Dr. Raj Guhaniyogi received his Ph.D. in Statistics from University of Minnesota in April 2012 and was a postdoctoral researcher at Duke University between 2012-2014, prior to being an Assistant and Associate Professor in the University of California Santa Cruz between 2014-2021. He joined the Department of Statistics at Texas A&M as an Associate Professor in Fall 2021. His research focuses on Bayesian regression with heterogeneous non-Euclidean objects, Bayesian data Sketching with random compression matrices, distributed Bayesian inference for massive structured data, Bayesian high dimensional regression, deep learning, federated learning, manifold regression, online Bayesian learning with streaming data, spatial and spatio-temporal modeling for big data in direct collaboration with scientists in the study of neurodegenrative disorders from multi-modal neuroimaging data, and in the study of forestry and environmental health from remote sensing data. He is the recipient of the Early Investigator Award from the American Statistical Association, Section on Statistics and Environment (ENVR) in 2023 and also a Hellman Fellowship from the University of California in 2016. He currently serves the Editorial Boards of Journal of Machine Learning Research (JMLR), Journal of Computational and Graphical Statistics (JCGS), New England Journal of Statistics in Data Sciences and Sankhya Ser. B.