• Franzolini, B., De Iorio, M., and Eriksson, J. (2024+). Conditional partial exchangeability: a probabilistic framework for multi-view clustering. arXiv:2307.01152 [pdf] [code] [Video]
• De Iorio, M., Cabral, R., Franzolini, B., Heinecke A. (2024+). Singularities in Bayesian inference: crucial or overstated?
• Anceschi, N., Fasano, A., Franzolini, B., Rebaudo, G. (2024+). Scalable expectation propagation for generalized linear models. arxiv:2407.02128

• Cremaschi, A., van den Boom, W., Ng, N.B.H., Franzolini, B., Tan, K.B., Yen, J.C.K., Tan, K.H., Chong, Y.-S., Eriksson, J.G., De Iorio, M. (2025). Post-partum screening for type 2 diabetes in women with a history of gestational diabetes mellitus: A cost-effectiveness analysis in Singapore. Value in Health Regional Issues, 45, 101048. DOI:10.1016/j.vhri.2024.101048 [link]
• Ascolani, F., Franzolini, B., Lijoi, A., and Prünster, I. (2024). Nonparametric priors with full-range borrowing of information. Biometrika, 111(3), 945–969. DOI:10.1093/biomet/asad063 [pdf] [code]
• Franzolini, B. and Rebaudo, G. (2024). Entropy regularization in probabilistic clustering. Statistical Methods & Applications, 33(1), 37–60. DOI:10.1007/s10260-023-00716-y [pdf] [code]
• Franzolini, B., Beskos, A., De Iorio, M., Poklewski Koziell, W., and Grzeszkiewicz, K. (2024). Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market. The Annals of Applied Statistics, 18(1), 555-584. DOI:10.1214/23-AOAS1801 [pdf] [code]
• Franzolini, B., Cremaschi, A., van den Boom, W., and De Iorio, M. (2023). Bayesian clustering of multiple zero-inflated outcomes. Philosophical Transactions of the Royal Society A, 381(2247): 20220145. DOI:10.1098/rsta.2022.0145 [pdf] [code]
• Franzolini, B., Lijoi, A., and Prünster, I. (2023). Model selection for maternal hypertensive disorders with symmetric hierarchical Dirichlet processes. The Annals of Applied Statistics 17(1): 313-332. DOI:10.1214/22-AOAS1628 [pdf] [code]

• Catalano, M., Franzolini, B., Giordano, M., Rebaudo, G. (2024) A discussion on: “Sparse Bayesian factor analysis when the number of factors is unknown” by Frühwirth-Schnatter, S., Hosszejni, D., Freitas Lopes, H. Bayesian Analysis, 18(1): 287-366. DOI: 10.1214/24-BA1423 [pdf]
• Franzolini, B. (2024). How to leverage Bayesian mixtures for dynamic clustering and classification In Book of Short Papers SIS 2024, in press. [pdf] [code]
• Bondi, L., Franzolini, B., Palma, M. (2024). A longitudinal study of the gender gap in school grades via flexible Bayesian Beta regression In Book of Short Papers SIS 2024, in press. [pdf]
• Franzolini, B., Bondi, L., Fasano, A., and Rebaudo, G. (2023). Bayesian forecasting of multivariate longitudinal zero-inflated counts: an application to civil conflict. In Book of Short Papers CLADAG 2023, 465-468. (Editors: Corretto, P., Giordano G., La Rocca, M., Parrella, M. L., Rampichini, C.) ISBN 9788891935632 [book] [pdf]
• Fasano, A., Anceschi, N., Franzolini, B., and Rebaudo, G. (2023). Efficient computation of predictive probabilities in probit models via expectation propagation. In Book of Short Papers CLADAG 2023, 449-452. (Editors: Corretto, P., Giordano G., La Rocca, M., Parrella, M. L., Rampichini, C.) ISBN 9788891935632 [book] [pdf] [code]
• Fasano, A., Anceschi, N., Franzolini, B., and Rebaudo, G. (2023). Efficient expectation propagation for posterior approximation in high-dimensional probit models. In Book of Short Papers SIS 2023, 1133-1138. (Editors: Chelli, F. M., Ciommi, M., Mariani, F., Recchioni, M. C.) ISBN 9788891935618 [book] [pdf] [code]
• Rebaudo G., Fasano, A., Franzolini, B., and Müller, P. (2023) A discussion on: “Evaluating sensitivity to the stick-breaking prior in Bayesian nonparametrics” by Giordano, R., Liu, R., Jordan M. I. and Broderick T. Bayesian Analysis, 18(1): 287-366. DOI: 10.1214/22-BA1309 [pdf]
• Franzolini, B. and Rebaudo, G. (2022). A regularized-entropy estimator to enhance cluster interpretability in Bayesian nonparametrics. In Book of Short Papers SIS 2022, 387-397. (Editors: Balzanella, A., Bini, M., Cavicchia, C. and Verde, R.) ISBN 9788891932310 [book] [pdf]
• Ascolani, F., Franzolini, B., Lijoi, A., and Prünster, I. (2021). On the dependence structure in Bayesian nonparametric priors. In Book of Short Papers SIS 2021, 1219-1225. (Editors: Perna, C., Salvati, N. and Schirripa Spagnolo, F.) ISBN 9788891927361 [book-part1] [book-part2] [pdf]

• Franzolini, B. (Advisors: Lijoi, A., and Prünster, I.), Feb 2022. On Dependent Processes in Bayesian Nonparametrics: Theory, Methods, and Applications. Bocconi University [pdf - high quality] [pdf - compressed]

A full pdf version of my CV is available here.
I am a Researcher (Italian RTD-A) at the Bocconi Institute for Data Science and Analytics , at Bocconi University, Italy.

My research interests are in Bayesian Nonparametrics, Probabilistic Machine Learning, Applied Bayesian Modelling and Computational Statistics with a focus on
dependent random measures,
clustering, random partition models and network data
change-point detection and dynamic models,
biostatistics, behavioural and financial econometrics applications.

I am the Chair of the Junior Section of ISBA (j-ISBA) .

Until July 2023, I was a Research Fellow at the Agency for Science, Technology and Research (A*STAR) and member of the Division of Biomedical Data Science (BiDS), led by Prof. Maria De Iorio, at the National University of Singapore (NUS), in Singapore. Until January 2022, I was a PhD student in Statistics at Bocconi University, Italy, under the supervision of Prof. Antonio Lijoi and Prof. Igor Prünster.