Research group
Litty Mathew, 3rd year PhD student (joint with Prof Caroline Brophy and Prof. Ian Donohue, TCD).
Lorenzo Merli, 1st year PhD student.
Donald Zvada, 1st year PhD student.
Cecilia Manente, 3rd year visiting PhD student from Sapienza, University of Rome.
News
July 2025: I'll give a talk at the 39th International Workshop on Statistical Modelling.
June 2025: I'll take part in a Panel discussion in the Women in Data Science (WiDS) Dublin Regional Conference 2025.
February 2025: I was awarded funding under the Research Ireland Frontiers for the Future Programme to work on the development of novel statistical methods for the analysis of longitudinal dietary patterns and their association with health outcome.
Publications
2025+
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 (submitted).
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).
2024
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 (2024). 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.
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.