Welcome to my personal web page!

I am a postdoctoral researcher in Statistics at the Department of Statistics and Data Sciences at the University of Texas at Austin, under the mentorship of Peter Müller and Abhra Sarkar. I am also a member of the Bayes Lab at the Bocconi Institute for Data Science and Analytics (BIDSA) and of the Complex Data Modeling Research Network led by MiDas.

Previously, I was a Ph.D. student in Statistics at Bocconi University, under the supervision of Antonio Lijoi and Igor Prünster.

Research interests

I am interested in Bayesian modeling of complex data structures. More specifically I have been working on:

  • Bayesian nonparametrics: discrete random probabilities, mixture models, random partitions and probabilistic clustering.

  • Bayesian modeling for binary and categorical data: dynamic regression for binary time series, hidden Markov models, drift diffusion models and Bayesian categorical regression.

  • Probabilistic dimensionality reduction: Bayesian multidimensional scaling and individual difference scaling analysis.

  • Computational statistics: Monte Carlo, Markov chain Monte Carlo, sequential Monte Carlo methods and variational inference.

  • Applications: auditory neuroscience, genomics and proteomics.

e-mail: rebaudo.giovanni @ gmail.com

News and upcoming events

  • ISBA 2022 (Montreal, June 26 - July 1, 2022):
    • Augusto Fasano will present some works about Advances in Bayesian Inference for Binary and Categorical Data, including
      • Fasano, A., Rebaudo, G., Durante D. and Petrone S. (2021). A closed-form filter for binary time series. Statistics and Computing 31, 47 [DOI]
    • Beatrice Franzolini will present some works about Nonparametric priors for multi-sample data: dependence and borrowing of information, including
      • Franzolini, B., Lijoi, A., Prünster, I. and Rebaudo G. Multivariate Species Sampling Processes [working paper].
    • Antonio Lijoi will present some works about Discreteness and dependence: an effective interplay in Bayesian nonparametrics, including
      • Lijoi, A., Prünster, I. and Rebaudo G. (2022). Flexible clustering via hidden hierarchical Dirichlet priors. Scandinavian Journal of Statistics [in press] [DOI].
    • I will present the work Rebaudo, G. and Müller, P. Graph-aligned Random Partition Model.
  • BNP13 (Puerto Varas, Oct 24 - 28, 2022):
    • I will present the work Rebaudo, G. and Müller, P. Graph-aligned Random Partition Model.
  • IISA 2022 (Bengaluru, Dec 26 - 30, 2022):
    • I will present the work Ascolani, F., Lijoi, A., Rebaudo G. and Zanella G. Clustering consistency with Dirichlet process mixtures [arXiv]