Research

I am interested on Bayesian Analysis, especially on Bayesian Nonparametric models. My research is based on exploring innovative approaches in model-based clustering. I am currently working on:

• Bayesian clustering methods with high dimensional data;

• Multivariate time series with change point detection.

• Topic modelling techniques with distributions defined on the simplex.

Research projects

• A. Giampino, R. Ascari, S. Migliorati, A flexible mixed-membership model for community and enterotype detection for microbiome data. Computational Statistics and Data Analysis (2025) https://doi.org/10.1016/j.csda.2025.108181.
• A. Giampino, A. Canale, B. Nipoti, A Bayesian Model for Co-clustering Ordinal Data with Informative Missing Entries. Statistics and Computing, (2025) https://doi.org/10.1007/s11222-025-10703-w
• A. Giampino, B. Nipoti, M. Vannucci, M. Guindani, Local Level Dynamic Random Partition Models for Changepoint Detection. Accepted to Bayesian Analysis (2025) https://arxiv.org/abs/2407.20085

Books chapter & proceedings

• A. Giampino, R. Ascari, S. Migliorati.Microbiome enterotype detection via a latent variable allocation model, Methodological and Applied Statistics and Demography IV (SIS 2024), Springer (2025) https://doi.org/10.1007/978-3-031-64447-416
• A. Giampino, R. Ascari, S. Migliorati.A Flexible Generalization of the Latent Dirichlet Allocation, Data Analysis and Related Applications 4: New Approaches, Volume 12 (ASMDA 2023), Wiley, (2024) https://doi.org/10.1002/9781394316915.ch8
• R. Ascari, A. Giampino,A flexible topic model, 14th Scientific Meeting of the Classification and Data Analysis Group (CLADAG 2023), Pearson, (2023) https://iris.unive.it/retrieve/a72dffc6-b72f-4356-9798-d1a2aad2baf5/CLADAG-2023-Bertarelli.pdf

Currently research projects

• A. Giampino, R. Ascari, S. Migliorati, A flexible latent Dirichlet model for modelling taxa communities, (2025+)
• A. Giampino, S. Migliorati, M. Guindani, Temporal communities behaviour, (2025+)
• A. Giampino, S. Migliorati, J. Vandekerckhove, M. Guindani, A Bayesian Finite Mixture for Cognitive Data, (2025+)
• J. Zonca, A. Giampino, C. Reverberi, P. Cherubini, The advice gap: adaptive yet suboptimal advice integration (2025+), PsyArXiv https://doi.org/10.31234/osf.io/4qkjev1 Submitted to: Nature Communications

Book of Abstract

• A. Giampino, R. Ascari, S. Migliorati,A Flexible Latent Dirichlet Model for Modeling Taxa Communities, ISBN 9788899594244, CLADAG (Sep 2025)
• A. Giampino, A. Canale & B. Nipoti, Bayesian co-clustering of ordinal data with informative censoring, ISBN 978-9925-7812-8-7, CMStatistics (Dec 2024)
• A. Giampino, M. Guindani, B. Nipoti & M. Vannucci, Changepoint detection with random partition models, ISBN 978-9925-7812-7-0, CMStatistics (Dec 2023)
• R. Ascari & A. Giampino, A Flexible Topic Model, ISBN 9788891935632, CLADAG (Sep 2023)
• R. Ascari & Giampino A. A generalization of the latent Dirichlet allocation, ISBN 978-9925-7812-6-3, CMStatistics (Dec 2022)
• A. Giampino, R. Ascari & S. Migliorati LEFDA: An extension of the classical LDA, ISBN 978-90-73592-40-7, COMPSTAT (Aug 2022)

Awards

• BNP (Bayesin Nonparametrics) 2025: Travel award

• ISBA (International Society for Bayesian Analysis) 2024: Best poster

• Ph.D. Fellowship, Nov 2019 - Oct 2023: University of Milano - Bicocca, Milan (Italy)

• OBayes (Objective Bayes Methodology Conference) 2022: Travel award

• ISBA (International Society for Bayesian Analysis) 2022: Travel award

• SUS5 (Stats Under the Stars 5th edition) 2019: Winner of the statistical hackathon in 2019.