Research group
Litty Mathew, 4th year PhD student (joint with Prof Caroline Brophy and Prof. Ian Donohue, TCD).
Lorenzo Merli, 2nd year PhD student.
Donald Zvada, 2nd year PhD student.
Cecilia Manente, 3rd year visiting PhD student from Sapienza, University of Rome.
News
December 2026: I'll give a talk at the CFE-CMStatistics 2026 conference (12th to 14th of December) in Berlin .
December 2026: I'll give a talk at the 3rd workshop on Bayesian Methods for the Social Science (BMSS-III) (9th to 11th of December) in Dublin .
July 2026: I'll visit Ass. Prof. Owen Ward at Simon Fraser University (26th to 30th of July) in Vancouver.
July 2026: I'll give a talk at the IFCS 2026 (14th to 16th of July) in Milan.
June 2026: I'll give a talk at the SIS-FENStatS 2026 CONFERENCE (22nd to 25th of June) in Rome.
May 2026 : The TCD Statistics group is organizing CASI 2026 (11th to 13th of May 2026).
Grants
T-DIET: developing novel statistical methods for the analysis of longitudinal dietary patterns and their association with health outcomes (PI) (211,299€). Funding Agency: Research Ireland (Frontiers for the Future Programme). Period: 2025 - 2029.
Preprints
2026 +
Bueno, I., Palma, G.R., D'Angelo, S., Moral, R.A. and Godoy, W.A.C. Repeated fungal challenges and their influence on social interactions in mini-colonies of leaf-cutting ants (in preparation).
D'Angelo, S., Fop, M. Data compression for fast dimension reduction and clustering of high-dimensional discrete data (submitted). Link.
Manente, C., Alfò, M. and D'Angelo, S. A Dynamic Latent Space Model for Healthcare Mobility Networks: the Italian National Health Service case (submitted). Link.
Journal articles (peer-reviewed)
2026
Mathew, L., Brophy, C., Donohue, I., Ross, S. RP-J and D'Angelo, S. A general hidden Markov model framework for capturing changes in species behaviour under disturbance from acoustic time series. Ecological Modelling (2026). Link.
2025
Casa, A., Fop, M. and D’Angelo, S. A discussion on “Sparse Bayesian factor analysis when the number of factors is unknown” by Frühwirth-Schnatter, Hosszejni and Freitas Lopes. Bayesian Analysis (2025). Link.
2023
D’Angelo, S., Alfò, M. and Fop, M. Model-based clustering for multidimensional social networks. Journal of the Royal Statistical Society Series A: Statistics in Society (2023). Link.
2022
Gormley, I.C., D'Angelo, S. and Brennan, L. Combining biomarker and food intake data. Wiley StatsRef: Statistics Reference Online (2022). Link.
2021
D’Angelo, S., Gormley, I.C., McNamara, A.E. and Brennan, L. multiMarker: software for modelling and prediction of continuous food intake using multiple biomarkers measurements. BMC bioinformatics (2021). Link.
D’Angelo, S., Brennan, L. and Gormley, I.C. Inferring food intake from multiple biomarkers using a latent variable model. The Annals of Applied Statistics (2021). Link.
2020
D'Angelo, S., Alfò, M. and Murphy, T.B. Modeling node heterogeneity in latent space models for multidimensional networks. Statistica Neerlandica (2020). Link.
2019
D'Angelo, S., Gormley, I.C., McNulty, B.A., Nugent, A.P., Walton, J., Flynn, A. and Brennan, L. Combining biomarker and food intake data: calibration equations for citrus intake. The American Journal of Clinical Nutrition (2019). Link.
D’Angelo, S., Murphy, T.B. and Alfò, M. Latent space modelling of multidimensional networks with application to the exchange of votes in Eurovision song contest. The Annals of Applied Statistics (2019). Link.
2018
Egidi, V., Salvatore, M.A., Rivellini, G. and D’Angelo, S. A network approach to studying cause-of-death interrelations. Demographic Research (2018). Link.
Crispino, M., D’Angelo, S., Ranciati, S. and Mira, A. Understanding Dependency Patterns in Structural and Functional Brain Connectivity Through fMRI and DTI Data. Studies in Neural Data Science: StartUp Research 2017, Siena, Italy, June 25-27 (2018). Link.
Conference papers & Proceedings (peer-reviewed)
2026
Manente, C., Alfò, M., D'Angelo, S. Modeling Healthcare Mobility in Italy: a Dynamic Latent Space Network Approach. Joint Meeting SIS-FENStatS 2026, Rome (IT), 22-25 June 2026.
D'Angelo, S., White, A., Faulkner, M. A bipartite network analysis of sound-spelling pairs distribution across a corpus of Old English texts. Joint Meeting SIS-FENStatS 2026, Rome (IT), 22-25 June 2026.
2025
Mathew, L., Brophy, C., Donohue, I., Ross, S.R.P.J., D'Angelo, S. Assessing Ecosystem Dynamics under Disturbance: A Hidden Markov Model Framework for Species Detection Data, Assessing Ecosystem Dynamics under Disturbance: A Hidden Markov Model Framework for Species Detection Data (Best Paper Award). 7th International Conference on Statistics: Theory and Applications (ICSTA'25), Paris (FR), 17-19 August 2025. Link.
D'Angelo, S. A latent variable approach to shift from discrete to continuous explanatory variables in experimental settings. 39th International Workshop on Statistical Modelling, Limerick (IE), 13-18 July 2025. Link.
2021
D’Angelo, S. Sender and receiver effects in latent space models for multiplex data. CLADAG 2021, Florence (IT), 9-11 September 2021. Link.
2019
D’Angelo, S., Fop, M. A latent space model for clustering in multiplex data. CLADAG 2019, Cassino (IT), 11-13 September 2019. Link.
Software
spaceNet: Latent Space Models for Multidimensional Networks (R package). The package fits latent space models for multivariate networks (multiplex) analysis, estimated via MCMC algorithms. Link.
multiMarker: Latent Variable Model to Infer Food Intake from Multiple Biomarkers (R package). The package fits a latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates inference on food intake from biomarker data alone, along with the associated uncertainty. Link.
multiMarker (web application). multiMarker is a web application that infers the relationship between biomarker and food quantity data from an intervention study, and allows prediction of food intake when only biomarker data are available. Further, the framework allows quantification of the uncertainty in intake predictions. Link.
Bio-Intake: Biomarker guided dietary intake (web application). Bio-Intake (Biomarker guided dietary intake) allows users to upload mean daily self-reported citrus intake data (g/day) (estimated from food diaries) and computes calibrated intakes (g/day) based on a biomarker calibration equation. The biomarker based calibration equation was developed from urinary proline betaine and mean daily intake (estimated from a 4 day food diary) and is used to correct self-report estimates for measurement error. Link.