- Associate Professor, Department of Statistics
- Engineering 2, Room 539A
- Rajarshi Guhaniyogi received his B.Stat.(Hons.) degree in 2006, M.Stat. in 2008, from the Indian Statistical Institute, Kolkata, India, and Ph.D. in 2012 from the Division of Biostatistics at the University of Minnesota, Twin Cities. After finishing his Ph.D., he worked as a Postdoctoral Researcher with Dr. David B. Dunson in the Department of Statistical Science at Duke University, Durham, North Carolina, until June, 2014. His Ph.D. dissertation focuses on developing novel hierarchical Bayesian modeling techniques for large spatial data under the supervision of Dr. Sudipto Banerjee. As a postdoc in the Department of Statistical Science at Duke University, Rajarshi has developed massive dimensional parametric and non-parametric Bayesian methods motivated by improving practical performance in real world applications in batch and online data settings, using statistical theory to justify and guide the development of new methods. Rajarshi is a recipient of the Distinguished Student Paper Award, Eastern North American Region, 2012, Student Paper Competition Award, Section on Environmental Statistics, Joint Statistical Meetings, 2012, Jacob E. Bearman Outstanding Student Achievement Award, University of Minnesota, 2012, Minnesota Medical Foundation Fellowship, 2009 and numerous fellowships and awards from the Government of India for outstanding achievement as an undergraduate and graduate student. His research interests lie broadly in development and application of Bayesian parametric and non-parametric methodology in high dimensional machine learning problems.
- Areal wombling, compressive methods for high-dimensional regression, multi-linear modeling, manifold regression, nonparametric bayes, online learning with massive streaming data, spatial bayes modeling for massive geostatistical datasets, bayesian tensor regression; applications in epidemiology, forestry, genomics, neuroscience; machine learning applications
- Bayesian Compressed Regression
- Bayesian Conditional Density Filtering
- Compressed Gaussian Process for Manifold Regression
- Modeling Low- rank Spatially-Varying Cross-Covariances using Predictive Process with Applica- tion to Soil Nutrient Data.
- Adaptive Gaussian predictive process models for large spatial datasets
- Ph.D. in Biostatistics, 2012, University of Minnesota, Twin Cities
- M. Stat. in Mathematical Statistics & Probability, 2008, Indian Statistical Institute, Kolkata, India
- B. Stat. in Mathematics & Statistics, 2006, Indian Statistical Institute, Kolkata, India
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