Xinwei Deng

Associate Professor

Education

  • Ph.D., School of Industrial and Systems Engineering,
    Georgia Institute of Technology (Aug. 2004 – Aug. 2009)
    Advisors: Professor C. F. Jeff Wu and Professor Ming Yuan
    Major concentration: Statistics
    Minor: Optimization
  • B.S., Mathematics,
    Nanjing University, China (Sep. 1999 – July 2003)

Awards & Honors

  • Recipient of Mentoring Project Award, Virginia Tech, 2012
  • International Travel Supplemental Grant (ITSG) Award, Virginia Tech, 2012
  • NSF Travel Support, International Conference on Robust Statistics (ICORS), 2012
  • QPRC Student Scholarship, Quality and Productivity Research Conference, 2008
  • DAE Conference Support for Junior Researchers, Design and Analysis of Experiments
    Conference, 2007
  • JRC Student Scholarship, Joint Research Conference, 2006
  • Kiplinger Fellowship, Georgia Institute of Technology, 2004, 2005

Professional Membership

  • Member of American Statistical Association
  • Member of International Chinese Statistical Association
  • Member of Institute for Operations Research and the Management Sciences (INFORMS)

  • Stat 6404: Multivariate Analysis: Advanced Topics, Spring 2013
  • Stat 5504: Multivariate Methods, Fall 2012
  • Stat 5304: Statistical Computing, Spring 2012
  • Stat 5525: Data Analytics I, Fall 2011
  • 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
  • Statistical modeling with applications in financial services
  • 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. (2014). Ensemble Modeling for Data Fusion in Manufacturing Process Scale-up, IIE Transactions, to appear. pdf
  • Deng, X., Hung, Y. and Lin, C. D. (2014). Design for Computer Experiments with Qualitative and Quantitative Factors, StatisticaSinica, to appear. pdf
  • Deng, X. and Jin, R. (2014). QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing Systems, Technometrics, accepted.  
  • Jiang, H. J., Deng, X., Lopez, V., and Hamann, H. (2014). Online Updating of Computer Model Output Using Real-time Sensor Data, Technometrics, accepted.
  • Zeng, L., Deng, X., and Yang, J. (2014). Constrained Hierarchical Modeling of Degradation Data in Tissue-engineered Scaffold Fabrication, IIE Transactions, accepted.
  • Wang, X., Wu, S., Wang, K., Deng, X., Liu, L., and Cai, Q. (2014). Spatial Calibration Model for Nanotube Film Quality Prediction, IEEE Transactions on Automation Science and Engineering, accepted.
  • Sun, H., Deng, X., Wang, K., and Jin, R. (2014). Logistic Regression for Crystal Growth Process Modeling through Hierarchical Nonnegative Garrote based Variable Selection, revision for IIE Transactions.
Xinwei Deng
  • 540-231-5638
  • xdeng@vt.edu
  • 211 Hutcheson Hall (MC 0439)
    250 Drillfield Drive
    Blacksburg, VA 24061