XClose

Statistical Science

Home
Menu

Dr Ioanna Manolopoulou

PositionAssociate Professor
Phone (external)+44 20 7679 5944
Phone (internal)45944
Email(*)i.manolopoulou
Personal webpagehttp://www.ucl.ac.uk/~ucakima/
ThemesComputational Statistics

* @ucl.ac.uk
 

Biographical Details

Dr Ioanna Manolopoulou…

Ioanna Manolopoulou is an Associate Professor at the Department of Statistical Science, UCL and a Turing Fellow of the Alan Turing Institute. She completed her PhD in Statistics in 2008 at the Statistical Laboratory, University of Cambridge. From 2008 to 2012 she was employed as a Visiting Assistant Professor and Postdoctoral Associate with Profs Mike West and Sayan Mukherjee, as well as a fellow of the Statistical and Applied Mathematical Sciences Institute (SAMSI).
 

Research Interests

Bayesian statistics, semi-parametric modelling, mixture modelling, state-space models, cell motility, diffusion models, phylogeography, point processes.

Selected publications

  • I. Manolopoulou, A. Hille and B.C. Emerson (2019) “BPEC: An R package for Bayesian Phylogeographic and Ecological Clustering”, accepted, Journal of Statistical Software. 
  • J. Pitkin, G. Ross and I. Manolopoulou (2018) “Dirichlet Process Mixture of Order Statistic Sequences with applications to Retail Analytics”, Journal of Statistical Society, Series C.
  • A. Heath, I. Manolopoulou and G. Baio (2017) “Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching”, Medical Decision Making.
  • A. Heath, I. Manolopoulou and G. Baio (2016) “Efficient High-Dimensional Gaussian Process Regression to calculate the Expected Value of Partial Perfect Information in Health Economic Evaluations”, Statistics in Medicine.
  • P.R. Hahn, J. Murray and I. Manolopoulou (2016) “Flexible prior specification for partially iden- tified nonlinear regressions with binary responses”, Journal of the American Statistical Association.
  • I. Manolopoulou, M.P. Matheu, M.D. Cahalan, M. West and T.B. Kepler (2012). Bayesian Spatio-Dynamic Modelling in Cell Motility Studies: Learning Nonlinear Taxic Fields Guiding the Immune Response. Journal of the American Statistical Association, with discussion.