# 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.

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

# 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

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).

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).

# Selected Papers

Guhaniyogi, R., Qamar, S. and Dunson, D.B. (2017). Bayesian Tensor Regression. Journal of Machine Learning Research, Accepted, 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.(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. (2017). Multivariate Bias Adjusted Tapered Predictive Process Models. Spatial Statistics, 21, 42-65 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. and Banerjee, S. (2017). Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets. Revision submitted

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

Belani, H.K., Sekar, P., Guhaniyogi, R., Abraham, A., Bohjanen, P.R. and Bohjanen, K. (2014). Human papillomavirus vaccine acceptance among young men in Bangalore, India. International Journal of Dermatology, 53, 486-491. full text