Deng, X. and Yuan, M. (2009). Large Gaussian Covariance Matrix Estimation with Markov Structure, Journal of Computational and Graphical Statistics, 18(3), 640-657.

Deng, X. and Tsui, K. W. (2013). Penalized Covariance Matrix Estimation using a Matrix-Logarithm Transformation, Journal of Computational and Graphical Statistics, 22(2), 494-512.

Zheng, H., Tsui, K-W, Kang, X. and Deng, X. (2017). Cholesky-based Model Averaging for Covariance Matrix Estimation, Statistical Theory and Related Fields, 1(1), 48-58.

Nino-Ruiz, E. D., Sandu, A., and Deng, X. (2017). A Parallel Ensemble Kalman Filter Implementation Based on Modified Cholesky Decomposition, Journal of Computational Science, 36, 100654.

Nino-Ruiz, E. D., Sandu, A., and Deng, X. (2018). An Ensemble Kalman Filter Implementation Based On Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation, SIAM Journal on Scientific Computing, 40(2), A867-A886.

Kang, X., Deng, X., Tsui, K. and Pourahmadi, M. (2019). On Variable Ordination of Modified Cholesky Decomposition for Estimating Time-Varying Covariance Matrices, International Statistical Review, in press.

Kang, X., and Deng, X. (2019). An Improved Modified Cholesky Decomposition Method for Inverse Covariance Matrix Estimation, Journal of Statistical Computation and Simulation, in press.

Kang, X., and Deng, X. (2021). On Variable Ordination of Modified Cholesky Decomposition for Sparse Covariance Matrix Estimation, Canadian Journal of Statistics, 49(2), 283-310.

Tang, P., Jiang, H., Kim, H., and Deng, X. (2021). Robust Estimation of Sparse Precision Matrix using Adaptive Weighted Graphical Lasso Approach, Journal of Nonparametric Statistics, 33(2), 249-272.

Kang, X., Lian, J., and Deng, X. (2023). On Block Cholesky Decomposition for Sparse Inverse Covariance Estimation, Statistica Sinica, in press.

Deng, X., Yuan, M. and Sudjianto A. (2007). A Note on Robust Kernel Principal Component Analysis, Contemporary Mathematics, 443, 21-33.

Shao, J., Wang, Y., Deng, X., and Wang, S. (2011). Sparse Linear Discriminant Analysis by Thresholding for High Dimensional Data, Annals of Statistics, 39(2), 1241-1265.

Shao, J., and Deng, X. (2012). Estimation in High-Dimensional Linear Models with Deterministic Covariates, Annals of Statistics, 40(2), 812-831.

Lozano, A. C., Jiang, H. J., and Deng, X. (2013). Robust Joint Sparse Estimation of Multiresponse Regression and Inverse Covariance Matrix, 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2013), 293-301. (Acceptance rate 25%).

Wu, H., Deng, X., and Ramakrishnan, N. (2018). Sparse Estimation of Multivariate Poisson Log-Normal Model and Inverse Covariance for Counting Data , Statistical Analysis and Data Mining, 11, 66-77.

Xie, Y., Xu L., Li, J., Deng, X., Hong, Y., and Kolivras, K. N. (2019). Spatial Variable Selection via Elastic Net with an Application to Virginia Lyme Disease Case Data, Journal of the American Statistical Association, in press.

Chan, V., Tsui, K-W, Wei, Y., Zhang, Z. and Deng, X. (2021). Efficient Estimation of Smoothing Spline with Exact Shape Constraints, Statistical Theory and Related Fields, 5(1), 55-69.

Chu. S, Jiang, H., Xue. Z., and Deng, X. (2021). Adaptive Convex Clustering of Generalized Linear Models with Application in Purchase Likelihood Prediction, Technometrics, 63(2), 171-183.

Kang, X., Chen, X., Jin, R., Wu, H., and Deng, X. (2021). Multivariate Regression of Mixed Responses for Evaluation of Visualization Designs, IISE Transactions, 53(3), 313-325.

Xie, W., and Deng, X. (2020). Scalable Algorithms for the Sparse Ridge Regression, SIAM Journal on Optimization, 30(4), 3359-3386.

Kang, X., Ranganathan, S., Kang, L., Gohlke, J., and Deng, X. (2021). Bayesian Auxiliary Variable Model for Birth Records Data with Qualitative and Quantitative Responses, Journal of Statistical Computation and Simulation, 91(16), 3283-3303.

Kang, X., Kang, L., Chen, W., and Deng, X. (2022). A Generative Approach to Modeling Data with Qualitative and Quantitative Responses, Journal of Multivariate Analysis, 190, 104952.

Liang, Q., Ranganathan, S., Wang, K., and Deng, X. (2022). JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment-Topic Prediction, Technometrics, in press.

Wang, Y., Lee, I-C, Hong, Y., and Deng, X. (2022). Building Degradation Index with Variable Selection for Multivariate Sensory Data, Reliability Engineering and System Safety, in press.

Chu, S., Liu, X., Marathe, A., and Deng, X. (2023). A Latent Process Approach for Change-Point Detection of Mixed-Type Observations, Quality Engineering, in press.

Liu, X., Hu, Z., Kuhlman, C. J. and Deng, X. (2023). Learning Common Knowledge Networks Via Exponential Random Graph Models, The IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2023) (Acceptance rate 17%).

Xu, L., Hong, Y., Smith, E. P., McLeod, D. S., Deng, X., and Freeman, L. J. (2023). Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data, Journal of Agricultural, Biological, and Environmental Statistics, in press.

Shen, S., Zhang, Z., Jin, R., and Deng, X. (2024). Sparse Estimation and Variable Selection for Dynamic Generalized Linear Models, IISE Transactions, in press.

Wei, Y., Myers, W., and Deng, X. (2024). An Efficient Filtering Approach for Model Estimation in Sparse Regression, Statistical Analysis and Data Mining, accepted.

Deng, X., Hung, Y. and Lin, C. D. (2015). Design for Computer Experiments with Qualitative and Quantitative Factors, Statistica Sinica, 25, 1567-1581.

Jiang, H. J., Deng, X., Lopez, V., and Hamann, H. (2016). Online Updating of Computer Model Output Using Real-time Sensor Data, Technometrics, 58(4), 472-482.

Deng, X., Lin, C. D., Liu, K-W, and Rowe, R. K. (2017) Additive Gaussian Process for Computer Models with Qualitative and Quantitative Factors, Technometrics, 59(3), 283-292.

Kang, X., and Deng, X. (2020). Design and Analysis of Computer Experiments with Quantitative and Qualitative Inputs: A Selective Review, WIREs Data Mining and Knowledge Discovery, in press.

Xiao, Q., Mandal, A., Lin, C. D., and Deng, X. (2021). EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors, SIAM/ASA Journal on Uncertainty Quantification, 9(2), 333-353.

Zhang, F., Chen, R-B, Hung, Y., and Deng, X. (2023). Indicator-based Bayesian Variable Selection for Gaussian Process Models in Computer Experiments, Computational Statistics and Data Analysis, in press.

Cai, X., Xu, L., Lin, C. D., Hong, Y., and Deng, X. (2023). Adaptive-Region Sequential Design with Quantitative and Qualitative Factors in Application to HPC Configuration, Journal of Quality Technology, in press.

Liu, X., Hu, Z., Deng, X., and Kuhlman, C. J. (2023). Uncertainty Visualization for Characterizing Heterogeneous Human Behaviors in Discrete Dynamical System Models, Advances in Complex Systems, in press.

Hu, Z., Liu, X., Deng, X., and Kuhlman, C. J. (2024). An Uncertainty Quantification Framework for Agent-Based Modeling and Simulation in Networked Anagram Games, Journal of Simulation, in press.

Morgan, J.P. and Deng, X. (2011). Experimental Design, WIREs Data Mining and Knowledge Discovery, 2, 164-172.

Shen, S., Kang, L., and Deng, X. (2019). Additive Heredity Model for the Analysis of Mixture-of-Mixtures Experiments, Technometrics, in press.

Shen, S., Zhang, Z., and Deng, X. (2019). On Design and Analysis of Funnel Testing Experiments in Webpage Optimization, Journal of Statistical Theory and Practice, in press.

Cedeno-Mieles, V., Hu, Z., Deng, X., Ren, Y., et al. (2019). Networked Experiments and Modeling for Producing Collective Identity in a Group of Human Subjects Using an Iterative Abduction Framework, Social Network Analysis and Mining, in press.

Li, Y., and Deng, X. (2021). A Sequential Algorithm for Elastic I-Optimal Design of Generalized Linear Models, Canadian Journal of Statistics, 49(2), 438-470.

Cedeno-Mieles, V., Ren, Y., Hu, Z., Deng, X., et al. (2020). Data Analysis and Modeling Pipelines for Controlled Networked Social Science Experiments, PLoS One, 15(11), e0242453.

Zeng, Y., Chen, X., Deng, X., and Jin R. (2021). A Prediction-Oriented Optimal Design for Visualization Recommender System, Statistical Theory and Related Fields, 5(2), 134-148.

Li, Y. and Deng, X. (2021). On Efficient Design of Pilot Experiments for Generalized Linear Models, Journal of Statistical Theory and Practice, 15, article 83.

Li, Y., Kang, L., and Deng, X. (2022). A Maximin Φp-Efficient Design for Multivariate Generalized Linear Models, Statistica Sinica, 32, 2047-2069.

Shen, S., Mao, H., Zhang, Z., Chen, Z., Nie, K., and Deng, X. (2023). Clustering-based Imputation for Dropout Buyers in Large-scale Online Experimentation, New England Journal of Statistics in Data Science, in press.

Kang, L., Deng, X., and Jin, R. (2023). Bayesian D-Optimal Design of Experiments with Quantitative and Qualitative Responses, New England Journal of Statistics in Data Science, in press.

Jin, R. and Deng, X. (2015). Ensemble Modeling for Data Fusion in Manufacturing Process Scale-up., IIE Transactions, 47(3), 203-214.

Deng, X. and Jin, R. (2015). QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing Systems, Technometrics, 57(3), 320-331.

Sun, H., Deng, X., Wang, K., and Jin, R. (2016). Logistic Regression for Crystal Growth Process Modeling through Hierarchical Nonnegative Garrote based Variable Selection, IIE Transactions, 48(8), 787-796.

Sun, H., Rao, P. K., Kong, Z., Deng, X., and Jin, R. (2018). Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process, IEEE Transactions on Automation Science and Engineering, 15(1), 393-403.

Kang, L., Kang X., Deng, X., and Jin, R. (2018). Bayesian Hierarchical Models for Quantitative and Qualitative Responses, Journal of Quality Technology, 50(3), 290-308.

Jin, R., Deng, X., Chen, X., Zhu, L., and Zhang, J. (2019). Dynamic Quality Models in Consideration of Equipment Degradation, Journal of Quality and Technology, 51(3), 217-229.

Li, Y., Jin, R., Sun, H., Deng, X., and Zhang, C. (2019). Manufacturing Quality Prediction Using Smooth Spatial Variable Selection Estimator with Applications in Aerosol Jet Printed Electronics Manufacturing, IISE Transactions, in press.

Shen, S., Mao, H., and Deng, X. (2019). An EM-Algorithm Approach to Open Challenges on Correlation of Intermediate and Final Measurements, Quality Engineering, 31(3), 505-510.

Shen, S., Mao, H., and Deng, X. (2019). Rejoinder: An EM-Algorithm Approach to Open Challenges on Correlation of Intermediate and Final Measurements, Quality Engineering, 31(3), 516-521.

Wang, H., Zhang, Q., Wang, K., and Deng, X. (2020). A Statistics-Guided Approach to Dimensional Quality Characterization of Freeform Surfaces with an Application to 3D Printing, Quality Engineering, in press.

Wang, L., Chen, X., Kang, S., Deng, X., and Jin, R. (2021). Meta-modeling of High-Fidelity FEA Simulation for Efficient Product and Process Design in Additive Manufacturing, Additive Manufacturing, 35, 101211.

Li, Y., Deng, X., Ba, S., Myers, W. R., et al. (2021). Clustering-based Data Filtering for Manufacturing Big Data Systems, Journal of Quality and Technology, in press.

Kang, S., Deng, X., and Jin R. (2021). Cost-Efficient Data-Driven Approach to Design Space Exploration for Personalized Geometric Design in Additive Manufacturing, Journal of Computing and Information Science in Engineering, 21(6), 061008.

Kang, S., Jin R., Deng, X., and Kenett, R. S. (2021). Challenges of Modeling and Analysis in Cybermanufacturing: A Machine Learning and Computation Perspective, Journal of Intelligent Manufacturing, in press.

Peng, X., Salado, A., and Deng, X. (2022). A Parallel Tempering Approach for Efficient Exploration of the Verification Tradespace in Engineered Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press.

Kenett, R. S., Gotwalt, C., Freeman, L., and Deng, X. (2022). Self-Supervised Cross Validation using Data Generation Structure, Applied Stochastic Models in Business and Industry, in press.

Chen, X., Kang, X., Jin, R., and Deng, X. (2022). Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing, Technometrics, in press.

Peng, X., Deng, X., and Salado, A. (2023). A UCB-based Tree Search Approach to Joint Verification-Correction Strategy for Large Scale Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press.

Nanotechnology and Quantification

Deng, X., Joseph V. R., Mai, W., Wang, Z. L. and Wu, C. F. J. (2009). A Statistical Approach to Quantifying the Elastic Deformation of Nanomaterials, Proceedings of the National Academy of Sciences, 106(29), 11845-11850.

Mai, W., and Deng, X. (2010). Applications of Statistical Quantification Techniques in Nanomechanics and Nanoelectronics, Nanotechnology, 21(40), 405704.

Zhang, Q., Deng, X., Qian, P. Z. G., and Wang, X. (2013). Spatial Modeling for Refining and Predicting Surface Potential Mapping with Enhanced Resolution., Nanoscale, 5, 921-926.

Wang, X., Wu, S., Wang, K., Deng, X., Liu, L., and Cai, Q. (2016). Spatial Calibration Model for Nanotube Film Quality Prediction, IEEE Transactions on Automation Science and Engineering, 13(2), 903-917.

Enviromental Science

Li, H., Deng, X., Kim, D-Y and Smith. E. (2014). Modeling Maximum Daily Temperature Using a Varying Coefficient Regression Model., Water Resource Research, 50(4), 3073-3087.

Li, H., Deng, X., Dolloff, A., and Smith E. (2016). Bivariate Functional Data Clustering: Grouping Streams based on a Varying Coefficient Model of the Stream Water an Air Temperature Relationship, Environmetrics, 27(1), 15-26.

Li, H., Deng, X., and Smith, E. P. (2017). Missing Data Imputation for Paired Stream and Air Temperature Sensor Data, Environmetrics, 28(1), e2426.

Wan, H., McLaughlin, D., Yang, S., van Eerden, B., Ranganathan, S., and Deng, X. (2021). Remotely-sensed Evapotranspiration for Informed Urban Forest Management, Landscape and Urban Planning, 210, 104069.

Tissue Engineering and Health Analytics

Zeng, L., Deng, X., and Yang, J. (2016). Constrained Hierarchical Modeling of Degradation Data in Tissue-engineered Scaffold Fabrication, IIE Transactions, 48(1), 16-33.

Zeng, L., Deng, X., and Yang, J. (2018). A Constrained Gaussian Process Approach to Modeling Tissue-engineered Scaffold Degradation, IISE Transactions, 50(5), 431-447.

Wu, Q., Deng, X., Wang, S., and Zeng, L. (2021). Constrained Varying-Coefficient Model for Time-Course Experiments in Soft Tissue Fabrication, Technometrics, 63(2), 249-262.

Xie, K., Marathe, A., Deng, X., et al. (2023). Alternative Approaches for Creating Wealth Index: Case of Mozambique, BMJ Global Health, in press.

Kamruzzaman, M., Heavey, J., Song, A., Bielskas, M., Bhattacharya, P., Madden, G., Klein, E., Deng, X., and Vullikanti, A. (2024). Improving Risk Prediction of Methicillin-Resistant Staphylococcus Aureus (MRSA) using Network Features, Journal of Medical Internet Research-AI, in press.

Driving Risk Analytics

Mao, H., Deng, X., Lord, D., Guo, F. (2019). Adjusting Finite Sample Bias for Poisson and Negative Binomial Regression in Traffic Safety Modeling, Accident Analysis and Prevention, in press.

Mao, H., Deng, X., Jiang, H., Shi, L., Li, H., Tuo, L., and Guo, F. (2021). Driving Safety Assessment for Ride-hailing Drivers, Accident Analysis and Prevention, 149, 105574.

Mao, H., Guo, F., Deng, X., and Doerzaph, Z. (2021). Decision-adjusted Driver Risk Predictive Models using Kinematics Information, Accident Analysis and Prevention, 156, 106088.

Deng, X., Joseph, V. R., Sudjianto A. and Wu, C. F. J. (2009). Active Learning via Sequential Design with Applications to Detection of Money Laundering, Journal of the American Statistical Association, 104(487), 969-981.

Cadena, J., Basak, A., Vullikanti, A., and Deng, X. (2018). Graph Scan Statistics with Uncertainty, 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2771-2778. (Acceptance rate 25%).

Ren, Y., Cedeno-Mieles, V., Hu, Z., Deng, X., et al. (2018). Generative Modeling of Human Behavior and Social Interactions Using Abductive Analysis, 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018), 413-420. (Acceptance rate 15%).

Cedeno-Mieles, V., Hu, Z., Deng, X., Ren, Y., et al. (2019). Mechanistic and Data-Driven Agent-Based Models to Explain Human Behavior in Web-Based Group Anagrams Games, 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019) (Acceptance rate 14%).

Lian, J., Freeman, L., Hong, Y., and Deng, X. (2021). Robustness with Respect to Class Imbalance in Artificial Intelligence Classification Algorithms, Journal of Quality Technology, 53(5), 505-525.

Hong, Y., Lian, J., Xu, L., Min, J., Wang, Y., Freeman, L., and Deng, X. (2022). Statistical Perspectives on Reliability of Artificial Intelligence Systems, Quality Engineering, in press.

Guo, Q., Chen, J., Wang, D., Yang, Y., Deng, X., Huang J., Carin, L., Tao, C. and Li, F. (2022). Tight Mutual Information Estimation with Contrastive Fenchel-Legendre Optimization, Advances in Neural Information Processing Systems (NeurIPS 2022), accepted.

Xiao, Q., Wang, Y., Mandal, A., and Deng, X. (2023). Modeling and Active Learning for Experiments with Quantitative-Sequence Factors, Journal of the American Statistical Association, in press.

Guo, Q., Wanigasekara, P., Zheng, J., Fang, Z., Deng, X., and Tao, C. (2023). How Do Large Multimodal Models Really Fare in Classical Vision Few-Shot Challenges? A Deep Dive, Robustness of Zero/Few-shot Learning in Foundation Models Workshop, NeurIPS 2023, accepted.

Lian, J., Liu, X., Choi, K., Veeramani, B., Hu, A., Freeman, L., Bowen, E., and Deng, X. (2024). Data Composition for Continual Learning in Application of Cyberattack Detection, The International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2024), accepted.

Lian, J., Choi, K., Veeramani, B., Hu, A., Murli, S., Freeman, L., Bowen, E., and Deng, X. (2024). Continual Learning and Its Industrial Applications: A Selective Review, WIREs Data Mining and Knowledge Discovery, accepted.