Prof Sujit K Sahu

          

   

Education

1990-1994 PhD, Department of Statistics, University of Connecticut.
1987-1989 Master of Statistics, Indian Statistical Insitute, Calcutta.
1984-1987 BSc in Statistics (Honours), Presidency College, University of Calcutta.

Employment

Since 2013 Professor of Statistics, Mathematics and S3RI
2004-2013 Senior Lecturer in Statistics, Mathematics and S3RI
1999-2003 Lecturer in Statistics, School of Mathematics, University of Southampton.
1997-1999 Lecturer in Statistics, School of Mathematics, Cardiff University.
1994-1996 Research Associate, Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics, Cambridge University.

Visiting positions

September 2011 Visiting Lecturer Department of Statistical Science, Duke University.
November 2011 Visiting Lecturer Mathematical Sciences Institute, Australian National University, Canberra.
Jul 2007-Jan 2008 Senior Research Associate, US Enviornmental Protection Agency.
Mar-Jul, 2003 Visiting fellow, SAMSI and Department of Statistical Science, Duke University.

Journal Editorships

Since 2019 Spatial Statistics
2015-2019 Biometrical Journal
2013-2015 Environmental and Ecological Statistics
2012-2014 Sankhya, the Indian Journal of Statistics
2008-2012 Journal of the American Statistical Association (Applications and Case Studies)
2008-2012 Journal of the Royal Statistical Society, Series C: Applied Statistics

External Examining

Date University Thesis Title
July 2020 Newcastle University Modelling Voxel Dependent Hemodynamic Response Function
May 2019 University of Sheffield Bayesian Inference for Dynamic Spatio-temporal Models
December 2018 University of Sheffield Spatio-temporal Models for high dimensional data
September 2017 Glasgow University Nonparametric statistical downscaling for the fusion of in-lake and remote sensing data
December 2016 Lancaster University Statistical Methods for Weather-related Insurance Claims.
May 2014 Open University Dynamic Bayesian Smooth Transition Autoregressive (DBSTAR) models for non-stationary nonlinear time series.
March 2014 Lancaster University Spatio-Temporal Modelling of Partially Observed Processes.
December 2012 University of Kent at Canterbury Contribution to the Bayesian Analysis of Mixture Models.
April 2011 Bath University Bayesian Spatio-temporal modelling of Air pollution.
March 2008 University of Manchester Markov Chain Monte Carlo Methods Applied to Integer-Valued Time Series.
December 2006 Imperial College Non-Stationary Spatial Statistics in the Geosciences.
July 2006 Lancaster University A Bayesian partitioning approach to modelling and mapping case control data.