Faculty in Statistical Bioinformatics

Meet our Faculty and learn about their background

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

Dr Moumita Karmakar is an Instructional Assistant Professor in the Department of Statistics at Texas A&M University. Her research interests involve analyzing high-throughput genomic datasets for statistical patterns and developing statistical methodology for toxicological problems. Her pedagogical interests involve teaching the Bioinformatics course in the Department of Statistics for online masters students and introductory statistics courses for Texas A&M undergraduates.

Yang Ni

Yang Ni is the Co-Director of the Center and Assistant Professor of Statistics. His current methodological interests include causal discovery, graphical models, flexible Bayesian semi/nonparametric models, and machine learning. His research is primarily motivated by microbiome multi-omic data, single-cell multi-omic data, and large electronic health records.

Tapasree RoySarkar

Tapasree is the Research Assistant Professor at the Department of Biology. Her current research interests include cancer progression and metastasis, epithelial to mesenchymal transition, cancer bioinformatics, cancer related multi-omics data, cancer metabolomics, and nanotechnology.
External Advisory Board

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

Alicia Laura Carriquiry  is a distinguished professor of statistics at Iowa State University, and was president of the International Society for Bayesian Analysis in 2001.Her research applies Bayesian statistics to nutritiongenomicsforensics, and traffic safetyCarriquiry was elected as a member of the International Statistical Institute in 1995. She became a fellow of the American Statistical Association in 1999, and of the Institute of Mathematical Statistics in 2006. In 2016, she was elected to the National Academy of Medicine. She serves on the board of directors for the International Society for Bayesian Analysis.

Dipak Dey

Prof. Dipak K. Dey is a Board of Trustees Distinguished Professor in the Department of Statistics at the University of Connecticut (UConn). A prominent statistician, he is most known for his pioneering work in Bayesian analysis, decision science, and model selection. With over 320 research articles published in reputable national and international journals, and over 10 books and edited volumes to his name, he has made a significant impact on the field of statistics and data science. 

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

Professor and Chair, Biostatistics and Informatics, Colorado. Lover of Data, Inference, Computing .

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. 


Peter Muller

Peter Mueller is Professor of Statistics and Mathematics at UT Austin. He works on Bayesian inference, with a focus on nonparametric Bayesian methods, simulation based methods, optimal design and multiple comparison procedures. He is interested in applications in biostatistics and bioinformatics, including in particular Bayesian clinical trial design, hierarchical models, population PK/PD models, inference for histone modifications and tumor heterogeneity.

Giovanni Parmigiana

Dr. Parmigiani is Professor of Biostatistics at Harvard University and Chair of the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute. Dr. Parmigiani is an expert in Bayesian methods and one of the foremost biostatistical methodologists and practitioners in cancer clinical trials and cancer genomics.
University Advisory Board

Raymond J. Carroll

Raymond Carroll is Distinguished Professor of Statistics, Nutrition and Toxicology. He is the former Director of this Center. His laboratory is the Laboratory for Statistical Bioinformatics in Nutrition and Cancer.

Robb Chapkin

Robb Chapkin is a Distinguished Professor of Nutrition and Food Science. He is a mentor in the Biofinformatics and Nutrition Training Program and a director of the Genomics and Bioinformatics Facility Core of the Center for Environmental and Rural Health.

Kenneth Ramos

Kenneth S. Ramos, MD, PhD is a Professor of Translational Medical Sciences and the Alkek Chair of Medical Genetics. He is an accomplished physician-scientist with designations in the National Academy of Medicine and National Academy of Sciences. He is a transformational leader recognized throughout the world for his scientific contributions in the areas of genomics, precision medicine and toxicology.
Other Faculty

Rodolfo Aramayois

Rodolfo Aramayois an Associate Professor for the Department of Biology at Texas A&M University His interests are with Genetics, Epigenetics and Meiotic RNA Silencing

Anirban Bhattacharya

Anirban is an Associate Professor of Statistics. Develops parsimonious models for high dimensional contingency table data, motivated by epidemiology and genetic applications

James Cai

James Cai is an Associate Professor in Veterinary Medicine & Biomedical Sciences. His lab works at the interface of human genetics, computational statistics, and data science. Current research focuses on understanding diverse behaviors of cells using machine learning, network science and dynamical system analysis.

Alan Dabney

Alan Dabney is an Associate Professor and Associate Department Head for Teaching Excellence of Statistics, specializing in microarrays and proteomics.

Jessica Galloway-Peña

Jessica Galloway-Peña is an Assistant Professor in Veterinary Pathobiology. Her lab is dedicated to understanding the molecular basis of pathogenesis and how the microbiota influence the persistence, transmission, and evolution of pathogens.

Irtisha Singh

Irtisha Singh is an Assistant Professor of Molecular and Cellular Medicine, specializing in computational algorithms, statistical methods, and molecular biology, for hypothesis-driven analysis of the high throughput datasets.

Samiran Sinha

Samiran Sinha is a Professor of Statistics, specializing in Bayesian computation.

Valen Johnson, Dean, College of Science

Valen Johnson is Professor of Statistics. His current methodological interests focus on Bayesian hypothesis testing and its connections to classical testing procedures, Bayesian variable selection, Markov chain Monte Carlo model diagnostics and latent variable modeling.

Charlie Johnson

Charlie Johnson is the Managing Director for the Center for Bioinformatics and Genomics Systems Engineering Joint Tees & Agrilife Center

Wenyi Wang

Wenyi Wang is a Professor, Department of Bioinformatics and Computational Biology at MD Anderson, specializing in data wrangling to understand big data generated by cancer multi-omics.

Raymond Wong

Raymond Wong is an Associate Professor in Statistics, specializing in Bioinformatics problems with modern data complications such as enormous volume, large dimensionality and manifold structures.