Applied Statistics

Machine Learning

Data Science

Artificial Intelligence

Quality Engineering

[J60] H. Kim, C. Park, and H. Kim (2024), "Multi-task optimization with Bayesian neural network surrogates for parameter estimation of a simulation model," Computational Statistics and Data Analysis, accepted.

[C12] T. Park, H. Lee, and H. Kim (2024), "Rebalancing Using Estimated Class Distribution for Imbalanced Semi-Supervised Learning under Class Distribution Mismatch,"  European Conference on Computer Vision (ECCV) 2024, Milan, Italy, September 2024. 

[C11] T. Yoon and H. Kim (2024), "Uncertainty Estimation by Density Aware Evidential Deep Learning,"  International Conference on Machine Learning (ICML), Vienna, Austria, July 2024. 

[J59] H. Kim, H. Yoon, and H. Kim (2024), "Few-shot Classification of Wafer Bin Maps Using Transfer Learning and Ensemble Learning," ASME Journal of Manufacturing Science and Engineering, 146(7), 070903.

[J58] H. Lee, H. Kim, and H. Kim (2024), "Classification of Chip-level Defect Types in Wafer Bin Maps Using Only Wafer-level Labels," ASME Journal of Manufacturing Science and Engineering, 146(7), 070902.

[J57] E.-Y. Ma, U. Lee, and H. Kim (2024), "Simultaneous Treatment Effect Estimation and Variable Selection for Observational Data," IISE Transactions, accepted.

[C10] H. Lee and H. Kim (2024), "CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning,"  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024, Seattle, USA, June 2024. 

[J56] W. Koo,  E.-Y. Ma, and H. Kim (2024), "Deep Latent Factor Model for Spatio-Temporal Forecasting," Technometrics, 66(3), 470-482. (Finalist of IISE Quality Control and Reliability Engineering (QCRE) Track Best Paper Competition)

[J55] G. Jung,  S. Park, E.-Y. Ma, H. Kim, and U. Lee (2024), "A Tutorial on Matching-based Causal Analysis of Human Behaviors Using Smartphone Sensor Data," ACM Computing Surveys, 56(9), 1-33.

[J54] J. Choi,  E.-Y. Ma, and H. Kim (2024), "Simultaneous Classification and Out-of-Distribution Detection for Wafer Bin Maps ," Quality Engineering, 36(4), 713-725.

[J53] W. Doo and H. Kim (2024), "Simultaneous Deep Clustering and Feature Selection via K-concrete Autoencoder," IEEE Transactions on Knowledge and Data Engineering, 36, 2629-2642.

[J52] S. Kim and H. Kim (2024), "Mixed-Type Defect Pattern Recognition in Noisy Labeled Wafer Bin Maps," Quality Engineering, 36(4), 743-757.

[J51] J. Jung,  S. Kim, and H. Kim (2024), "Spatially-Correlated Time Series Clustering Using Location-Dependent Dirichlet Process Mixture Model," Statistical Analysis and Data Mining, 17(1), e11649.

[J50] H. Cho, W. Koo, and H. Kim (2023), "Prediction of Highly Imbalanced Semiconductor Chip-Level Defects in Module Tests Using Multimodal Fusion and Logit Adjustment," IEEE Transactions on Semiconductor Manufacturing, 36(3), 425-433.

[J49] H. Lee, J. Lee, and H. Kim (2023), "Semi-supervised learning for simultaneous location detection and classification of mixed-type defect patterns in wafer bin maps," IEEE Transactions on Semiconductor Manufacturing, 36(2), 220-230.

[J48] H. Kim and H. Kim (2023), "Contextual anomaly detection for multivariate time series data," Quality Engineering, 35(4), 686-695.

[C9] K. Kim, E.-Y. Ma, J. Choi, and H. Kim (2023), "Inverse-Reference Priors for Fisher Regularization of Bayesian Neural Networks,"  Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23), Washington, DC, USA, February 2023. 

[J47] H. Kim and H. Kim (2023),  "Contextual anomaly detection for high-dimensional data using Dirichlet process variational autoencoder,"  IISE Transactions, 55(5), 433-444. 

[J46] H. Yoon and H. Kim (2023), "Label-Noise Robust Deep Generative Model for Semi-Supervised Learning," Technometrics, 65(1), 83-95.

[J45] S. Park, K. Kim, and H. Kim (2023),  "Prediction of highly imbalanced semiconductor chip-level defects using uncertainty-based adaptive margin learning,"  IISE Transactions, 55(2), 147-155. 

[J44] H. Kim and H. Kim (2023),  "Deep embedding kernel mixture networks for conditional anomaly detection in high-dimensional data," International Journal of Production Research , 61(4), 1101-1113. 

[J43] U. Lee, G. Jung, E.-Y. Ma, J. S. Jim, H. Kim, J. Aikhanov, Y. Noh,  and H. Kim (2023), "Toward data-driven digital therapeutics analytics: literature review and research directions," IEEE/CAA Journal of Automatica Sinica, 10(1), 42-66.

[J42] J. Ko and H. Kim (2022), "Deep Gaussian process models for integrating multifidelity experiments with non-stationary relationships," IISE Transactions, 54(7),  686-698. (Selected as a featured article and highlighted in the ISE magazine)

[J41] W. Doo and H. Kim (2022),  "Simultaneous band selection and segmentation of hyperspectral images via a mixture of finite maximum margin mixtures," International Journal of Remote Sensing , 43(6), 2296-2314.

[J40]  W.-S. Yun, S. Ko, M. Byun, H. Kim, I.-C. Moon, and T.-E. Lee (2022),  "Toward robust battle experimental design for command and control of mechanized infantry brigade," Military Operations Research, 27(1), 45-72.

[C8] H. Lee, S. Shin, and H. Kim (2021),  "ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning,"  Conference on Neural Information Processing Systems (NeurIPS) 2021.

[C7] K. Kim,  J. Shin,  and H. Kim (2021),  "Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models," Conference on Neural Information Processing Systems (NeurIPS) 2021.

[C6] T. Jeong  and H. Kim (2021),  "Objective Bound Conditional Gaussian Process for Bayesian Optimization,"  International Conference on Machine Learning (ICML) 2021.

[J39] P. Tang, H. Jiang, H. Kim, and X. Deng (2021), "Robust estimation of sparse precision matrix using adaptive weighted graphical Lasso approach," Journal of Nonparametric Statistics, 33, 249-272. 

[J38] W. Doo and H. Kim (2021), "Bayesian Variable Selection in Clustering High-Dimensional Data via a Mixture of Finite Mixtures," Journal of Statistical Computation and Simulation, 91, 2551-2568.

[J37] E.-Y. Ma, J.-W. Kim, Y. Lee, S.-W. Cho,  H. Kim, and J. K. Kim (2021), "Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea," Scientific Reports, 11, 4457. 

[C5] T. Jeong  and H. Kim (2020), "OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification," Conference on Neural Information Processing Systems (NeurIPS) 2020.

[J36] H. Lee and H. Kim (2020), "Semi-Supervised Multi-Label Learning for Classification of Wafer Bin Maps with Mixed-Type Defect Patterns ," IEEE Transactions on Semiconductor Manufacturing , 33(4), 653-662.

[J35] S. Kim, R. Duan, G.-Q. Ma,  and H. Kim (2020), "Multiresolution spatial generalized linear mixed model for integrating multi-fidelity spatial count data without common identifiers between data sources ," Spatial Statistics , 39, 100467.

[J34] Y. Hyun  and H. Kim (2020), "Memory-augmented convolutional neural networks with triplet loss for imbalanced wafer defect pattern classification  ," IEEE Transactions on Semiconductor Manufacturing, 33(4), 622-634.

[J33] W. Koo and H. Kim (2020), "Bayesian nonparametric latent class model for longitudinal data," Statistical Methods in Medical Research, 29(11), 3381-3395.

[J32] J. Hwang  and H. Kim (2020), "Variational deep clustering of wafer map patterns ," IEEE Transactions on Semiconductor Manufacturing, 33(3), 466-475.

[J31] Y. Lee and H. Kim (2020), "Bayesian nonparametric joint mixture model for clustering spatially correlated time series," Technometrics, 62(3), 313-329.

[J30] Y. Lee, T. Jeong, and H. Kim (2020) , "A Bayesian nonparametric mixture measurement error model with application to spatial density estimation using mobile positioning data with multi-accuracy and multi-coverage," Technometrics, 62(2),  173-183.

[J29] T. Ko and H. Kim (2020), "Fault classification in high-dimensional complex processes using semi-supervised deep convolutional generative models," IEEE Transactions on Industrial Informatics, 16(4), 2868-2877.

[J28] K. Kim, H. Kim, V. Kim, and H. Kim (2020), "A multiscale spatially varying coefficient model for regional analysis of topsoil geochemistry," Journal of Agricultural, Biological, and Environmental Statistics, 25, 74-89. 

[C4] T. Jeong, Y. Lee, and H. Kim (2019), "Ladder Capsule Network," International Conference on Machine Learning (ICML), pp. 3071-3079, Long Beach, CA, USA, June 2019. (Acceptance rate: 22.6%)

[J27] K. Kim, V. Kim, and H. Kim (2019), "Spatio-temporal autoregressive model for origin-destination air passenger  flows," Journal of the Royal Statistical Society: Series A, 182(3), 1003-1016.

[J26] J. Chung and H. Kim (2019), "Crime risk maps: multivariate spatial analysis of crime data," Geographical Analysis, 51(4), 475-499.

[J25] H. Kim, R. Duan, S. Kim, J. Lee, and G.-Q. Ma (2019), "Spatial cluster detection in mobility networks: a copula approach," Journal of the Royal Statistical Society: Series C, 68(1), 99-120.

[J24] S. Kim, H. Kim, and J.-C. Lu (2019), "A practical approach to measuring the impacts of stockouts on demand," Journal of Business & Industrial Marketing, 34(4), 891-901.

[J23] W. Doo and H. Kim (2018), "Modeling the probability of a batter/pitcher matchup event: A Bayesian approach," PLoS ONE, 13(1), e0204874.

[J22] H. Kim and H. Kim (2018), "Functional logistic regression with fused lasso penalty," Journal of Statistical Computation and Simulation, 88(15), 2982-2999.

[J21] K. Kyeong and H. Kim (2018), "Classification of mixed-type defect patterns in wafer bin maps using convolutional neural networks," IEEE Transactions on Semiconductor Manufacturing, 31(3), 395-402 .

[J20] J. Kim, Y. Lee, and H. Kim (2018), "Detection and clustering of mixed-type defect patterns in wafer bin maps," IISE Transactions, 50(2), 99-111. (Selected as a featured article and highlighted in the ISE magazine)

[J19] W. Soh, H. Kim, and B.-J. Yum (2018), "Application of kernel principal component analysis to multi-characteristic parameter design problems," Annals of Operations Research, 263, 69-91.

[J18] H. Kim and J. Lee (2017), "Hierarchical spatially varying coefficient process model," Technometrics, 59(4), 521-527.

[J17] K. Kim, H. Zabihi, H. Kim, and U. Lee (2017), "TrailSense: a crowdsensing system for detecting risky mountain trail segments with walking pattern analysis," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), Article 65. (Presented at UbiComp 2017)

[J16] H. Kim, J. T. Vastola, S. Kim, J.-C. Lu, and M. A. Grover (2017), "Incorporation of engineering knowledge into the modeling process: a local approach," International Journal of Production Research, 55(20), 5865-5880.

[J15] Y. Jung and H. Kim (2017), "Detection of PVC by using a wavelet-based statistical ECG monitoring procedure," Biomedical Signal Processing and Control, 36, 176-182.

[J14] S. Kim, H. Kim, and Y. Park (2017), "Early detection of vessel delays using combined historical and real-time information," Journal of the Operational Research Society, 68(2), 182-191.

[J13] H. Kim, J. T. Vastola, S. Kim, J.-C. Lu, and M. A. Grover (2017), "Batch sequential minimum energy design with design region adaptation," Journal of Quality Technology, 49(1), 11-26.

[J12] H. Kim, S. Kim, J. Deng, J.-C. Lu, K. Wang, C. Zhang, M. A. Grover, and B. Wang (2017), "An integrated holistic model of a complex process," International Journal of Advanced Manufacturing Technology, 89(1), 1137-1147.

[C3] M. Choy, D. Kim, J.-G. Lee, H. Kim, and H. Motoda (2016), "Looking back on the current day: interruptibility prediction using daily behavioral features," ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), accepted. (Acceptance rate: 24 %)

[C2] W. S. Lee, Y. Lee, H. Kim, and I.-C. Moon (2016), "Bayesian nonparametric collaborative topic Poisson factorization for electronic health records-based phenotyping," International Joint Conference on Articial Intelligence (IJCAI), accepted. (Acceptance rate: 25 %)

[J11] W. Soh, H. Kim, and B.-J. Yum (2016), "A multivariate loss function approach to robust design of systems with multiple performance characteristics," Quality and Reliability Engineering International, 32(8), 2685-2700.

[J10] S. Kim, H. Kim, and Y. Namkoong (2016), "Ordinal classification of imbalanced data with application in emergency and disaster information services," IEEE Intelligent Systems, 31(5), 50-56.

[J9] S. Kim and H. Kim (2016), "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, 32(3), 669-679.

[J8] S. Kim, H. Kim, R. W. Lu, J.-C. Lu, M. J. Casciato, and M. A. Grover (2015), "Adaptive combined space-filling and D-optimal designs," International Journal of Production Research, 53(17), 5354-5368.

[J7] S. Kim, H. Kim, J.-C. Lu, M. J. Casciato, M. A. Grover, D. W. Hess, R. W. Lu, and X. Wang (2015), "Layers of experiments with adaptive combined design," Naval Research Logistics, 62(2), 127-142.

[J6] H. Kim and X. Huo (2014), "Asymptotic optimality of a multivariate version of the generalized cross validation in adaptive smoothing splines," Electronic Journal of Statistics, 8, 159-183.

[J5] H. Kim, X. Huo, M. Shilling, and H. Tran (2014), "A Lipschitz regularity-based statistical model, with applications in coordinate metrology," IEEE Transactions on Automation Science and Engineering, 11(2), 327-337.

[J4] H. Kim, X. Huo, and Jianjun Shi (2014), "A single interval based classifier," Annals of Operations Research, 216, 307-325.

[J3] H. Kim and X. Huo (2013), "Optimal sampling and curve interpolation via wavelets," Applied Mathematics Letters, 26(7), 774-779.

[J2] K. Lee, A. Gray, and H. Kim (2013), "Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data," Data Mining and Knowledge Discovery, 26(3), 512-532.

[J1] H. Kim and X. Huo (2012), "Locally optimal adaptive smoothing splines," Journal of Nonparametric Statistics, 24(3), 665-680.

[C1] T. Au, R. Duan, H. Kim, and G.-Q. Ma (2010), "Spatiotemporal event detection in mobility networks," IEEE International Conference on Data Mining (ICDM), 28-37. (Acceptance rate: 19%)