring." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1150-1157. 2019.
Jung, Wonkyung, Daejin Jung, Sunjung Lee, Wonjong Rhee, and Jung Ho Ahn. "Restructuring batch normalization to accelerate CNN training." SysML (2018).
Yu, Wei, Wonjong Rhee, Stephen Boyd, and John M. Cioffi. "Iterative water-filling for Gaussian vector multiple-access channels." IEEE Transactions on Information Theory 50, no. 1 (2004): 145-152.
Rhee, Wonjong, and John M. Cioffi. "On the capacity of multiuser wireless channels with multiple antennas." IEEE Transactions on Information Theory 49, no. 10 (2003): 2580-2595.
정보이론 및 최적화이론을 활용한 딥러닝의 수학적 원리 및 응용 연구, 연구재단/중견, 2017.9~2020.8
최신 딥러닝 기법을 이용한 반도체 소자 모델링 기법 혁신, 하이닉스, 2019.3~2019.12
SKINET AutoML 구현을 위한 HPO (Hyper Parameter Optimization)및  NAS(Neural Architecture Search) 연구, SK Telecom, 2018.4~2018.12

Kim, Hakkyun, Kyoungmi Lee, and Kiwan Park (2015), “Balancing Out Feelings of Risk by Playing It Safe: The Effect of Social Networking on Subsequent Risk Judgment,” Organizational Behavior and Human Decision Processes, 131(November), 121-131.
Kim, Hakkyun, Kiwan Park, and Norbert Schwarz (2010), “Will This Trip Really Be Exciting? The Role of Incidental Emotions in Product Evaluation,” Journal of Consumer Research, 36 (April), 983-991.
Priester, Joseph R., Richard E. Petty, and Kiwan Park (2007), “Whence Univalent Ambivalence? From the Anticipation of Conflicting Reactions,” Journal of Consumer Research, 34(June), 11-21.
4차산업 혁명 시대의 서비스 사용 촉진 및 저해 요인에 대한 분석, 한국연구재단 중견연자지원(2019S1A5A2A01050564), 2019.07-2022.06, 연구책임자

인공지능기반 최적파이토슈티컬 도출시스템 및 응용연구실, 기초연구실사업, 과기정통부
아동청소년 비만 예방관리를 위한 BT-IT 융합기반 통합 플랫폼 기술개발, 과기정통부
방송통신 융합기술을 활용한 보육기관 맞춤형 스마트 웰니스 서비스 개발, 과기정통부

- E. Machery, C. Y. Olivola, H. Cheon, I. T. Kurniawan, C. Mauro, N. Struchiner, and H. Susianto, (Forthcoming.) “Is Identity Essentialism a Fundamental Feature of Human Cognition?” Cognitive Science.
- 이한슬, 천현득 (2023). 「알고리듬 투명성을 이해하기」, 『과학철학』 26(1): 31-58.
- Cheon, H. (2022). "A new direction for global epistemology", Metascience 31(2), 195-198
- 이한슬, 천현득 (2021). 「인공지능 윤리에서 해명가능성 원리」, 『인문학연구』 35집: 37-63.
- 천현득 (2020). 「지각의 인지적 침투와 관찰의 이론적재성」, 『과학철학』 23(1): 75-107.
- 천현득 (2019). 「인공 반려의 유혹: 인공물과의 교감을 생각한다」, 『과학철학』 22(2): 27-52.
- 천현득 (2019). 「인공지능의 존재론: 이미 도래했으나 아직 실현되지 않은 존재를 사유하기」, 『쓺-문학의 이름으로』 8집: 7-25.
- 천현득 (2019). 「"킬러 로봇"을 넘어: 자율적 군사로봇의 윤리적 문제들」, Trans-Humanities 12(2): 5-31.
- 천현득 (2017). 「인공 지능에서 인공 감정으로: 감정을 가진 기계는 실현가능한가?」, 『철학』 131: 217-243.
- Cheon, H. and E. Machery, (2016). “Scientific Concepts”, Oxford Handbook for the Philosophy of Science (ed. by Paul Humphreys)

Contextual multi-armed bandit algorithm for semiparametric reward model. presented at ICML 2019
Doubly Robust Lasso Bandit. presented at NeurIPs 2019
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric.  presented at ACML 2019 and published at Machine Learning
Lipschitz continuous autoencoders in application to anomaly detection.  accepted for AISTAT 2020
Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. published at CSDA
딥러닝의 통계적 접근: 의료영상자료를 위한 합성곱 신경망 모형의 새로운 통계적 추론 방법 연구 (연구재단 중견연구, 2017-03-01 - 2020-02-29)

"Passivated co-doping approach to bandgap narrowing of titanium dioxide with enhanced photocatalytic activity”, Applied Catalysis B: Environmental 200, 1 (2017)
"Novel J eff= 1/2 Mott state induced by relativistic spin-orbit coupling in Sr2IrO4”, Physical Review Letters 101, 076402 (2008)
"O(N) LDA+U electronic structure calculation method based on the nonorthogonal pseudoatomic orbital basis”, Physical Review B 73, 045110 (2006)
"Magnetic ordering at the edges of graphitic fragments: Magnetic tail interactions between the edge-localized states”, Physical Review B 72, 174431 (2005)
"Electronically Driven Instabilities and Superconductivity in the Layered La2-xMxCuO4 Perovskites", Physical Review Letters 58, 1035 (1987)
"Center for Strongly Correlated Materials Research (SRC)", 한국연구재단, 1999-2008

Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs. JAMA Netw Open. 2 (3), e191095 2019 Mar 1
Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology. 290 (1), 218-228 Jan 2019
Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs. Clin Infect Dis. 69 (5), 739-747 2019 Aug 16
Deep Learning for Chest Radiograph Diagnosis in the Emergency Department. Radiology. 293 (3), 573-580 Dec 2019
CT-based Deep Learning Model to Differentiate Invasive Pulmonary Adenocarcinomas Appearing as Subsolid Nodules Among Surgical Candidates: Comparison of the Diagnostic Performance With a Size-Based Logistic Model and Radiologists. Eur Radiol. 2020 Feb 13[Online ahead of print]
2017.03.-2020.02. 인공지능 딥러닝 알고리즘을 이용한 폐결절 분류 및 폐암 진단 시스템 개발. 한국연구재단 (미래창조과학부)
2017.11-2019.10.31. 다기관 의료영상 인공지능 연구를 위한 보안 클라우드 플랫폼. 서울시 산학연 협력사업 (서울시)

Heewoong Park and Jonghun Park, "Assessment of Word-Level Neural Language Models for Sentence Completion", Applied Sciences, Vol. 10, No. 4, Feb 2020
In-Beom Park, Jaeseok Huh, Joongkyun Kim, and Jonghun Park, "A Reinforcement Learning Approach to Robust Scheduling of Semiconductor Manufacturing Facilities", to appear in IEEE Transactions on Automation Science and Engineering, 2020
Jonggwon Park, Kyoyun Choi, Sungwook Jeon, Dokyun Kim and Jonghun Park, "A Bi-directional Transformer for Musical Chord Recognition", to appear in Proc. of the 20th International Society for Music Information Retrieval Conference (ISMIR) 2019, Delft, Netherlands
Heewoong Park, Sukhyun Cho, Kyubyong Park, Namju Kim, and Jonghun Park, "TRAINING UTTERANCE-LEVEL EMBEDDING NETWORKS FOR SPEAKER IDENTIFICATION AND VERIFICATION", Proc. of InterSpeech 2018
Moon-jung Chae, Kyubyong Park, Jinhyun Bang, Soobin Suh, Jonghyuk Park, Namju Kim, and Jonghun Park, "CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION", Proc. of ICASSP 2018
신경망 구조 탐색을 위한 메타러닝 기법 및 생성형 모형 기반 이상치 탐지 기술 연구, 카카오브레인, 2019.04.01~2020.03.31
잠재공간의 효과적 제어를 통한 심층신경망의 시퀀스 데이터 생성 기법 연구, 한국연구재단, 2019.6.1 - 2022.5.31
Deep Reinforcement Learning을 활용한 지능형 Real-Time Scheduling/Dispatching, 뉴로코어, 2019. 6. - 2019.11.

Hyeongu Yun, Yongkeun Hwang and Kyomin Jung, Improving Context-Aware Neural Machine Translation Using Self-Attentive Sentence Embedding , AAAI Conference on Artificial Intelligence (AAAI), Jan 2020, New York City, New York, USA
Hyoungwook Nam, Segwang Kim, Kyomin Jung, Number Sequence Prediction Problems and Computational Powers of Neural Network Models , AAAI Conference on Artificial Intelligence (AAAI)- (Oral), Jan 2019, Honolulu, Hawaii, USA
Seunghyun Yoon, Joongbo Shin, Kyomin Jung, Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering , Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), June 2018, New Orleans, LA, USA
논리적 추론을 위한 딥러닝 아키텍쳐 개발, 삼성전자 미래기술육성센터, 2019-2022
대화 상황과 감정 인지형 인공지능 대화 시스템 개발, KEIT, 2017-2022

Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang, Knowledge Extraction with No Observable Data, NeurIPS 2019, Vancouver, Canada.
Jaemin Yoo, Hyunsik Jeon, and U Kang, Belief Propagation Network for Hard Inductive Semi-supervised Learning, 28th International Joint Conference on Artificial Intelligence (IJCAI) 2019, Macao, China.
Junghwan Kim, Haekyu Park, Ji-Eun Lee, and U Kang, SIDE: Representation Learning in Signed Directed Networks, The Web Conference (WWW) 2018, Lyon, France.
Minji Yoon, Woojeong Jin, and U Kang, Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees, The Web Conference (WWW) 2018, Lyon, France.
Jun-gi Jang, Dongjin Choi, Jinhong Jung, and U Kang, Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range, ACM International Conference on Information and Knowledge Management (CIKM) 2018, Lingotto, Turin, Italy.
초고속 텐서 스트림 분석을 통한 실시간 경량 다차원 데이터 마이닝, 과학기술정보통신부, 2019 - 2022
시청이력기반 콘텐츠 추천 기술, SK Telecom, 2018
Real-time Anomaly Detection in High-Speed Time-evolving Graphs, AOARD, 2017 - 2018

W. Kim, Y. Ahn and B. Shim, "Deep Neural Network Based Active User Detection for Grant-free NOMA Systems," IEEE Transactions on Communications, 2020.
W. Kim, H. Ji, H. Lee, Y. Kim, J. Lee and B. Shim, "Sparse Vector Transmission: An Idea Whose Time Has Come," IEEE Vehicular Technology Magazine, 2020.
L. Nguyen, J. Kim and B. Shim, "Low-Rank Matrix Completion: A Contemporary Survey," IEEE Access, vol. 7, no. 1, pp. 94215-94237, Jul. 2019.
H. Ji, S. Park, J. Yeo, Y. Kim, J. Lee and B. Shim, "Ultra Reliable and Low Latency Communications in 5G Downlink: Physical Layer Aspects," IEEE Wireless Communications, vol. 25, no. 3, pp. 124-130 , June. 2018.
J. Choi, B. Shim, Y. Ding, B. Rao and D. Kim, "Compressed sensing for wireless communications: useful tips and tricks," IEEE Communications Surveys and Tutorials, vol. 19, no. 3, pp. 1527-1550, 2017.

Development of a Smartphone Application for Dietary Self-Monitoring. Front Nutr. 2019;6:149
Association of depression and anxiety disorder with the risk of mortality in breast cancer: A National Health Insurance Service study in Korea. Breast Cancer Res Treat. 2020;179(2):491-498
Use of a Mobile Application for Self-Monitoring Dietary Intake: Feasibility Test and an Intervention Study. Nutrients. 2017;9(7):pii: E748
Red meat intake, CYP2E1 and PPARγ polymorphisms, and colorectal cancer risk. Eur J Cancer Prev. 2019;28(4):304-310

Hoang DT, Song J, Periwal V, and Jo J. Network inference in stochastic systems from neurons to currencies: Improved performance at small sample size, Phys Rev E, 99:023311 (2019)
Song J, Marsili M, and Jo J. Resolution and relevance trade-offs in deep learning, Journal of Statistical Mechanics, 12:123406 (2018)
Hoang DT, Jo J and Periwal V. Data-driven inference of hidden nodes in networks, Phys Rev E, 99:042114 (2019)
Cubero RJ, Jo J, Marsili M, Roudi Y and Song J. Statistical criticality arises in most informative representations, Journal of Statistical Mechanics, 6:063402 (2019)
Xu J and Jo J. Immunological recognition by articial neural networks, Journal of Korean Physical Society, 73:1908-1917 (2018)
Dynamics Inference from Time Series Data, NRF, 2019.06.01 - 2022.02.28

Optimal Ratcheting in Executive Compensation. Decision Analysis, accepted. with I. Hwang, Y. Kim.

Got Organic Milk? Joint Inventory Model with Supply Uncertainties and Partial Substitution. Operations Research Letters, 49(5), 2021. with D. Jeon, Z. Peng, Y. Rong.

Why Have Voluntary Time-of-Use Tariffs Fallen Short in the Residential Sector? Production & Operations Management, 29(3), 2020. with D.G. Choi, K. Murali, V. Thomas

Money Well Spent? Operations, Mainstreaming, and Fairness of Fair Trade. Production & Operations Management, 28(12), 2019. with H.Y. Mak, S.J. Park

The Effects of Ecolabels and Environmental Regulation on Green Product Development. Manufacturing & Service Operations Management, 21(3), 2019. with K. Murali, N.C. Petruzzi

Promoting Clean Technology Products: To Subsidize Products or Service Infrastructure? Service Science, 11(2), 2019. with G. Ma, H.Y. Mak, Z. Wan

Beyond the Speed-Price Tradeoff. MIT Sloan Management Review, 59(4), Summer, 2018. with J. Acimovic, H.Y. Mak

Learning Discrete and Continuous Factors of Data via Alternating Disentanglement (ICML19)
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization (ICML19)
EMI: Exploration with Mutual Information (ICML19)
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization (CVPR19)
[뉴럴 프로세싱 시스템 연구/16세부]Deep adversarial reinforcement learning via expert video demonstrations, 삼성전자(주)/민간지원사업, 2017-2020
머신러닝 기반 Storage 품질 예측 시스템의 향상을 위한 최적화기반 data augmentation 방법에 관한 연구, 삼성전자(주)/민간지원사업, 2019-2024
데이터간 범용적인 상호 유사성 추론을 위한 딥러닝 모형 연구, 과학기술정보통신부/이공분야기초연구사업전략공모사업, 2017-2020

Wonseop Lee, Hyeong-Seok Ko. Heuristic misfit reduction: A programmable approach for 3D garment fit customization. Computer & Graphics, 71, 1-13.
Dong-Hoon Han, Ick-Hoon Cha, Kyung-Hyun Lee and Hyeong-Seok Ko. Garment Preconditioning and Regional Simulation Omission for Trying-On Virtual Ensembles. Computer Animation and Social Agents 2019.
Goanghun Kim and Hyeong-Seok Ko. A Practical Approach to Physically‐Based Reproduction of Diffusive Cosmetics. Computer Graphics Forum. Volume 37, Number 7.
CCTV 영상으로부터 3D 모델과 딥러닝 및 이미지 품질 개선 기술을 활용한 정면 얼굴 복원 기술 개발, 과학기술정보통신부, 2018~2023.
의류 직물의 3D 재현 연구. 한국연구재단. 2012~2015.
증강현실 기술을 이용한 실시간 매직미러 서비스 기술개발. 정보통신산업진흥원. 2012~2014.

Shin D, Ji S, Lee D, Lee J, Oh SH, Lee J*,
Deep Reinforcement Learning Designed RF Pulse: DeepRF_SLR
arXiv. 2019 Dec;1912.09015.
Jung W*, Steffen Bollmann*, Lee J*
[REVIEW] Overview of quantitative susceptibility mapping using deep learning – Current status, challenges and opportunities
arXiv. 2019 Dec;1912.05410.
Jung W, Yoon J, Ji S, Choi JY, Kim JM, Nam Y, Kim EY, Lee J*
Exploring linearity of deep neural network trained QSM: QSMnet+
Neuroimage 2020 in press
Lee J, Lee D, Choi JY, Shin D, Shin HG, Lee J*
Artificial neural network for myelin water imaging
Magn Res Med 2020 83(5):1875-1883
Yoon J, Gong E, Chatnuntawech I, Bilgic B, Lee J, Jung W, Ko J, Jung H, Setsompop K, Zaharchuk G, Kim EY, Pauly J, Lee J*
Quantitative susceptibility mapping using deep neural network: QSMnet
NeuroImage 2018 179:199-206