Statistical Science


Dr Ioanna Manolopoulou

PositionProfessor of Statistical Science
Phone (external)+44 20 7679 5944
Phone (internal)45944
Personal webpagehttps://ioannamanolopoulou.github.io/
ThemesComputational Statistics

* @ucl.ac.uk


Ioanna Manolopoulou is a Professor of Statistical Science at the Department of Statistical Science, UCL as well as Associate Director of the HDRUK-Turing PhD programme. She is also affiliated to the recently-established UCL ELLIS unit.

Research Interests

Her main research interests lie in developing, extending or re-evaluating Bayesian models with a view to producing inferences which are useful and interpretable in practical applications. The focus is in flexible Bayesian modelling tools such as mixture models and tree models and applications of interest range from health data science to retail analytics.

Selected publications

  • A. Caron, G. Baio and I. Manolopoulou (2022), “Sparse Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation”, Journal of Computational and Graphical Statistics.
  • A. Caron, G. Baio and I. Manolopoulou (2022), “Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review”, Journal of the Royal Statistical Society, Series A
    Series A.
  • M. Vega, J. O’Sullivan, R. Prior, I. Manolopoulou and M. Musolesi (2022), “Posterior Summaries of Grocery Retail Topic Models: Evaluation, Interpretability and Credibility”, Journal
    of the Royal Statistical Society, Series C.
  • 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.