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Xinwei Deng

Associate Professor

Department of Statistics

Virginia Tech

211 Hutcheson Hall

Blacksburg, VA 24061

Phone: 540-231-5638

Email: xdeng"at"vt.edu

Curriculum Vitae

Research Interests

·  Interface between design of experiments and machine learning

·  Model and analysis of high-dimensional data

·  Covariance matrix estimation and its applications

·  Statistical methods to Nanotechnology

·  Design and analysis of computer experiments

·  Statistical modeling with applications in financial services

Publications and Reports

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

 

·  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.pdf

 

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

 

·  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.pdf

 

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

 

·  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.pdf

 

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

 

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

 

·  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. pdf

 

·  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. pdf

 

·  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 17.4%) pdf

 

·  Jiang, H. J., Deng, X., Lopez, V., and Hamann, H. (2013). Online Updating and Scheduling of Computer Model with Application to Data Center Thermal Management, Proceedings of ASME IPACK2013, 73042. pdf 

 

·  Moon, J. Y., Chaibub Neto, E., Deng. X., and Yandell, B. S. (2014). Bayesian Causal Phenotype Network Incorporating Genetic Variation and Biological Knowledge, in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics, Oxford University Press.pdf

 

·  Yeo, I-K, Johnson, R. A., and Deng, X. (2014). An Empirical Characteristic Function Approach to Selecting a Transformation to Normality, Communications for Statistical Applications and Methods, 21(3), 213-224. pdf

 

·  Li, H., Deng, X., Kim, D-Y and Smith. E. (2014). A Varying Coefficient Model for Daily Stream Temperatures, Water Resource Research, 50(4), 3073-3087. pdf

 

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

 

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

 

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

 

·  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. pdf

 

·  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. pdf

 

·  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 and Air Temperature Relationship, Environmetrics, 27(1), 15-26. pdf

 

·  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. pdf

 

·  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. pdf

 

·  Deng, X., Hung, Y., and Lin, C. D. (2017). Design and Analysis of Computer Experiments, in Handbook of Research on Applied Cybernetics and Systems Science, IGI Global, 264-279. pdf

 

·  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. pdf

 

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

 

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

 

· 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, in press. pdf

 

·  Sun, H., Rao, P. K., Kong, Z., Deng, X., and Jin, R. (2017). Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process, IEEE Transactions on Automation Science and Engineering, accepted. pdf

 

·  Wu, H., Deng, X., and Ramakrishnan, N.  (2017). Sparse Estimation of Multivariate Poisson Log-Normal Model and Inverse Covariance for Counting Data, Statistical Analysis and Data Mining, accepted. pdf

 
· Nino-Ruiz, E. D., Sandu, A., and Deng, X. (2017). An Ensemble Kalman Filter Implementation Based On Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation, SIAM Journal on Scientific Computing, accepted. pdf
 
· Cadena, J., Basak, A., Deng, X., and Vullikanti, A. (2017). Graph Scan Statistics with Uncertainty. 32nd AAAI Conference on Artificial Intelligence (AAAI-18), accepted. (Acceptance rate 25%) pdf  
 
· Zeng, L. and Deng, X. (2017). A Constrained Gaussian Process Approach to Modeling Tissue-engineered Scaffold Degradation, IISE Transactions, accepted. pdf

 

Submitted

· Kang, L., Deng, X., and Jin, R. (2016). Bayesian D-Optimal Design of Experiments with Quantitative and Qualitative Responses, revision for Journal of the American Statistical Association.

 

· Xie, Y., Li, J., Deng, X., Hong, Y., and Kolivras, K. N. (2017). Spatial Variable Selection via   Elastic Net with an Application to Virginia Lyme Disease Case Data, revision for Journal of the American Statistical Association.

 

· Kang, X., and Deng, X. (2017). Ensemble Estimation of Large Sparse Covariance Matrix Based on the Modified Cholesky Decomposition, submitted to Statistica Sinica.

 

· Kang, X., and Deng, X. (2017). An Improved Modified Cholesky Decomposition Method for Inverse Covariance Matrix Estimation, submitted to Journal of Computational and Graphical Statistics.

 

· Deng, X. and Qian, P. Z. G. (2017). Designs of Simulation Experiments for Estimating Error Rate of a Classification Rule, to be submitted to Journal of Computational and Graphical Statistics.

 

· Zhang, A., Deng, X., Wang, J., and Hobart, J. (2016). A Two-stage Risk Model Construction and Evaluation in Reject Inference, revision for Annals of Applied Statistics.

 

· Jin, R. and Deng, X.  (2016). Dynamic Quality Models for Manufacturing Systems Considering Equipment Degradation, revision submitted to Journal of Quality and Technology.

 

· Peng, T., Jiang, H., Kim, H., and Deng, X. (2016) Robust Estimation of Sparse Gaussian Graphical Model by a Minimum Distance Criterion, revision for Journal of Nonparametric Statistics.

 

· Kang L., Kang X., Deng X. and Jin R. (2016). Bayesian Hierarchical Models for Quantitative and Qualitative Responses, revision for Journal of Quality Technology.

 

· Chu, S., Deng, X., and Marathe, A. (2016). A Latent Process Approach for Change-Point Detection of Mixed-Type Observations, revision for Journal of Quality Technology.

 

· Kang X., Deng X., Tsui K. and Pourahmadi, M (2017). Order-Averaged Cholesky-GARCH Models: Comparison of Asset Ordination Methods, submitted to American Statistician.

 

· Lan, Q., Sun, H., Robertson, J., Deng, X., and Jin R. (2017). Non-invasive Assessment of Liver Quality in Transplantation based on Thermal Imaging Analysis, submitted to Journal of Biomedical Informatics.

 

· Li, Y., Jin, R., Sun, H., Deng, X., and Zhang, C. (2017). Smooth Spatial Variable Selectionfor Quality Prediction in Printed Electronics Manufacturing, submitted to IIE Transactions.

 

· Zhang, A. and Deng, X. (2017). A Regularized Approach to Sparse Linear Discrimination Analysis for Two-class Classification, to be submitted to Journal of Statistical Planning and Inference.

 

· Li, Y. and Deng, X. (2017). A Sequential Algorithm of Constructing I-Optimal Design for Generalized Linear Models, to be submitted to Journal of the American Statistical Association.

 

· Chu. S, Jiang, H., Deng, X., and Xue. Z, (2017). Convex Clustering for Generalized Linear Models with Applications to IT Service Pricing, to be submitted to Technometrics. 

Teaching

·  Stat 5525/CS 5525: Data Analytics I, Fall 2011

·  Stat 5304: Statistical Computing, Spring 2012, Spring 2014

·  Stat 5504: Multivariate Methods, Fall 2012, Fall 2014, Fall 2015, Fall 2016

·  Stat 6404: Advanced Multivariate Analysis, Spring 2013

·  Stat 5526/CS5526: Data Analytics II, Spring 2014, Spring 2016

·  Stat 5204: Experimental Design and Analysis, Spring 2015, Spring 2016, Spring 2017

·  Stat 4204/5204G: Design of Experiments: Concepts and Applications, Spring 2017

·  Stat 6984: Causality Learning, Spring 2017

 

 

Acknowledgement: Research supported by NSF-CMMI-1435996, NSF-CMMI-1233571, NSF-CMMI- 1634867, NSF-CMMI- 1745207, IAPRA, Safe-D National UTC, CCAM, VT-ICTAS, and P&G.