Veera Baladandayuthapani, PhD

Dr. Veera Baladandayuthapani is currently a Professor in the Department of Biostatistics at University of Michigan (UM), 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 A&M 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. His work has been published in top statistical/biostatistical/bioinformatics and biomedical/oncology journals. He has also co-authored a book on Bayesian analysis of gene expression data. He currently holds multiple PI-level grants from NIH and NSF to develop innovative and advanced biostatistical and bioinformatics methods for big datasets in oncology. He has also served as the Director of the Biostatistics and Bioinformatics Cores for the Specialized Programs of Research Excellence (SPOREs) in Multiple Myeloma and Lung Cancer and Biostatistics&Bioinformatics platform leader for the Myeloma and Melanoma Moonshot Programs at MD Anderson. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. He currently serves as an Associate Editor for Journal of American Statistical Association, Biometrics and Sankhya.

• BIOSTAT 651: Winter 2019

• PhD, Statistics, Texas A&M University, College Station, TX, 2005
• M.A., Statistics,  University of Rochester, Rochester, NY, 2000
• B.S. (Honors), Mathematics, Indian Institute of Technology (IIT), Kharagpur, India, 1999

My research program sits at the intersection of statistics, biology and medicine. My statistical methodology interests are mainly in high-dimensional/Big data modeling and Bayesian inference. This includes Bayesian bioinformatics, functional data analyses, graphical models, Bayesian semi-/non parametric models and Bayesian machine learning. These methods are motivated by modern biomedical technologies generating large and complex-structured datasets such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. A special focus is on developing integrative models combining different sources of biomedical big data for biomarker discovery and clinical prediction to aid precision/translational medicine.

Through the course of my career, I have developed a broad perspective of applied scientific problems, leveraging the underlying scientific hypotheses to be the motivating factors for development of new statistical and computational methodologies. An overarching goal of my research plan is to provide accurate probabilistic representations of applied problems using novel data-driven, robust and flexible statistical methods, incorporating all sources of knowledge from the substantive area of investigation – to achieve impactful scientific results.

• Ha, M*, Banerjee S., Akbani R, Liang H, Mills, G, Do K-A, Baladandayuthapani, V. Personalized Integrated Network Modeling of the Cancer Proteome Atlas. Nature Scientific Reports, 2018
  
  
• Ni, Y*, Stingo S. and Baladandayuthapani, V. Bayesian Hierarchical Varying-sparsity Model with Application to Cancer Proteogenomics. (2018) Journal of American Statistical Association
  
  
• Bharath K, Kambadur, P., Dey D., Rao. A, and Baladandayuthapani, V. Statistical Tests For Large Tree-structured Data (2017) Journal of the American Statistical Association
  
  
• Ni, Y*, Stingo S. and Baladandayuthapani, V. Bayesian Graphical Regression (2018) Journal of the American Statistical Association
  
  
• Bharath, K, Kurtek, S, Rao, A.U.K., Baladandayuthapani, V (2018) Radiologic Image-based Statistical Shape Analysis of Brain Tumors. Journal of Royal Statistical Society - Series C
  
  
• Ni, Y*, Stingo S. and Baladandayuthapani, V. Sparse Multi-dimensional Graphical Models: A Bayesian Unified Framework. (2017) Journal of the American Statistical Association
  
  
• Saha, A.*, Banerjee, S., Narang, S., Rao, G., Martinez, J., Rao, A.U.K., Baladandayuthapani, V. (2016) DEMARCATE: Density-based Magnetic Resonance Image Clustering for Assessing Tumor Heterogeneity in Cancer. Neuroimage: Clinical (to appear).
  
  
• Wang W*, Baladandayuthapani, V.**, Morris JS, Broom BM, Manyam G, Do KA. (2012) iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics
  
  
• Baladandayuthapani, V, Talluri, R, Ji Y., Coombes, K., Hennessy, B., Davies, M., Mallick B. K. Bayesian Sparse Graphical Models for Classification with Application to Protein Expression Data (2015) Annals of Applied Statistics.
  
  
• Azadeh, S*, Hobbs, B., Moeller, F., Nielsen, D., Baladandayuthapani, V. Integrative Bayesian Analysis of Neuroimaging-Genetic Data with Application to Cocaine Dependence. Neuroimage (2015)
  
  
• Zhang X, Baladandayuthapani, V, Lin H, Mulligan G, .., Barlogie B.,.., Davis R. E., Ma W. C., Wang Z., Yang L., and Orlowski R. Z. (2016) Tight Junction Protein 1 Modulates Proteasome Capacity and Proteasome Inhibitor Sensitivity in Multiple Myeloma Through EGFR/JAK1/STAT3 Signaling. Cancer

• Baljevic, M, Baladandayuthapani. V Lin, H.Y, Partovi, C.M, Berkova, ... Zaman, S, and Gandhi, V. V. and Orlowski, R.Z, Phase II Study of the c-MET Inhibitor ARQ 197 (Tivantinib) in Patients with Re- lapsed or Relapsed/Refractory Multiple Myeloma. (2017) Annals of Hematology
  
  ∗ = student/post-doctoral trainee of Dr. Baladandayuthapani at the time of this research.