Rajarshi Guhaniyogi

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I am a postdoctoral researcher since April 2012 working with Prof. David B. Dunson at the Duke University. Prior to this, I completed my Ph.D. from the University of Minnesota under the supervision of Prof. Sudipto Banerjee. I obtained my undergraduate (B.STAT) and masters (M.STAT) degree in Statistics from Indian Statistical Institute, Kolkata with a specialization in Mathematical Statistics and Probability in 2006 and 2008 respectively.

Apart from Statistics, I am interested in world cinema, literature, Indian music and travel. I especially enjoy following current affairs.

Research Interests

Areal Wombling, Approximate Bayes, Bayesian Nonparametrics, Bayesian Asymptotics, Compressive Sensing Methods for Massive Datasets, Image Analysis, Multi-linear Modeling, Manifold Regression, Misaligned Data, Network analysis, Online Learning, Spatial Statistics, Tensor Regression.

Selected Papers

  • Bayesian Compressed Regression (with David B. Dunson). Accepted subject to minor revision in JASA: Theory & Methods, 2013.
  • Modeling Low-rank Spatially-Varying Cross-Covariances using Predictive Process with Application to Soil Nutrient Data (with A.O. Finley, S. Banerjee, and R. Kobe). Accepted in Journal of Agricultural, Biological, and Environmental Statistics, 2013. Winner of the ENAR Distinguished Student Paper Award, 2012 and Student Paper Award, JSM 2012
  • Adaptive Gaussian Predictive Process Models for Large Spatial Datasets (with Andrew O. Finley, Sudipto Banerjee, and Alan E. Gelfand). Environmetrics, 22, 997-1007, 2011.

Awards & Honors

  • Distinguished Student Paper Award, ENAR, Washington DC, 2012.
  • Student Paper Competition Award, Section on Environmental Statistics, JSM, San Diego, 2012.
  • Jacob E. Bearman Outstanding Student Achievement Award, Division of Biostatistics, University of Minnesota, Minneapolis, 2012.
  • Summer Fellowship, Minnesota Medical Foundation, Minneapolis, Summer 2009.
  • National Scholarship, Indian Statistical Institute, India, 2003-2008.

Invited Talks

  • Compressed Gaussian Process, International Indian Statistical Association Conference, Riverside (June 2014).
  • Bayesian Conditional Density Filtering for Massive Streaming Datasets, 3rd Institute of Mathematical Statistics Asia-Pacific Rim Meeting, Taipei (June 2014).
  • Large Scale Nonparametric Bayesian Manifold Regression, Session on Bayesian Semi- and Nonparametric Modelling, ERCIM WG Conference on Computing & Statistics, London (December 2013).
  • Some Recent Developments on the Modeling of Massive Dimensional Nonstationary Data Sets, Joint Statistical Meeting, Montreal (August 2013).
  • Modeling Nonstationary Cross-Covariances: a Low Rank Approach, International Chinese Statistical Association Symposium, Boston (June 2012).
  • On Bayesian Hierarchical Modeling for Large Datasets, M. D. Anderson Cancer Research Center, Houston (October 2011).
  • On Bayesian Hierarchical Modeling for Large Datasets, Joint Statistical Meeting, Miami (August 2011).

Contact Information

Department of Statistical Science
Duke University
214 Old Chemistry Building
Durham, NC 27705
Office: 223B Old Chemistry Building
Email: rg124@stat.duke.edu
Phone: (651) 207-7569