Thursday, January 17, 2-3pm
100 Washtenaw Avenue
Ann Arbor, MI 48109
4th floor, Forum Hall
Veera Baladandayuthapani: “Integrative Big Data Models for Precision Medicine”
Veera Baladandayuthapani, PhD, is currently a Professor in the Department of Biostatistics at the University of Michigan. He joined U-M in fall 2018 after spending 13 years in the Department of Biostatistics at the 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 co-authored a book on Bayesian analysis of gene expression data, and 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.
Modern biomedicine has generated unprecedented amounts of data. A combination of clinical, environmental, and public health information, proliferation of associated genomic information, and increasingly complex digital information have created unique challenges in assimilating, organizing, analyzing, and interpreting such structured, as well as unstructured, data. Each of these distinct data types provides a different, partly independent and complementary, high-resolution view of various biological processes. Modeling and inference in such studies is challenging, not only due to high dimensionality, but also due to presence of structured dependencies (e.g., pathway/regulatory mechanisms, serial and spatial correlations, etc.). Integrative analyses of these multi-domain data combined with patients’ clinical outcomes can help us understand the complex biological processes that characterize a disease, as well as how these processes relate to the eventual progression and development of a disease. This talk will cover statistical and computational frameworks that acknowledge and exploit these inherent complex structural relationships for both biomarker discovery and clinical prediction to aid translational medicine. The approaches will be illustrated using several case examples in oncology.