Bani K. Mallick

Susan M. Arseven `75 Chair in Data Science and Computational Statistics

University Distinguished Professor

Director, Center for statistical Bioinformatics

Director, Texas A&M TRIPODS Data Science Institute

Texas A&M University

Office: 401B Blocker Building.
Hours: TBA

Phone: (979) 845-1275.(office)
FAX: (979) 845-3144.
E-mail:
bmallick@stat.tamu.edu
Address: Statistics Department , Texas A&M University , College Station, TX 77843-3143 USA.

Education

Awards and Honors


Editorial Service
Past Editor: Sankhya, B
Past Associate Editor: Journal of the American Statistical association, A&C
Current Associate Editor: SIAM Journal on Uncertainty Quantification
Past Associate Editor: Journal of Computation and Graphical Statistics
Past Associate Editor: Biostatistics

Grants
Funded by multiple NSF, NIH, DOE grants

Course I'm Teaching

STAT652 STAT633

Research Interests

Selected Publications (from 2013)

Software

  • The R code for implemeting Probabilistic correlation calculation for count data in the paper Zoh, R., Mallick, B., Ivanov, I., Baladandayuthapani, V., Chapkin, R. and Carroll, R. (2016) ``PCAN: Probabilistic Correlation analysis of two Non-Normal Data sets'' Biometrics 72, 1358-1368. PCANCode
  • The MATLAB code for implemnting the algorithm proposed in Bhattacharya, A., Chakraborty, A. and Mallick, B. (2015). Fast samppling with Gaussian scale mixture priors in high-dimensional regression. Biometrika 4, 985-991. [arxiv] [Matlab implementation]
  • BayesME: an R package for nonparametric density deconvolution and nonparametric regression (not yet implemented) allowing for heteroscadastic measurement error and heteroscedastic regression error .Based on Sarkar, A., Mallick, B. K., Staudenmayer, J., Pati, D. and Carroll, R. J. (2014). Bayesian semiparametric density deconvolution in the presence of conditionally heteroscedastic measurement errors. Journal of Computational and Graphical Statistics, 25, 1101-1125 and Sarkar, A., Mallick, B. K. and Carroll, R. J. (2014). Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors. Biometrics, 70, 823-834. Packages mvtnorm and msm are required.
    Syllabus:             here