Faming Liang

Professor,  Department of Statistics,  Texas A&M University, College Station, TX 77843-3143.

Office: BLOC 406D;    Phone: (979) 8458885;    Email: fliang@stat.tamu.edu

Courses taught:

  •       Computing for Statistics

  •       Computer Intensive Statistical Methods

  •       Linear Model

  •       Regression Analysis

  •       Probability

  •       Mathematical Statistics

  •       Introduction to Linear Models

  •       Probability (graduate course)

  •       Statistical Inference (graduate course)

  •       Computing for Bioinformatics (graduate course)


Lecturing Assignment for Semester I, 2009-2010:

STAT414     Mathematical Statistics

Course Description

 Lecture Notes

  Assignments:

Research Interests: 

  • Markov Chain Monte Carlo

  • Bioinformatics

  • Neural Network

  • Stochastic Optimization

  • Computational Physics


Research Group


Recent Talks



Editorial Service

  •   Associate editor, Journal of Computational and Graphical Statistics, 2006-Present.

  •   Editorial Board Member, International Journal of Operations Research and Information Systems (IJORIS)}, 2008---present.
  •   Editorial Board Member, Journal of Advanced Research in Statistics and Probability, 2009--present

  •   Associate editor, Biometrics, 2006-2008.

Honors

  •   Elected member, International Statistical Institute (ISI), 2005.

  •   Statistica Sinica, invited paper, 2001 Joint Statistical Meeting.

Selected Publications:

  •      Books

  1.  Kendall, W.S., Liang, F., and Wang, J.S. (2005) (Editors) Markov Chain Monte Carlo: Innovations and Applications. World Scientific: Singapore.  ISBN 981-256-427-6.


                                                               

  •     Software  

     
  1.       Liang, F., Wu, M., and Tian, Y. (2008)  Bayesian LatentChIP.
  2.       Wu, M. and Liang, F. (2009)  Testing multiple hypotheses using population information of samples. Code ,  Supplementary Material.

  •      Articles

  1. Wong, W.H. and Liang, F. (1997) Dynamic weighting in Monte Carlo and optimization, Proc. Natl. Acad. Sci. USA, 94, 14220-14224.

  2. Liang, F. and Wong, W.H. (1999) Dynamic weighting in simulations of spin systems, Phys. Lett. A, 252, 257-262.

  3. Cong, J., Kong, T., Xu, D., Liang, F., Liu, J.S., and Wong, W.H. (1999) Relaxed simulated tempering for VLSI floorplan designs, Proc. Asia and South Pacific Design Automation Conf., Hong Kong, pp13-16.

  4. Cong, J., Kong, T., Xu, D., Liang, F., Liu, J.S., and Wong, W.H. (2000) Dynamic weighting Monte Carlo for constrained floorplan design in mixed signal application,Proc. Asia and South Pacific Design Automation Conf., Japan.

  5. Sanderson P, Taylor D., Ali M., Liew S.C., Couturier S., Lee G., Truong Y., Liang F., Gin K. and Holden H. (1999) Development of a methodology for monitoring variations in turbid waters draining modified wetlands in southeast Sumatra, Indonesia: preliminary results for suspended sediments, Eighth International Symposium on the Interactions Between Sediments and Water}, Beijing, pp. 13-17.

  6. Liu, J.S., Liang, F., and Wong. W.H. (2000) The use of multiple-try method and local optimization in Metropolis sampling, J. Amer. Statist. Assoc.,95, 121-134.

  7. Liang, F. and Wong. W.H. (2000) Evolutionary Monte Carlo sampling: applications to $C_p$ model sampling and change-point problem. Statistica Sinica,10, 317-342.

  8. Truong, Y. K., Liang, F., Sanderson, P. G., Taylor, D. and Liew, S. C. (2000) M onitoring variations in turbid waters draining modified wetlands in southeast Su matra, Indonesia: A functional data analytic approach. In Nonparametric approach to Knowledge Discovery, Nara, Japan, December 14-17, 2000. Proceedings.

  9. Liu, J.S., Liang, F., and Wong. W.H. (2001) A theory for dynamic weighting in Monte Carlo, J. Amer. Statist. Assoc., 96, 561-573.

  10. Liang, F. and Wong. W.H. (2001) Real-parameter evolutionary sampling with applications in Bayesian Mixture Models, J. Amer. Statist. Assoc., 96, 653-666. 

  11. Liang, F., Truong, Y.K. and Wong, W.H. (2001) Automatic Bayesian model averaging for linear regression and applications in Bayesian curve fitting. Statistica Sinica , 11, 1005-1029. 

  12. Liang, F. and Wong, W.H. (2001) Evolutionary Monte Carlo for Protein Folding simulations, Journal of Chemical Physics , 115, 3374-3380.

  13. Liang, F. (2002) Some connections between Bayesian and non-Bayesian methods for regression model selection.   Statistics & Probability Letters , 57, 53-63.

  14. Liang, F. (2002) Dynamically Weighted Importance Sampling in Monte Carlo Computation, J. Amer. Statist. Assoc. , 97, 807-821.

  15. Liang, F. (2003) An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine,  Neural Computation, 15, 1959-1989.

  16.  Liang, F. (2003) Use of sequential structure in simulation from high dimensional systems, Physical Review E,  67, 56101-56107.

  17. Zhang, J., Liang, F., Dassen, W. and de Gunst, M. (2003)  Search for Haplotype-Interactions that are susceptible to type I diabetes using unphased genotype data,  American J. Human Genetics, 73,   1385-1401.  

  18. Liang, F. (2004). Generalized 1/k-Ensemble Algorithm, Physical Review E, 69, 66701-66707.

  19.  Liang, F. (2004) Annealing Contour Monte Carlo for Structure Optimization in an Off-lattice Protein Model.  Journal of Chemical Physics, 120, 6756-6763. (This paper is selected  by editors expert for re-publication in the April 1, 2004 issue of  Virtual Journal of Biological Physcis Research.)

  20.  Liang, F. (2004) Annealing contour Monte Carlo for neural network training. Proceedings on Cybernetics and Informatics Technologies, Systems and Applications, Volume III, pp.130-135.

  21. Liang, F. and Kuk, Y.C.A. (2004)  A finite population estimation study with Bayesian neural networks.  Survey Methodology,  30, 219-234.

  22. Liang, F. (2005) Bayesian neural networks for non-linear time series forecasting.  Statistics and Computing, 15, 13-29.

  23.  Liang, F. and Liu, C. (2005)  Efficient MCMC estimation of discrete distributions.  Computational Statistics and Data Analysis,  49, 1039-1052.

  24. Liang, F. (2005) Evidence Evaluation for Bayesian Neural Networks. Neural Computation,  17, 1385-1410.

  25. Liang, F. (2005) Generalized Wang-Landau algorithm for Monte Carlo Computation. J. Amer. Statist. Assoc., 100, 1311-1327.

  26. Liang, F. (2005) Determination of normalizing constants for simulated tempering. Physica A, 356, 468-480.

  27. Liang, F. (2005). Annotated bibliography: Advanced Markov chain Monte Carlo methods.  ISBA Bulletin,  12(4),  2-5.

  28. Liang, F. and Huang, J. (2006) Book  Review: Statistical and Computational Inverse Problems.  Technometrics, 48, 146.

  29. Liang, F. (2006) A theory on flat histogram Monte Carlo algorothms. Journal of Statistical Physics, 122,  511-529.

  30. Zhu, H., Liang, F., Gu, M. and Peterson, B. (2006) Stochastic Approximation algorithms for estimation of spatial mixed models. In  Handbook of Computing and Statistics with Applications, Vol.   1  (eds. S.Y. Lee), Elsevier. pp.399-421.

  31. Liang, F., Liu, C. and Wang, N. (2007) A sequentail  Bayesian procedure for identification of differentially expressed genes.  Statistica Sinica, 12, 571-597.

  32. Liang, F., Liu, C. and Carroll, R.J. (2007) Stochastic Approximation in Monte Carlo Computation.   J. Amer. Statist. Assoc., 102, 305-320.

  33. Liang, F. and Wang, N. (2007) Dynamic Hierarchical Clustering of Gene Expression Profiles.  Pattern Recognition Letters,  28,  1062-1076.

  34. Liang, F. (2007) Use of SVD-based probit transformation in clustering gene expression profiles.  Computational Statistics and Data Analysis,  51, 6355-6366.

  35. Liang, F. (2007) Continuous Contour Monte Carlo for Marginal Density Estimation with an Application
    to a Spatial Statistical Model    , Journal of Computational and Graphical Statistics, 16(3), 608-632.

  36. Liang, F.  (2007)  Annealing Stochastic Approximation Monte Carlo for Neural Network Training.  Machine Learning, 68(3), 201-233.

  37.  Cheon, S. and Liang, F. (2008) Phylogenetic Tree Reconstruction Using Sequential Stochastic Approximation   Monte Carlo.  BioSystems, 91, 94-107.

  38. Liang, F. (2008) Stochastic Approximation Monte Carlo for MLP Learning.  In Encyclopedia of Artificial Intelligence, (eds. J.R.R. Dopico,  J.D. de la Calle, and A.P. Sierra), pp.1482-1489.

  39. Zhang, J., J Rggieli, M. Schipper, M. Entius, F. Liang, J. Koerselman, H Ruven, Y van der Graaf, D. Grobbee, and P. Doevendans (2008)  Inflammatory gene haplotype-interaction networks involved in coronary collateral formation. Human Heredity, 66, 252-264.

  40. Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation.  Biometrika,  95(4), 961-977.

  41.  Liang, F. (2008) Clustering gene expression profiles using mixture model ensemble averaging approach.  JP Journal of Biostatistics, 2(1), 57-80.

  42. Zhang, J. and Liang, F. (2008) Convergence of stochastic approximation under irregular conditions. StatisticaNeerlandica, 62, 393-403.

  43. Yuan, R., Ding, Y., and Liang, F. (2008)  Adaptive Evolutionary Monte Carlo for Optimizations with Applications to Sensor Placement Problems.  Statistics and Computing, 18, 375-390.

  44. Liang, F. (2009) Improving SAMC Using Smoothing Methods: Theory and Applications to Bayesian Model Selection Problems. The Annals of Statistics,  37, 2626-2654.

  45. Liang, F. (2009) On the use of SAMC for Monte Carlo integration. Statistics & Probability Letters, 79, 581-587.

  46.  Liang, F. and Zhang, J. (2009) Learning Bayesian Networks for Discrete Data. Computational Statistics & Data Analysis, 53, 865-876.

  47. Zhang, X.S., Liang, F., Srinivasan, R., and Van Liew, M. (2009) Estimating uncertainty of streamflow simulation using Bayesian neural networks.  Water Resources Research, 45, W02403.

  48. Zhang, P., Hill, C., Xia, Y., and Liang, F. (2009)   Modeling the relationship between EDI implementation
    and firm performance improvement with neural networks.  IEEE Transactions on Automation Science and Engineering, in press.

  49. Liang, F. (2009) A double Metropolis-Hastings sampler for spatial models with intractable normalizing constants.
    Journal of Statistical Computing and Simulation, in press.

  50.  Cheon, S. and Liang, F. (2009) Bayesian phylogeny analysis via stochastic approximation Monte Carlo.  Molecular Phylogenetic & Evolution, 53, 394-403.

  51. Wu, M., Liang, F. and Tian, Y. (2009) Bayesian Modeling of ChIP-chip Data Using Latent Variables. BMCBioinformatics, in press.

  52. Mo, Q. and Liang, F. (2009)  Bayesian modeling of ChIP-chip data through a high-order Ising model.  Biometrics, in press.