- BIOSTAT 651: Winter 2019
- Ph.D., 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
Research Interests & Projects
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
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
- 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
- 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.
- 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.