Joyee Ghosh

Associate Professor of Statistics and Actuarial Science, College of Liberal Arts and Sciences

Contact Information

Primary Office: 372 Schaeffer Hall (SH)
The University of Iowa
Iowa City, IA 52242


I am an Associate Professor (with tenure) in the Department of Statistics and Actuarial Science at The University of Iowa since July 2016. I grew up in Kolkata, India, and did my undergraduate studies at St. Xavier's College, Kolkata from 2000 to 2003. After graduating with a B.Sc. in Statistics, in 2003, I completed the first year of a two-years Masters program in Calcutta University. I joined the Department of Statistical Science at Duke University as a graduate student in 2004. I obtained my M.S. and Ph.D. from Duke University in 2006 and 2008, and then did a two year postdoctoral fellowship in the Department of Biostatistics at UNC Chapel Hill. I joined The University of Iowa as an Assistant Professor in August 2010.


  • PhD in Statistics, Duke University, Durham, North Carolina, United States, 2008
  • MS in Statistics, Duke University, Durham, North Carolina, United States, 2006
  • BSc in Statistics, St. Xavier's College, Kolkata, India, 2003

Areas of Research Interest

  • Bayesian statistics
  • Computational statistics

Selected Professional Memberships

  • American Statistical Association
  • International Indian Statistical Association
  • International Society for Bayesian Analysis

Selected Awards and Honors

  • Special Invited Speaker for the session “Young Researchers – Special Invited Speakers" (featured session with 45 minutes talks), 2014 International Indian Statistical Association, Riverside, California, 2014
  • UNC Chapel Hill 2009-2010 Postdoctoral Scholar Award for Research Excellence, University of North Carolina, 2009
  • Winner, student paper competition organized by Section on Bayesian Statistical Science (SBSS) of American Statistical Association (ASA), 2008

Selected Publications

  • Ghosh, J. (In Press). Cauchy and other shrinkage priors for logistic regression in the presence of separation. Wiley Interdisciplinary Reviews: Computational Statistics.
  • Ghosh, J., Li, Y. & Mitra, R. (2018). On the use of Cauchy prior distributions for Bayesian logistic regression. Bayesian Analysis, 13(2), 359-383.
  • Villarini, G., Luitel, B., Vecchi, G. D. & Ghosh, J. (In Press). Multi-model ensemble forecasting of North Atlantic tropical cyclone activity. Climate Dynamics.
  • Ghosh, J., Ghattas, A. E. (2015). Bayesian variable selection under collinearity. The American Statistician, 69(3), 165-173. DOI: 10.1080/00031305.2015.1031827.
  • Ghosh, J., Reiter, J. P. (2013). Secure Bayesian model averaging for horizontallypartitioned data. Statistics and Computing, 23(3), 311-322. DOI: 10.1007/s11222-011-9312-6.
  • Clyde, M. A., Ghosh, J. (2012). Finite population estimators in stochastic search variable selection. Biometrika, 99(4), 981-988. DOI: 10.1093/biomet/ass040.
  • Clyde, M. A., Ghosh, J. & Littman, M. (2011). Bayesian adaptive sampling for variable selection and model averaging. Journal of Computational and Graphical Statistics, 20(1), 80-101. DOI: 10.1198/jcgs.2010.09049.
  • Ghosh, J., Herring, A. H. & Siega-Riz, A. (2011). Bayesian variable selection for latent class models. Biometrics, 67, 917-925. DOI: 10.1111/j.1541-0420.2010.01502.x.
  • Ghosh, J., Clyde, M. A. (2011). Rao-Blackwellization for Bayesian variable selection and model averaging in linear and binary regression: A novel data augmentation approach. Journal of the American Statistical Association, 106(495), 1041-1052. DOI: 10.1198/jasa.2011.tm10518.
  • Ghosh, J., Dunson, D. B. (2009). Default prior distributions and efficient posterior computation in Bayesian factor analysis. Journal of Computational and Graphical Statistics, 18(2), 306-320. DOI: 10.1198/jcgs.2009.07145.

Selected Grants and Contracts

  • Ghosh, JoyeeGrant Efficient Bayesian Model Averaging for Linear and Generalized Linear Models. Sponsored by National Security Agency Young Investigator Grant. March 1, 2014 - March 31, 2016.
  • Ghosh, JoyeeGrant Scaling up Bayesian Variable Selection for High Dimensions. Sponsored by National Science Foundation DMS Statistics. August 1, 2016 - July 31, 2019.
Last Modified Date: March 23, 2020