Much of my research lies at the intersection of methodological statistics, machine learning, and applied statistics. Data-related problems sparking my curiosity are constant sources of fresh ideas for my research. I am particularly passionate about scientific problems and working in collaborative and interdisciplinary projects where data-driven research demands the development of novel data analysis methods. I am always open to explore new fields and initiate new collaborations.

More specifically, my research interests center on latent variable models in application to high-dimensional and complex data. Within this area, my work primarily focuses on model-based clustering and classification, statistical network analysis, variable selection, and dimension reduction.

Some of my current projects involve collaboration networks, Bayesian optimization, clustering objects, analysis of computer experiments, and epidemiological data modeling.

Current PhD students

  • Felice Lamberti — visiting from Univ. of Bologna, co-supervision with Assoc. Prof. Saverio Ranciati.
    Probabilistic tensor decomposition for matrix-variate data.
  • Adam Kilroy — co-supervision with Prof. Aisling Ní Annaidh.
    Statistical and machine learning methods to predict skin tension and growth.
  • Jincheng Luo — co-supervision with Dr Miriam Casey.
    Statistical and mechanistic models for the transmission of bovine tuberculosis.
  • Niyati Seth.
    Advances in Bayesian optimization for combinatorial problems.

Past PhD students

  • Sara Geremia — visiting from Univ. of Trieste, co-supervision with Prof. Domenico de Stefano.
    Methods for the analysis of complex group formation mechanisms in attributed networks (2026).
  • CJ Clarke.
    Bayesian latent variable models for collections of networks (2026).
  • Ganesh Babu — co-supervision with Prof. Claire Gormley.
    Model-based clustering and classification methods for high-dimensional data with spatial and temporal dependence (2025).
  • Iuliia Promskaia — co-supervision with Prof. Adrian O’Hagan.
    Model-based clustering methods for networks with edge weights and node features (2025).
  • Matt Nagle — co-supervision with Prof. Aisling Ní Annaidh.
    Improving reconstructive surgery: Machine learning approaches for biomechanical skin assessment (2025).
  • Thais Pacheco Menezes — co-supervision with Prof. Brendan Murphy.
    Record linkage approaches for matching databases with nested records (2025).
  • Noemi Corsini — visiting from University of Padova, co-supervision with Prof. Giovanna Menardi.
    Advances in density-based clustering for complex data (2025).
  • Xian-Yao Gwee — co-supervision with Prof. Claire Gormley.
    Bayesian nonparametric models for network data (2024).