Rajarshi Guhaniyogi

Associate Professor, Department of Statistics

Texas A&M University

My research interests lie broadly in the development of Bayesian parametric and non-parametric methodology in complex biomedical and machine learning applications. My ongoing research focuses on Bayesian tensor regression, Bayesian regression with heterogeneous 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. 

I strongly believe that the improvement of statistical methods is intrinsically tied to addressing complex real-world problems. Collaboration with scientists from various fields plays a pivotal role in achieving this objective, and I find great fulfillment in working alongside both domain experts and fellow researchers in statistics and methodology. My ongoing methodological research threads are directly motivated by strong collaboration with neuroscientists on multi-modal neuroimaing Data, and with environmental and forestry scientists on remote sensing data. 

I am honored to receive the Early Investigator Award in 2023 by the American Statistical Association, Section on Statistics and Environment (ENVR) "For exceptional contributions to statistical methodology for Bayesian inference and machine learning methods through rich hierarchical frameworks for high-dimensional environmental data, for student mentoring and for service to the profession." I am also a recipient of the Early Career Award for Statistics and Data Sciences (ECASDS) in 2023 from the International Indian Statistical Association (IISA). I have also been the recipient of the Hellman Fellowship in 2016 awarded by the University of California, Distinguished Student Paper Award in 2012 by ENAR and JSM Student Paper Competition in 2012 by the American Statistical Association, Section on Statistics and Environment (ENVR).

My research is supported by National Institute of Health (R01 award - score of 1 percentile), National Science Foundation (Division of Mathematical Sciences), Office of Naval Research and other federally funded projects with me as PI / Co-PI. 

NEWS: