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. His Ph.D. dissertation focused on developing novel hierarchical Bayesian modeling techniques for large spatial data. Between 2012-2014, Rajarshi worked as a Postdoctoral Researcher with Dr. David B. Dunson in the Department of Statistical Science at Duke University, Durham, North Carolina. Rajarshi was an Assistant Professor in the Department of Statistics at the University of California Santa Cruz from 2014 to 2020. Since Summer 2020, he has been promoted to Associate Professor with tenure.
Rajarshi has been developing 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 Hellman Fellowship, University of California, 2016-2017, 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 of Bayesian parametric and non-parametric methodology in complex biomedical and machine learning applications. His ongoing research focus is on scalable Bayesian methods for big data, dimensionality reduction, functional and object data (networks, tensor, text) analysis. Rajarshi draws his motivation from applications in epidemiology, genetics, neuroscience, environmental science, forestry and social science.
Here is the most recent CV.