HR job title
- Distinguished Professor
- Contact Info
- 1 979 845 3105
Mallick, Bani Distinguished Professor
Positions
- Distinguished Professor, Statistics, College of Arts and Sciences
Bayesian hierarchical Modeling, Nonparametric Regression and classification, Bioinformatics, Spatio-temporal Modeling, Machine learning, Functional Data analysis, Bayesian nonparametrics, Petroleum reservoir characterization, Uncertainty analysis of Computer Model outputs
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Overview
Publications
selected publications
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academic article
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Lei, B., Mahajan, A., & Mallick, B.
(2024).
Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques. Scientific Reports.
14(1), 8595.
-
Lee, K., Zhao, P., Bhattacharya, A., Mallick, B. K., & Xie, L. e.
(2024).
An Active Learning-Based Approach for Hosting Capacity Analysis in Distribution Systems. IEEE Transactions on Smart Grid.
15(1), 617-626.
-
Ghosh, R. P., Maity, A. K., Pourahmadi, M., & Mallick, B. K.
(2023).
Adaptive Bayesian variable clustering via structural learning of breast cancer data. Genetic Epidemiology.
47(1), 95-104.
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Prakash, A., Lee, S. Y., Liu, X., Liu, L., Mallick, B., & Ding, Y. u.
(2023).
A Bayesian hierarchical model to understand the effect of terrain on wind turbine power curves. PP(99), 1-10.
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Mohseni, P., Duffield, N., Mallick, B., & Hasanzadeh, A.
(2023).
Adaptive Conditional Quantile Neural processes. 1445-1455.
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Niu, Y., Ni, Y., Pati, D., & Mallick, B. K.
(2023).
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
ahead-of-print(ahead-of-print), 1-15.
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Lee, S. Y., & Mallick, B. K.
(2022).
Bayesian Hierarchical Modeling: Application Towards Production Results in the Eagle Ford Shale of South Texas. Sankhya B - Applied and Interdisciplinary Statistics.
84(1), 1-43.
-
Ghosh, R. P., Mallick, B., & Pourahmadi, M.
(2021).
Bayesian Estimation of Correlation Matrices of Longitudinal Data. Bayesian Analysis.
16(3), 1039-1058.
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Niu, Y., Pati, D., & Mallick, B. K.
(2021).
Bayesian graph selection consistency under model misspecification. Bernoulli: a journal of mathematical statistics and probability.
27(1), 637-672.
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Sarkar, A., Pati, D., Mallick, B. K., & Carroll, R. J.
(2021).
Bayesian Copula Density Deconvolution for Zero-Inflated Data in Nutritional Epidemiology. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
116(535), 1075-1087.
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Lei, B., Kirk, T. Q., Bhattacharya, A., Pati, D., Qian, X., Arroyave, R., & Mallick, B. K.
(2021).
Bayesian optimization with adaptive surrogate models for automated experimental design. npj Computational Materials.
7(1), 194.
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Maity, A. K., Lee, S. C., Hu, L., Bell-Pedersen, D., Mallick, B. K., & Sarkar, T. R.
(2021).
Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data. Chemometrics and Intelligent Laboratory Systems.
212, 104276-104276.
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Luo, Z. T., Sang, H., & Mallick, B.
(2021).
A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables. JOURNAL OF MACHINE LEARNING RESEARCH.
22(37), 1-52.
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Ganugula, R., Arora, M., Lepiz, M. A., Niu, Y., Mallick, B. K., Pflugfelder, S. C., Scott, E. M., & Kumar, M.
(2020).
Systemic anti-inflammatory therapy aided by double-headed nanoparticles in a canine model of acute intraocular inflammation. Science Advances.
6(35), eabb7878.
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Maity, A. K., Lee, S. C., Mallick, B. K., & Sarkar, T. R.
(2020).
Bayesian structural equation modeling in multiple omics data with application to circadian genes. Bioinformatics.
36(13), 3951-3958.
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Payne, R. D., Guha, N., Ding, Y., & Mallick, B. K.
(2020).
A Conditional Density Estimation Partition Model Using Logistic Gaussian Processes. Biometrika.
107(1), 173-190.
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Maity, A. K., Bhattacharya, A., Mallick, B. K., & Baladandayuthapani, V.
(2020).
Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data. Biometrics.
76(1), 316-325.
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Chakraborty, A., Bhattacharya, A., & Mallick, B. K.
(2020).
Bayesian sparse multiple regression for simultaneous rank reduction and variable selection. Biometrika.
107(1), 205-221.
-
Guha, N., Baladandayuthapani, V., & Mallick, B. K.
(2020).
Quantile Graphical Models: Bayesian Approaches. JOURNAL OF MACHINE LEARNING RESEARCH.
21(79), 1-47.
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Afrin, K., Iquebal, A. S., Karimi, M., Souris, A., Lee, S. Y., & Mallick, B. K.
(2020).
Directionally dependent multi-view clustering using copula model. 15(10), e0238996-e0238996.
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Lee, S. Y., Lei, B., & Mallick, B.
(2020).
Estimation of COVID-19 spread curves integrating global data and borrowing information. 15(7), e0236860-e0236860.
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Das, N., Ghosh, R. P., Guha, N., Bhattacharya, R., & Mallick, B.
(2019).
Optimal Transport Based Tracking of Space Objects in Cylindrical Manifolds. Journal of the Astronautical Sciences.
66(4), 582-606.
-
Maity, A. K., Carroll, R. J., & Mallick, B. K.
(2019).
Integration of Survival and Binary Data for Variable Selection and Prediction: A Bayesian Approach. 68(5), 1577-1595.
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Zhaoa, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., ... Brown, M.
(2019).
Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm. Remote Sensing of Environment: an interdisciplinary journal.
232, 111181-111181.
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Kundu, S., Mallick, B. K., & Baladandayuthapan, V.
(2019).
Efficient Bayesian Regularization for Graphical Model Selection. Bayesian Analysis.
14(2), 449-476.
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Song, J. J., & Mallick, B.
(2019).
Hierarchical Bayesian models for predicting spatially correlated curves. 53(1), 196-209.
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Roy Sarkar, T., Maity, A. K., Niu, Y., & Mallick, B. K.
(2019).
Multiple Omics Data Integration to Identify Long Noncoding RNA Responsible for Breast Cancer-Related Mortality. Cancer Informatics.
18, 1176935119871933-117693511987193.
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Zoh, R. S., Sarkar, A., Carroll, R. J., & Mallick, B. K.
(2018).
A Powerful Bayesian Test for Equality of Means in High Dimensions. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
113(524), 1733-1741.
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Sarkar, A., Pati, D., Chakraborty, A., Mallick, B. K., & Carroll, R. J.
(2018).
Bayesian Semiparametric Multivariate Density Deconvolution. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
113(521), 401-416.
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Kundu, S., Cheng, Y., Shin, M., Manyam, G., Mallick, B. K., & Baladandayuthapani, V.
(2018).
Bayesian variable selection with graphical structure learning: Applications in integrative genomics. PLoS ONE.
13(7), e0195070-e0195070.
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Payne, R. D., & Mallick, B. K.
(2018).
Two-Stage Metropolis-Hastings for Tall Data. Journal of Classification.
35(1), 29-51.
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Yang, K., Guha, N., Efendiev, Y., & Mallick, B. K.
(2017).
Bayesian and variational Bayesian approaches for flows in heterogeneous random media. Journal of Computational Physics.
345, 275-293.
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Chakraborty, A., Bingham, D., Dhavala, S. S., Kuranz, C. C., Drake, R. P., Grosskopf, M. J., ... Mallick, B. K.
(2017).
Emulation of Numerical Models With Over-Specified Basis Functions. Technometrics.
59(2), 153-164.
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Efendiev, Y., Leung, W. T., Cheung, S. W., Guha, N., Hoang, V. H., & Mallick, B.
(2017).
BAYESIAN MULTISCALE FINITE ELEMENT METHODS. MODELING MISSING SUBGRID INFORMATION PROBABILISTICALLY. International Journal for Multiscale Computational Engineering.
15(2), 175-197.
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Mallick, B. K., Yang, K., Guha, N., & Efendiev, Y.
(2016).
Bayesian Solution Uncertainty Quantification for Differential Equations. Bayesian Analysis.
11(4), 1279-1284.
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Bhattacharya, A., Chakraborty, A., & Mallick, B. K.
(2016).
Fast sampling with Gaussian scale-mixture priors in high-dimensional regression. Biometrika.
103(4), 985-991.
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Zoh, R. S., Mallick, B., Ivanov, I., Baladandayuthapani, V., Manyam, G., Chapkin, R. S., Lampe, J. W., & Carroll, R. J.
(2016).
PCAN: Probabilistic correlation analysis of two non-normal data sets. Biometrics.
72(4), 1358-1368.
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Heydari, S., Fu, L., Lord, D., & Mallick, B. K.
(2016).
Multilevel Dirichlet process mixture analysis of railway grade crossing crash data. Analytic methods in accident research.
9, 27-43.
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Zhang, L., Sarkar, A., & Mallick, B. K.
(2016).
Bayesian sparse covariance decomposition with a graphical structure. 26(1-2), 493-510.
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Pourhabib, A., Mallick, B. K., & Ding, Y. u.
(2015).
Absent Data Generating Classifier for Imbalanced Class Sizes. JOURNAL OF MACHINE LEARNING RESEARCH.
16, 2695-2724.
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Guha, N., Wu, X., Efendiev, Y., Jin, B., & Mallick, B. K.
(2015).
A variational Bayesian approach for inverse problems with skew-t error distributions. Journal of Computational Physics.
301, 377-393.
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Chakraborty, A., De, S., Bowman, K. P., Sang, H., Genton, M. G., & Mallick, B. K.
(2015).
An adaptive spatial model for precipitation data from multiple satellites over large regions. Statistics and Computing.
25(2), 389-405.
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Dreano, D., Mallick, B., & Hoteit, I.
(2015).
Filtering remotely sensed chlorophyll concentrations in the Red Sea using a spacetime covariance model and a Kalman filter. Spatial Statistics.
13, 1-20.
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Sarkar, A., Mallick, B. K., & Carroll, R. J.
(2014).
Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors. Biometrics.
70(4), 823-834.
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Baladandayuthapani, V., Talluri, R., Ji, Y., Coombes, K. R., Lu, Y., Hennessy, B. T., Davies, M. A., & Mallick, B. K.
(2014).
BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA. Annals of Applied Statistics.
8(3), 1443-1468.
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Konomi, B. A., Sang, H., & Mallick, B. K.
(2014).
Adaptive Bayesian Nonstationary Modeling for Large Spatial Datasets Using Covariance Approximations. Journal of Computational And Graphical Statistics (JCGS).
23(3), 802-829.
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Mondal, A., Mallick, B., Efendiev, Y., & Datta-Gupta, A.
(2014).
Bayesian Uncertainty Quantification for Subsurface Inversion Using a Multiscale Hierarchical Model. Technometrics.
56(3), 381-392.
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Sarkar, A., Mallick, B. K., Staudenmayer, J., Pati, D., & Carroll, R. J.
(2014).
Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors. Journal of Computational And Graphical Statistics (JCGS).
23(4), 1101-1125.
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Zhang, L., Baladandayuthapani, V., Mallick, B. K., Manyam, G. C., Thompson, P. A., Bondy, M. L., & Do, K.
(2014).
Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer. Journal of the Royal Statistical Society Series C: Applied Statistics.
63(4), 595-620.
-
Kim, H., Ryu, D., Mallick, B. K., & Genton, M. G.
(2014).
Mixtures of skewed Kalman filters. Journal of Multivariate Analysis.
123, 228-251.
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Talluri, R., Baladandayuthapani, V., & Mallick, B. K.
(2014).
Bayesian sparse graphical models and their mixtures. Stat.
3(1), 109-125.
-
Zhang, L., & Mallick, B. K.
(2013).
Inferring gene networks from discrete expression data. Biostatistics.
14(4), 708-722.
-
Xun, X., Cao, J., Mallick, B., Carroll, R. J., & Maity, A.
(2013).
Parameter Estimation of Partial Differential Equation Models. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
108(503), 1009-1020.
-
Konomi, B. A., Dhavala, S. S., Huang, J. Z., Kundu, S., Huitink, D., Liang, H., Ding, Y. u., & Mallick, B. K.
(2013).
BAYESIAN OBJECT CLASSIFICATION OF GOLD NANOPARTICLES. Annals of Applied Statistics.
7(2), 640-668.
-
Bhadra, A., & Mallick, B. K.
(2013).
Joint high-dimensional Bayesian variable and covariance selection with an application to eQTL analysis. Biometrics.
69(2), 447-457.
-
Zhao, K., Valle, D., Popescu, S., Zhang, X., & Mallick, B.
(2013).
Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment: an interdisciplinary journal.
132, 102-119.
-
Ryu, D., Liang, F., & Mallick, B. K.
(2013).
Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
108(501), 111-123.
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Chakraborty, A., Mallick, B. K., McClarren, R. G., Kuranz, C. C., Bingham, D., Grosskopf, M. J., ... Drake, R. P.
(2013).
Spline-Based Emulators for Radiative Shock Experiments With Measurement Error. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
108(502), 411-428.
-
Park, C., Huang, J. Z., Huitink, D., Kundu, S., Mallick, B. K., Liang, H., & Ding, Y. u.
(2012).
A multistage, semi-automated procedure for analyzing the morphology of nanoparticles. IIE Transactions.
44(7), 507-522.
-
Chakraborty, S., Ghosh, M., & Mallick, B. K.
(2012).
Bayesian nonlinear regression for large p small n problems. 108, 28-40.
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Hartman, B. M., Mallick, B. K., & Talukdar, D.
(2012).
INVESTIGATING INTERNATIONAL NEW PRODUCT DIFFUSION SPEED: A SEMIPARAMETRIC APPROACH. Annals of Applied Statistics.
6(2), 625-651.
-
Lobach, I., Mallick, B., & Carroll, R. J.
(2011).
Semiparametric Bayesian analysis of gene-environment interactions with error in measurement of environmental covariates and missing genetic data. 4(3), 305-316.
-
Xun, X., Mallick, B., Carroll, R. J., & Kuchment, P.
(2011).
A Bayesian approach to the detection of small low emission sources. Inverse Problems.
27(11), 115009-115009.
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Drake, R. P., Doss, F. W., McClarren, R. G., Adams, M. L., Amato, N., Bingham, D., ... Visco, A. J.
(2011).
Radiative effects in radiative shocks in shock tubes. High Energy Density Physics.
7(3), 130-140.
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Stripling, H. F., Adams, M. L., McClarren, R. G., & Mallick, B. K.
(2011).
The Method of Manufactured Universes for validating uncertainty quantification methods. Reliability Engineering & System Safety.
96(9), 1242-1256.
-
Ryu, D., Li, E., & Mallick, B. K.
(2011).
Bayesian nonparametric regression analysis of data with random effects covariates from longitudinal measurements. 67(2), 454-466.
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Parzen, M., Ghosh, S., Lipsitz, S., Sinha, D., Fitzmaurice, G. M., Mallick, B. K., & Ibrahim, J. G.
(2011).
A generalized linear mixed model for longitudinal binary data with a marginal logit link function. Annals of Applied Statistics.
5(1), 449-467.
-
Mallick, B. K., Chakraborty, S., & Ghosh, M.
(2011).
Data Augmentation for Support Vector Machines. Bayesian Analysis.
6(1), 25-29.
-
Xia, H. H., Ding, Y. u., & Mallick, B. K.
(2011).
Bayesian hierarchical model for combining misaligned two-resolution metrology data. IIE Transactions.
43(4), 242-258.
-
Ghosh, S., & Mallick, B. K.
(2011).
A hierarchical Bayesian spatio-temporal model for extreme precipitation events. Environmetrics.
22(2), 192-204.
-
McClarren, R. G., Ryu, D., Drake, R. P., Grosskopf, M., Bingham, D., Chou, C., ... Torralva, B. R.
(2011).
A physics informed emulator for laser-driven radiating shock simulations. Reliability Engineering & System Safety.
96(9), 1194-1207.
-
Holloway, J. P., Bingham, D., Chou, C., Doss, F., Drake, R. P., Fryxell, B., ... Zhang, Z.
(2011).
Predictive modeling of a radiative shock system. Reliability Engineering & System Safety.
96(9), 1184-1193.
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Dhavala, S. S., Datta, S., Mallick, B. K., Carroll, R. J., Khare, S., Lawhon, S. D., & Adams, L. G.
(2010).
Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
105(491), 956-967.
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Sturino, J., Zorych, I., Mallick, B., Pokusaeva, K., Chang, Y., Carroll, R. J., & Bliznuyk, N.
(2010).
Statistical methods for comparative phenomics using high-throughput phenotype microarrays. International Journal of Biostatistics.
6(1), Article-29.
-
Sinha, S., Mallick, B. K., Kipnis, V., & Carroll, R. J.
(2010).
Semiparametric bayesian analysis of nutritional epidemiology data in the presence of measurement error. Biometrics.
66(2), 444-454.
-
Huitink, D., Kundu, S., Park, C., Mallick, B., Huang, J. Z., & Liang, H.
(2010).
Nanoparticle shape evolution identified through multivariate statistics. Journal of Physical Chemistry A.
114(17), 5596-5600.
-
Mondal, A., Efendiev, Y., Mallick, B., & Datta-Gupta, A.
(2010).
Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods. Advances in Water Resources.
33(3), 241-256.
-
Efendiev, Y., Datta-Gupta, A., Ma, X., & Mallick, B.
(2009).
Efficient sampling techniques for uncertainty quantification in history matching using nonlinear error models and ensemble level upscaling techniques. Water Resources Research.
45(11),
-
Kim, H., & Mallick, B. K.
(2009).
Moments of random vectors with skew t distribution and their quadratic forms (vol 63, pg 417, 2003). Statistics and Probability Letters.
79(19), 2098-2099.
-
Fu, W. J., Mallick, B., & Carroll, R. J.
(2009).
Why do we observe misclassification errors smaller than the Bayes error?. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION.
79(5), 717-722.
-
Baladandayuthapani, V., Mallick, B. K., & Carroll, R. J.
(2008).
Correction. Journal of Computational And Graphical Statistics (JCGS).
17(2), 515-515.
-
Baladandayuthapani, V., Mallick, B. K., Young Hong, M., Lupton, J. R., Turner, N. D., & Carroll, R. J.
(2008).
Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis. Biometrics.
64(1), 64-73.
-
Efendiev, Y., Datta-Gupta, A., Ma, X., & Mallick, B.
(2008).
Modified Markov chain Monte Carlo method for dynamic data integration using streamline approach. Mathematical Geosciences.
40(2), 213-232.
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Bae, K., Mallick, B. K., & Elsik, C. G.
(2008).
Prediction of Protein Interdomain Linker Regions by a Nonstationary Hidden Markov Model. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
103(483), 1085-1099.
-
Gold, D., Mallick, B., & Coombes, K.
(2008).
Real-time gene expression: statistical challenges in design and inference. Journal of Computational Biology.
15(6), 611-623.
-
Chakraborty, S., Mallick, B. K., Ghosh, D., Ghosh, M., & Doughertry, E.
(2007).
Gene expression-based glioma classification using hierarchical Bayesian Vector machines. Sankhya: The Indian Journal of Statistics.
69(3), 514-547.
-
Ryu, D., Sinha, D., Mallick, B., Lipsitz, S. L., & Lipshultz, S.
(2007).
Longitudinal Studies With Outcome-Dependent Follow-up: Models and Bayesian Regression. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
102(479), 952-967.
-
Gold, D. L., Coombes, K. R., Wang, J., & Mallick, B.
(2007).
Enrichment analysis in high-throughput genomics - accounting for dependency in the NULL. Briefings in Bioinformatics.
8(2), 71-77.
-
Wang, X., Ray, S., & Mallick, B. K.
(2007).
Bayesian curve classification using wavelets. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
102(479), 962-973.
-
Ray, S., & Mallick, B.
(2006).
Functional clustering by Bayesian wavelet methods. 68(2), 305-332.
-
Song, J. J., Ghosh, A., Miaou, S., & Mallick, B.
(2006).
Bayesian multivariate spatial models for roadway traffic crash mapping. Journal of Multivariate Analysis.
97(1), 246-273.
-
Efendiev, Y., Datta-Gupta, A., Ginting, V., Ma, X., & Mallick, B.
(2005).
An efficient two-stage Markov chain Monte Carlo method for dynamic data integration. Water Resources Research.
41(12), 1-6.
-
Holmes, C. C., Denison, D., Ray, S., & Mallick, B. K.
(2005).
Bayesian prediction via partitioning. 14(4), 811-830.
-
Kim, H. M., Mallick, B. K., & Holmes, C. C.
(2005).
Analyzing nonstationary spatial data using piecewise Gaussian processes. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
100(470), 653-668.
-
Sinha, S., Mukherjee, B., Ghosh, M., Mallick, B. K., & Carroll, R. J.
(2005).
Semiparametric Bayesian analysis of matched case-control studies with missing exposure. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
100(470), 591-601.
-
Baladandayuthapani, V., Mallick, B. K., & Carroll, R. J.
(2005).
Spatially adaptive Bayesian penalized regression splines (P-splines). Journal of Computational And Graphical Statistics (JCGS).
14(2), 378-394.
-
Bae, K., Mallick, B. K., & Elsik, C. G.
(2005).
Prediction of protein interdomain linker regions by a hidden Markov model. 21(10), 2264-2270.
-
Mallick, B. K., Ghosh, D., & Ghosh, M.
(2005).
Bayesian classification of tumours by using gene expression data. Journal of the Royal Statistical Society Series B: Statistical Methodology.
67(2), 219-234.
-
Efendiev, Y., Datta-Gupta, A., Osaka, I., & Mallick, B.
(2005).
Multiscale data integration using coarse-scale models. Advances in Water Resources.
28(3), 303-314.
-
Zhang, F., Mallick, B., & Weng, Z.
(2005).
A Bayesian method for identifying independent sources of non-random spatial patterns. Statistics and Computing.
15(4), 329-339.
-
Kim, H., Mallick, B. K., & Holmes, C. C.
(2005).
Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
100(470), 653-668.
-
Fu, W. J., Dougherty, E. R., Mallick, B., & Carroll, R. J.
(2005).
How many samples are needed to build a classifier: a general sequential approach. Bioinformatics.
21(1), 63-70.
-
Bae, K., & Mallick, B. K.
(2004).
Gene selection using a two-level hierarchical Bayesian model. Bioinformatics.
20(18), 3423-3430.
-
Kim, H. M., & Mallick, B. K.
(2004).
A Bayesian prediction using the skew Gaussian distribution. Journal of Statistical Planning and Inference.
120(1-2), 85-101.
-
Ray, S., & Mallick, B. K.
(2003).
A Bayesian transformation model for wavelet shrinkage. IEEE Transactions on Image Processing.
12(12), 1512-1521.
-
Kim, H. M., & Mallick, B. K.
(2003).
A note on Bayesian spatial prediction using the elliptical distribution. Statistics and Probability Letters.
64(3), 271-276.
-
Kim, H. M., & Mallick, B. K.
(2003).
Moments of random vectors with skew t distribution and their quadratic forms. Statistics and Probability Letters.
63(4), 417-423.
-
Charlet, L., Chakraborty, S., Appello, T., Latscha, A. A., Chatterjee, D., & Mallick, B.
(2003).
Propagation of a natural arsenic plume in West Bengal, India. 107, 285-288.
-
Mallick, B. K., & Walker, S.
(2003).
A Bayesian semiparametric transformation model incorporating frailties. Journal of Statistical Planning and Inference.
112(1-2), 159-174.
-
Lee, K. E., Sha, N., Dougherty, E. R., Vannucci, M., & Mallick, B. K.
(2003).
Gene selection: a Bayesian variable selection approach. Bioinformatics.
19(1), 90-97.
-
Holmes, C. C., & Mallick, B. K.
(2003).
Generalized nonlinear modeling with multivariate free-knot regression splines. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
98(462), 352-368.
-
Mallick, B., Hoffman, F. O., & Carrol, R. J.
(2002).
Semiparametric regression modeling with mixtures of Berkson and classical error, with application to fallout from the Nevada test site. 58(1), 13-20.
-
Mallick, B. K., Denison, D., & Gangopadhyay, A. K.
(2002).
A Bayesian Curve Fitting Approach to Power Spectrum Estimation. Journal of Nonparametric Statistics.
14(1-2), 141-153.
-
Holmes, C. C., Denison, D., & Mallick, B. K.
(2002).
Accounting for model uncertainty in seemingly unrelated regressions. Journal of Computational And Graphical Statistics (JCGS).
11(3), 533-551.
-
Denison, D., Dellaportas, P., & Mallick, B. K.
(2001).
Wind speed prediction in a complex terrain. Environmetrics.
12(6), 499-515.
-
Holmes, C. C., & Mallick, B. K.
(2001).
Bayesian regression with multivariate linear splines. 63(1), 3-17.
-
Flynn, N. E., Knabe, D. A., Mallick, B. K., & Wu, G.
(2000).
Postnatal changes of plasma amino acids in suckling pigs. Journal of Animal Science.
78(9), 2369-2375.
-
Holmes, C. C., & Mallick, B. K.
(2000).
Bayesian wavelet networks for nonparametric regression. IEEE Transactions on Neural Networks and Learning Systems.
11(1), 27-35.
-
Mallick, B. K., Denison, D. G., & Smith, A. F.
(1999).
Bayesian survival analysis using a MARS model. 55(4), 1071-1077.
-
Walker, S., & Mallick, B. K.
(1999).
A Bayesian semiparametric accelerated failure time model. 55(2), 477-483.
-
Gangopadhyay, A. K., Mallick, B. K., & Denison, D.
(1999).
Estimation of spectral density of a stationary time series via an asymptotic representation of the periodogram. Journal of Statistical Planning and Inference.
75(2), 281-290.
-
Denison, D., Mallick, B. K., & Smith, A.
(1998).
Bayesian MARS. Statistics and Computing.
8(4), 337-346.
-
Denison, D., Mallick, B. K., & Smith, A.
(1998).
Automatic Bayesian curve fitting. 60(2), 333-350.
-
Dension, D.
(1998).
A Bayesian CART algorithm. Biometrika.
85(2), 363-377.
-
Holmes, C. C., & Mallick, B. K.
(1998).
Bayesian Radial Basis Functions of Variable Dimension. Neural Computation.
10(5), 1217-1233.
-
Mallick, B. K.
(1998).
Bayesian curve estimation by polynomial of random order. Journal of Statistical Planning and Inference.
70(1), 91-109.
-
Denison, D., Smith, A., & Mallick, B. K.
(1998).
Comment. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
93(443), 954-957.
-
Walker, S. G., & Mallick, B. K.
(1997).
A note on the scale parameter of the dirichlet process. Canadian Journal of Statistics, The.
25(4), 473-479.
-
Kuo, L., & Mallick, B.
(1997).
Bayesian semiparametric inference for the accelerated failuretime model. Canadian Journal of Statistics, The.
25(4), 457-472.
-
Walker, S. G., & Mallick, B. K.
(1997).
Hierarchical Generalized Linear Models and Frailty Models with Bayesian Nonparametric Mixing. 59(4), 845-860.
-
Mallick, B.
(1997).
Combining information from several experiments with nonparametric priors. Biometrika.
84(3), 697-706.
-
Gelfand, A. E., Mallick, B. K., & Polasek, W.
(1997).
Broken Biological Size Relationships: A Truncated Semiparametric Regression Approach with Measurement Error. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
92(439), 836-845.
-
Mallick, B. K., & Gelfand, A. E.
(1996).
Semiparametric errors-in-variables models A Bayesian approach. Journal of Statistical Planning and Inference.
52(3), 307-321.
-
Gelfand, A. E., & Mallick, B. K.
(1995).
Bayesian analysis of proportional hazards models built from monotone functions. Biometrics.
51(3), 843-852.
-
Gelfand, A. E., Mallick, B. K., & Dey, D. K.
(1995).
Modeling Expert Opinion Arising as a Partial Probabilistic Specification. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
90(430), 598-604.
-
MALLICK, B. K., & GELFAND, A. E.
(1994).
Generalized linear models with unknown link functions. Biometrika.
81(2), 237-245.
-
Gelfand, A. E., & Mallick, B. K.
(1994).
Bayesian Analysis of Semiparametric Proportional Hazards Models
-
Luo, Z. T., Sang, H., & Mallick, B.
A Nonstationary Soft Partitioned Gaussian Process Model via Random Spanning Trees. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
ahead-of-print(ahead-of-print), 1-12.
-
Denison, D., Smith, A., & Mallick, B. K.
Bayesian CART model search - Comment. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
93(443), 954-957.
-
Lei, B., Mahajan, A., & Mallick, B.
(2024).
Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques. Scientific Reports.
14(1), 8595.
-
book
-
Biegler, L., Biros, G., Ghattas, O., Heinkenschloss, M., Keyes, D., Mallick, B., ... Willcox, K.
(2010).
LargeScale Inverse Problems and Quantification of Uncertainty. Wiley.
-
Mallick, B. K., Gold, D. L., & Baladandayuthapani, V.
(2009).
Bayesian Analysis of Gene Expression Data. Wiley.
-
Baladandayuthapani, V., Ray, S., & Mallick, B. K.
(2005).
Bayesian Methods for DNA Microarray Data Analysis. Ed. 25.
Elsevier.
-
Biegler, L., Biros, G., Ghattas, O., Heinkenschloss, M., Keyes, D., Mallick, B., ... Willcox, K.
(2010).
LargeScale Inverse Problems and Quantification of Uncertainty. Wiley.
-
chapter
-
Sarkar, T. R., McNeal, C. J., Meininger, C. J., Niu, Y., Mallick, B. K., Carroll, R. J., & Wu, G.
(2021).
Dietary Intakes of Amino Acids and Other Nutrients by Adult Humans. Advances in Experimental Medicine and Biology.
Wu, G. (Eds.),
Amino Acids in Nutrition and Health. Advances in Experimental Medicine and Biology.
(pp. 211-227).
Springer International Publishing.
-
Baladandayuthapan, V., Wang, X., Mallick, B. K., & Do, K.
(2015).
Bayesian Functional Mixed Models for Survival Responses with Application to Prostate Cancer. Chen, Z., Liu, A., Qu, Y., Tang, L., Ting, N., & Tsong, Y. (Eds.),
Applied Statistics in Biomedicine and Clinical Trials Design.
(pp. 35-59).
Springer Nature.
-
Chakraborty, S., Mallick, B. K., & Ghosh, M.
(2013).
Bayesian hierarchical kernel machines for nonlinear regression and classification. Bayesian Theory and Applications.
(pp. 50-69).
Oxford University Press (OUP).
-
Richardson, S., Bottolo, L., & Rosenthal, J. S.
(2011).
Bayesian Models for Sparse Regression Analysis of High Dimensional Data*. Bayesian Statistics 9.
(pp. 539-568).
Oxford University Press (OUP).
-
Efendiev, Y., DattaGupta, A., Hwang, K., Ma, X., & Mallick, B.
(2010).
Bayesian Partition Models for Subsurface Characterization. LargeScale Inverse Problems and Quantification of Uncertainty.
(pp. 107-122).
Wiley.
-
Jeong, J., Vannucci, M., Do, K., Broom, B., Kim, S., Sha, N., ... Pusztai, L.
(2010).
Gene Selection for the Identification of Biomarkers in High- Throughput Data. Bayesian Modeling in Bioinformatics.
(pp. 259-280).
Taylor & Francis.
-
Maity, A., & Mallick, B. K.
(2010).
Proportional Hazards Regression Using Bayesian Kernel Machines. Bayesian Modeling in Bioinformatics.
(pp. 343-368).
Taylor & Francis.
-
Mallick, B., Ghosh, D., & Ghosh, M.
(2008).
Machine Learning in Structural Biology: Interpreting 3D Protein Images. Introduction to Machine Learning and Bioinformatics.
(pp. 255-294).
Chapman and Hall/CRC.
-
Mallick, B. K., Ghosh, D., & Ghosh, M.
(2008).
Bayesian Machine-Learning Methods for Tumor Classification Using Gene Expression Data. INTRODUCTION TO MACHINE LEARNING AND BIOINFORMATICS.
(pp. 303-325).
-
Baladandayuthapani, V., Holmes, C. C., Mallick, B. K., & Carroll, R. J.
(2006).
Modeling Nonlinear Gene Interactions Using Bayesian MARS. Do, K., Vannucci, M., & Müller, P. (Eds.),
BAYESIAN INFERENCE FOR GENE EXPRESSION AND PROTEOMICS.
(pp. 116-136).
Cambridge University Press (CUP).
-
Kim, H., Ha, E., & Mallick, B.
(2004).
Spatial Prediction of Rainfall Using Skew-Normal Processes. Skew-Elliptical Distributions and Their Applications.
(pp. 279-289).
Taylor & Francis.
-
Denison, D. D., Hansen, M. H., Holmes, C. C., Mallick, B., & Yu, B.
(2003).
Introduction. Nonlinear Estimation and Classification.
(pp. 1-5).
Springer Nature.
-
Kim, H. M., & Mallick, B. K.
(2002).
Analyzing spatial data using Skew-Gaussian processes. Spatial Cluster Modelling.
(pp. 163-173).
-
Sarkar, T. R., McNeal, C. J., Meininger, C. J., Niu, Y., Mallick, B. K., Carroll, R. J., & Wu, G.
(2021).
Dietary Intakes of Amino Acids and Other Nutrients by Adult Humans. Advances in Experimental Medicine and Biology.
Wu, G. (Eds.),
Amino Acids in Nutrition and Health. Advances in Experimental Medicine and Biology.
(pp. 211-227).
Springer International Publishing.
-
conference paper
-
Geng, X., Tong, L., Bhattacharya, A., Mallick, B., & Xie, L. e.
(2021).
Probabilistic Hosting Capacity Analysis via Bayesian Optimization. IEEE Power and Energy Society General Meeting.
1-5.
-
Xie, J., Mondal, A., Efendiev, Y., Mallick, B., & Datta-Gupta, A.
(2010).
History Matching Channelized Reservoirs Using Reversible Jump Markov Chain Monte Carlo Methods. 393-412.
-
Drake, R. P., Doss, F. W., Fryxell, B., Grosskopf, M. J., Holloway, J. P., van der Holst, B., ... Bingham, D.
(2009).
Using High Power Lasers to Create Radiative Shock Waves. 317-+.
-
Mallick, B. K., Denison, D., & Gangopadhyay, A. K.
(2002).
A Bayesian curve fitting approach to power spectrum estimation. Journal of Nonparametric Statistics.
141-153.
-
Ray, S., Chan, A., & Mallick, B.
(2002).
Bayesian Wavelet Shrinkage in Transformation Based Normal Models. i-876-i-879.
-
Geng, X., Tong, L., Bhattacharya, A., Mallick, B., & Xie, L. e.
(2021).
Probabilistic Hosting Capacity Analysis via Bayesian Optimization. IEEE Power and Energy Society General Meeting.
1-5.
-
institutional repository document
-
Jaiswal, P., Pati, D., Bhattacharya, A., & Mallick, B. K.
(2023).
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
-
Niu, Y., Ni, Y., Pati, D., & Mallick, B. K.
(2023).
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data
-
Chakrabarti, A., Ni, Y., & Mallick, B. K.
(2023).
Bayesian Flexible Modelling of Spatially Resolved Transcriptomic Data
-
Lee, K., Zhao, P., Bhattacharya, A., Mallick, B. K., & Xie, L. e.
(2023).
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems
-
Das, S., Niu, Y., Ni, Y., Mallick, B. K., & Pati, D.
(2023).
Blocked Gibbs sampler for hierarchical Dirichlet processes
-
Dasgupta, S., Zhao, P., Helwig, J., Ghosh, P., Pati, D., & Mallick, B. K.
(2023).
An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling
-
Chakrabarti, A., Ni, Y., Morris, E., Salinas, M. L., Chapkin, R. S., & Mallick, B. K.
(2023).
Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data
-
Lei, B., Xu, D., Zhang, R., He, S., & Mallick, B. K.
(2023).
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
-
Ghosh, R. P., Maity, A. K., Pourahmadi, M., & Mallick, B. K.
(2022).
Adaptive Bayesian Variable Clustering via Structural Learning of Breast Cancer Data
-
Payne, R. D., Guha, N., & Mallick, B. K.
(2022).
A Bayesian Survival Tree Partition Model Using Latent Gaussian Processes
-
Zhao, P., Bhattacharya, A., Pati, D., & Mallick, B. K.
(2022).
Factorized Fusion Shrinkage for Dynamic Relational Data
-
Zhao, P., Bhattacharya, A., Pati, D., & Mallick, B. K.
(2022).
Structured Optimal Variational Inference for Dynamic Latent Space Models
-
Dasgupta, S., Niu, Y., Panaganti, K., Kalathil, D., Pati, D., & Mallick, B.
(2021).
Off-Policy Evaluation Using Information Borrowing and Context-Based Switching
-
Maity, A. K., Lee, S. C., Mallick, B. K., & Sarkar, T. R.
(2021).
Bayesian Structural Equation Modeling in Multiple Omics Data Integration with Application to Circadian Genes
-
Mondal, A., & Mallick, B.
(2021).
Spline-Based Bayesian Emulators for Large Scale Spatial Inverse Problems
-
Niu, Y., Guha, N., De, D., Bhadra, A., Baladandayuthapani, V., & Mallick, B. K.
(2020).
Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures
-
Lee, S. Y., Zhao, P., Pati, D., & Mallick, B. K.
(2020).
Tail-adaptive Bayesian shrinkage
-
Lee, S. Y., Lei, B., & Mallick, B. K.
(2020).
Estimation of COVID-19 spread curves integrating global data and borrowing information
-
Lee, Y., Lei, B., & Mallick, B. K.
(2020).
Estimation of COVID-19 spread curves integrating global data and borrowing information
-
Afrin, K., Iquebal, A. S., Karimi, M., Souris, A., Lee, S. Y., & Mallick, B. K.
(2020).
Directionally Dependent Multi-View Clustering Using Copula Model
-
Geng, X., Tong, L., Bhattacharya, A., Mallick, B., & Xie, L. e.
(2020).
Probabilistic Hosting Capacity Analysis via Bayesian Optimization
-
Sarkar, A., Pati, D., Mallick, B. K., & Carroll, R. J.
(2019).
Bayesian Copula Density Deconvolution for Zero-Inflated Data in Nutritional Epidemiology
-
Niu, Y., Pati, D., & Mallick, B.
(2019).
Bayesian Graph Selection Consistency Under Model Misspecification
-
Payne, R. D., Guha, N., Ding, Y. u., & Mallick, B. K.
(2017).
A Conditional Density Estimation Partition Model Using Logistic Gaussian Processes
-
Efendiev, Y., Leung, W. T., Cheung, S. W., Guha, N., Hoang, V. H., & Mallick, B.
(2017).
Bayesian Multiscale Finite Element Methods. Modeling missing subgrid information probabilistically
-
Chakraborty, A., Bhattacharya, A., & Mallick, B. K.
(2016).
Bayesian sparse multiple regression for simultaneous rank reduction and variable selection
-
Yang, K., Guha, N., Efendiev, Y., & Mallick, B. K.
(2016).
Bayesian and Variational Bayesian approaches for flows in heterogenous random media
-
Guha, N., Baladandayuthapani, V., & Mallick, B. K.
(2016).
Quantile Graphical Models: Bayesian Approaches
-
Kundu, S., Shin, M., Cheng, Y., Manyam, G., Mallick, B. K., & Baladandayuthapani, V.
(2015).
Bayesian Variable Selection with Structure Learning: Applications in Integrative Genomics
-
Bhattacharya, A., Chakraborty, A., & Mallick, B. K.
(2015).
Fast sampling with Gaussian scale-mixture priors in high-dimensional regression
-
Payne, R. D., & Mallick, B. K.
(2014).
Two-Stage Metropolis-Hastings for Tall Data
-
Sarkar, A., Pati, D., Mallick, B. K., & Carroll, R. J.
(2014).
Bayesian Semiparametric Multivariate Density Deconvolution
-
Baladandayuthapani, V., Talluri, R., Ji, Y., Coombes, K. R., Lu, Y., Hennessy, B. T., Davies, M. A., & Mallick, B. K.
(2014).
Bayesian sparse graphical models for classification with application to protein expression data
-
Talluri, R., Baladandayuthapani, V., & Mallick, B. K.
(2013).
Bayesian sparse graphical models and their mixtures using lasso selection priors
-
Zhang, L., Sarkar, A., & Mallick, B. K.
(2013).
Bayesian Low Rank and Sparse Covariance Matrix Decomposition
-
Sarkar, A., Pati, D., Mallick, B. K., & Carroll, R. J.
(2013).
Adaptive Posterior Convergence Rates in Bayesian Density Deconvolution with Supersmooth Errors
-
Kundu, S., Baladandayuthapani, V., & Mallick, B. K.
(2013).
Bayes Regularized Graphical Model Estimation in High Dimensions
-
Konomi, B. A., Dhavala, S. S., Huang, J. Z., Kundu, S., Huitink, D., Liang, H., Ding, Y. u., & Mallick, B. K.
(2013).
Bayesian object classification of gold nanoparticles
-
Hartman, B. M., Mallick, B. K., & Talukdar, D.
(2012).
Investigating international new product diffusion speed: A semiparametric approach
-
Sarkar, A., Bhadra, A., & Mallick, B. K.
(2012).
Nonparametric Bayesian Approaches to Non-homogeneous Hidden Markov Models
-
Xun, X., Mallick, B., Carroll, R. J., & Kuchment, P.
(2011).
A Bayesian Approach to Detection of Small Low Emission Sources
-
Parzen, M., Ghosh, S., Lipsitz, S., Sinha, D., Fitzmaurice, G. M., Mallick, B. K., & Ibrahim, J. G.
(2011).
A generalized linear mixed model for longitudinal binary data with a marginal logit link function
-
Jaiswal, P., Pati, D., Bhattacharya, A., & Mallick, B. K.
(2023).
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
editor of
-
book
-
Denison, D. D., Hansen, M. H., Holmes, C. C., Mallick, B., & Yu, B.
(2013).
Nonlinear Estimation and Classification
Mallick, B. K. (Eds.),
Springer Science & Business Media.
-
Dey, D. K., Ghosh, S., & Mallick, B. K.
(2010).
Bayesian Modeling in Bioinformatics
Mallick, B. K. (Eds.),
CRC Press.
-
Dey, D. K., Ghosh, S. K., & Mallick, B. K.
(2000).
Generalized Linear Models A Bayesian Perspective
Mallick, B. K. (Eds.),
CRC Press.
-
Denison, D. D., Hansen, M. H., Holmes, C. C., Mallick, B., & Yu, B.
(2013).
Nonlinear Estimation and Classification
Mallick, B. K. (Eds.),
Springer Science & Business Media.
Research
principal investigator on
- HDR Tripods: Texas A&M Research Institute for Interdisciplinary Foundations of DATA Science (TRIFECDAS) awarded by National Science Foundation - (Arlington, Virginia, United States) 2019 - 2022
- Graph-based Bayesian Analysis of Genomics and Proteomics Data awarded by National Institutes of Health - (Bethesda, Maryland, United States) 2015 - 2020
co-principal investigator on
- Small Animal Model Vaccines and Pathogenesis - BA 23-1171 awarded by National Institutes of Health - (Bethesda, Maryland, United States) 2017 - 2024
- BIGDATA: IA: Collaborative Research: From Bytes to Watts - A Data Science Solution to Improve Wind Energy Reliability and Operation awarded by National Science Foundation - (Arlington, Virginia, United States) 2017 - 2020
- Cloud Computing Based Robust Space Situational Awareness awarded by United States Air Force - (Arlington, Virginia, United States) 2015 - 2018
- Scalable multilevel uncertainty quantification concepts for extreme-scale multiscale problems awarded by United States Department of Energy - (Washington D.C., District of Columbia, United States) 2013 - 2018
investigator on
Teaching
teaching activities
- STAT491 Research Instructor
- STAT632 Stat Meth Ii Bayes Model Instructor
- STAT633 Adv Bayes Model & Comput Instructor
- STAT652 Stat In Research Ii Instructor
- STAT685 Directed Studies Instructor
- STAT691 Research Instructor
Works By Students
chaired theses and dissertations
-
Bae, Kyounghwa (2005-08). Bayesian model-based approaches with MCMC computation to some bioinformatics problems.
-
Baladandayuthapani, Veerabhadran (2007-04). Bayesian methods in bioinformatics.
-
Chakraborty, Antik (2018-08). Bayesian Shrinkage: Computation, Methods and Theory.
-
Chang, Ilsung (2006-08). Bayesian inference on mixture models and their applications.
-
De, Debkumar (2014-05). Essays on Bayesian Time Series and Variable Selection.
-
Dhavala, Soma Sekhar (2012-02). Bayesian Semiparametric Models for Heterogeneous Cross-platform Differential Gene Expression.
-
Ghosh, Souparno (2010-10). Copula Based Hierarchical Bayesian Models.
-
Gold, David L. (2009-05). Bayesian learning in bioinformatics.
-
Hartman, Brian Matthew (2011-10). Bayesian Hierarchical, Semiparametric, and Nonparametric Methods for International New Product Di ffusion.
-
Konomi, Bledar (2012-02). Bayesian Spatial Modeling of Complex and High Dimensional Data.
-
Lee, Kyeong Eun (2005-08). Bayesian models for DNA microarray data analysis.
-
Mondal, Anirban (2012-10). Bayesian Uncertainty Quantification for Large Scale Spatial Inverse Problems.
-
Payne, Richard Daniel (2018-03). Two-Stage Metropolis Hastings; Bayesian Conditional Density Estimation & Survival Analysis via Partition Modeling, Laplace Approximations, and Efficient Computation.
-
Ray, Shubhankar (2006-10). Nonparametric Bayesian analysis of some clustering problems.
-
Ryu, Duchwan (2005-08). Regression analysis with longitudinal measurements.
-
Sarkar, Abhra (2014-06). Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors.
-
Song, Joon Jin (2004-11). Bayesian multivariate spatial models and their applications.
-
Talluri, Rajesh (2012-10). Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.
-
Wang, Xiaohui (2009-05). Bayesian classification and survival analysis with curve predictors.
-
Wang, Yanqing (2014-08). Relative Risks Analysis in Nutritional Epidemiology.
-
Xun, Xiaolei (2012-07). Statistical Inference in Inverse Problems.
-
Zhang, Lin (2012-08). Application of Bayesian Hierarchical Models in Genetic Data Analysis.
Background
education and training
- Ph.D. in Statistics, University of Connecticut - (Storrs, Connecticut, United States) 1994
awards and honors
- Association of Former Students University-Level Distinguished Achievement Award, The, conferred by Texas A&M University - (College Station, Texas, United States), 2019
- Traditional Fulbright Scholarship, conferred by Council for International Exchange of Scholars - (Washington D.C., District of Columbia, United States), 2017
- IIE Transactions Award, conferred by Institute of Industrial Engineers - (Hong Kong, Hong Kong, China), 2014
- AAAS Fellow, conferred by American Association For The Advancement of Science - (Washington D.C., District of Columbia, United States), 2013
- Chapter Service Recognition Award, conferred by American Statistical Association - (Alexandria, Virginia, United States), 2010
- ASA Fellows, conferred by American Statistical Association - (Alexandria, Virginia, United States), 2005
Contact
first name
- Bani
has last name
- Mallick
mailing address
-
Texas A&M University
Department Of Statistics
3143 TAMU
College Station, TX 77843-3143
USA
Other
- bani-k-mallick@tamu.edu