Faculty in Statistical Bioinformatics
Meet our Faculty and learn about their background
Dr. Bani Mallick
Director
Dr. Bani K. Mallick https://web.stat.tamu.edu/~bmallick/ is a Distinguished Professor and Susan M. Arseven `75 Chair in Data Science and Computational Statistics in the Department of Statistics at Texas A&M University in College Station. He is also the Director of the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS) . Dr. Mallick is well known for his contribution to the theory and practice of Bayesian Semiparametric methods and Uncertainty Quantification. He is an elected fellow of American Association for the Advancement of Science, American Statistical Association, Institute of Mathematical Statistics, International Statistical Institute and the Royal Statistical Society. Mallick’s areas of research include semiparametric classification and regression, hierarchical spatial modeling, inverse problem, uncertainty quantification and Bioinformatics.
Yuchao Jiang
The Jiang Lab’s primary research interests lie in statistical modeling, method development and data analysis in genetics and genomics. Current research is focused on developing statistical methods and computational algorithms to better utilize and analyze different types of next-generation sequencing data under various setting, with application to data from large-scale cohort studies of human health and disease.
Moumita Karmakar
Yang Ni
Tapasree RoySarkar
Veera Baladandayuthapani
Dr. Veera Baladandayuthapani is currently a Professor in the Department of Biostatistics, where he is also the Associate Director of the Center for Cancer Biostatistics. He joined UM in Fall 2018 after spending 13 years in the Department of Biostatistics at University of Texas MD Anderson Cancer Center, Houston, Texas, where was a Professor and Institute Faculty Scholar and held adjunct appointments at Rice University, Texas AandM University and UT School of Public Health. His research interests are mainly in high-dimensional data modeling and Bayesian inference. This includes functional data analyses, Bayesian graphical models, Bayesian semi-/non-parametric models and Bayesian machine learning. These methods are motivated by large and complex datasets (a.k.a. Big Data) such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro and cancer- imaging.
Alicia Carriquiry
Dipak Dey
Kim-Anh Do
Kim-Anh Do, Ph.D., is Professor and Chair in the Department of Biostatistics at MD Anderson, a recipient of the Faculty Scholar Award at MD Anderson in 2003 and the Electa C. Taylor Chair for Cancer Research in 2017. She is a Fellow of the American Statistical Association, the American Association for the Advancement of Science (AAAS) and the Royal Statistical Society and is an Elected Member of the International Statistical Institute. She has served as a primary statistician or co-investigator on several National Institutes of Health (NIH) funded grants and clinical trials in prostate cancer, epidemiology, leukemia, upper aerodigestive cancer, breast cancer and brain cancer, including the Early Detection Research Network (EDRN) grant, the Prostate SPORE (as Director of the Biostatistics Core), the Breast SPORE, and the Brain SPORE at M. D. Anderson.
Debashis Ghosh
Jeffrey Morris
George S. Pepper Professor of Public Health and Preventative Medicine and the Director of the Division of Biostatistics. His research interests focus on developing quantitative methods to extract knowledge from biomedical big data, including work to relate complex biomedical object data—including functions, images and manifolds—to patient outcomes and characteristics using flexible, automated regression methods, and to integrate information across multiple types of multi-platform genomic, proteomic, imaging, and wearable device data to uncover biomedical insights contained in these complex data.