? Raj Guhaniyogi
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News!

  • 11/2018: Raj's article on Bayesian tensor response regression on brain activation studies is available online.
  • 08/2018: Group Github page has been updated.
  • 07/2018: Raj's PhD student Laura Baracaldo has successfully defended her PhD proposal. Congratulations Laura!
  • 06/2018: Raj's PhD student Rene Gutierrez has successfully defended his PhD proposal. Congratulations Rene!
  • 06/2018: Raj's article on meta kriging , a scalable Bayesian framework for large spatial data is published online on Technometrics.
  • 05/2018: Raj has been awarded research support from the Office of Naval Research (PI: Raj Guhaniyogi, $345,000).
  • 05/2018: Raj's article on distributed nonparametric Bayesian computation of massive spatial data is now on arxiv.
  • 05/2018: Article on multiscale spatial modeling for big data with tree shrinkage prior is on arxiv.
  • 02/2018: Aricle on joint modeling of longitudinal relation data and exogenous variables is available online.
  • 01/2018: Raj's PhD student Daniel Spencer has successfully defended his PhD proposal. Congratulations Daniel!

About Me

I am currently an Assistant Professor in the Department of Applied Mathematics & Statistics, University of California, Santa Cruz, since July 2014. Prior to this, I was a postdoctoral researcher working with Prof. David B. Dunson at Duke University. 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) degrees in Statistics from Indian Statistical Institute, Kolkata with a specialization in Mathematical Statistics and Probability in 2006 and 2008 respectively.

My research interests lie broadly in development of Bayesian parametric and non-parametric methodology in complex biomedical and machine learning applications. My ongoing research focus is on scalable Bayesian methods for big data, dimensionality reduction, functional and object data (networks, tensor, text) analysis. I draw motivation broadly from applications in epidemiology, genetics, neuroscience, environmental science, forestry and social science. A link of my github page with computer codes for research projects are available at Rajarshi Guhaniyogi Github. I sincerely thank the Office of Naval Research for generously funding my research.

Apart from Statistics, I like to watch movies and documentaries, read books, travel and explore new places. I take special interest in current affairs and cooking.

Educational History

  • B.STAT. Indian Statistical Institute, Kolkata, India, 2006.
  • M.STAT. Indian Statistical Institute, Kolkata, India, 2008.
  • Ph.D. Statistics, University of Minnesota, Twin Cities, Minnesota, USA, 2012.

Research Interests

  • Bayesian modeling (theory and methods) for massive & complex data
  • Modeling of genomic, environmental, forestry, neuroimaging & other machine learning data
  • Statistical computing and related software development.

Honors and awards

  • 2016-2017, Hellman Fellow, University of California Santa Cruz.
  • 2012, Distinguished Student Paper Award, ENAR, International Biometric Society.
  • 2012, Student Paper Award, Section on Environmental Statistics, Joint Statistical Meetings.
  • 2012, Jacob E. Bearman Outstanding Student Achievement Award, Division of Biostatistics, University of Minnesota.
  • 2009, Summer Fellowship, Minnesota Medical Foundation, Minneapolis.
  • 2003-2008, National Scholarship, Indian Statistical Institute, Kolkata, India.

Selected Invited Talks

DISK: Scalable Bayesian Framework for Massive Spatially Indexed Datasets, Invited Speaker, BIRS-CMO Workshop ON Recent Developments in Statistical Theory and Methods Based on Distributed Computing, Oaxaca, Mexico (May 2018).

Bayesian Large Scale Kriging, Invited Speaker, NASA JPL Workshop on Remote Sensing, Uncertainty Quantification and Theory of Data Systems, CalTech, Pasadena (February 2018).

Bayesian Large Scale Kriging, Invited Speaker, Department of Biostatistics and Epidemiology, University of California, San Francisco February 2018).

Massive Multiscale Spatial Kriging Using Tree Shrinkage Prior, Junior ISBA Invited Talk, Joint Statistical Meetings, Baltimore (August 2017).

Scalable Bayesian Regression Framework for Tensor Valued Objects, Invited speaker, Deaprtment of Statistics, University of California, Santa Barbara (April 2017).

Scalable Bayesian Regression Framework for Tensor Valued Objects, Department of Statistics, University of California, Davis (October 2016).

Spatial Meta Kriging: A Scalable Framework for Large Scale Data Analysis, IISA Annual Conference, Corvallis (August 2016).

Spatial Meta Kriging: A Scalable Framework for Large Scale Data Analysis, TIES Conference, Edinburgh, UK (July 2016).

Spatial Meta Kriging: A Scalable Framework for Large Scale Data Analysis, ISBA World Meeting, Sardinia, Italy (June 2016).

Bayesian Tensor Regression: Scalable Bayesian Regression Framework in Neuroscience Applications, SAMSI, Research Triangle Park (April 2016).

Bayesian Modeling for Big Data, special invited speaker, International Indian Statistical Association Meeting, Pune, India (December 2015).

Bayesian Modeling for Big Data, Department of Statistics, University of California, Los Angeles (November 2015).

Modeling Low-rank Spatially-Varying Cross-Covariances using Predictive Process, JABES Showcase Paper, Joint Statistical Meetings, Seattle (August 2015)

Bayesian Conditional Density Filtering, IASSL, Colombo, SriLanka (December 2014).

Some Recent Developments in Spatial Statistics with Large Datasets, TIES, International Environmetric Society, Guangzhou, China (December 2014).

Distributed Gaussian Process, Joint Statistical Meetings, Boston (August 2014).

Compressed Gaussian Process, International Indian Statistical Association Conference, Riverside (June 2014).

Bayesian Compression for High Dimensional Regression, Department of Statistics, Purdue University, West Lafayette (February 2014).

Bayesian Hierarchical Low Rank Spatial Process Models for Large Datasets, Department of Statistics, Virginia Tech University, Blacksburg (February 2014).

Bayesian Compression for High Dimensional Regression, Department of Statistics, Ohio State University, Columbus (February 2014).

Bayesian Compression for High Dimensional Regression, Department of Biostatistics, Johns Hopkins University, Baltimore (February 2014).

Bayesian Compression for High Dimensional Regression, Department of Applied Mathematics & Statistics, University of California, Santa Cruz (January 2014).

Selected Papers

Guhaniyogi, R., Qamar, S. and Dunson, D.B. (2017). Bayesian Tensor Regression. Journal of Machine Learning Research, 18, 1-31. full text

Guhaniyogi, R., Qamar, S. and Dunson, D.B. (2017). Bayesian Conditional Density Filtering. Journal of Computational and Graphical Statistics. Accepted, full text

Guhaniyogi, R. and Banerjee, S. (2017). Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets. Technometrics, Accepted, full text

Guhaniyogi, R.(2017). Bayesian Nonparametric Areal Wombling for Small Scale Maps with an Application to Urinary Bladder Cancer Data from Connecticut. Statistics in Medicine, DOI:10.1002/sim.7408 full text

Guhaniyogi, R. (2017). Convergence Rate of Bayesian Supervised Tensor Modeling with Multiway Shrinkage Priors. Journal of Multivariate Analysis, 160, 157-168 full text

Guhaniyogi, R. and Dunson, D.B. (2016). Compressed Gaussian Process for Manifold Regression. Journal of Machine Learning Research, 17, 1-26. full text

Guhaniyogi, R. and Dunson, D.B. (2016). Bayesian Compressed Regression. Journal of the American Statistical Association, Theory & Methods, 110, 1500-1514. full text

Guhaniyogi, R., Finely, A.O., Banerjee, S. and Kobe, R. (2013). Modeling Low-rank Spatially-Varying Cross-Covariances using Predictive Process with Application to Soil Nutrient Data. Journal of Agricultural, Biological and Environmental Statistics, 18, 274-298. full text

Guhaniyogi, R., Finely, A.O., Banerjee, S. and Gelfand, A.E. (2011). Adaptive Gaussian predictive process models for large spatial datasets. Environmetrics, 22, 997-1007. full text


Contact Information

Room 359A
Department of Applied Mathematics & Statistics
Baskin School of Engineering, UC Santa Cruz
Santa Cruz, CA 95064
Email: rguhaniy (at) ucsc.edu
Phone: (831) 459-4259

Upcoming Talks

Invited Speaker, BIRS-CMO Workshop, Oaxaca, Mexico, May 2018

DISK: Divide and Conquer Spatial Kriging for Massive Sea Surface Temperature Database -> Details

Invited Speaker, Banff International Research Station, December 2017

Inferential Challenges for Large Spatio-Temporal Data Structures -> Details

Invited Speaker, Eastern North Atlantic Regional Conference, Atlanta, March 2018

Scalable Bayesian regression framework for tensor valued objects -> Details