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.
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 and Bayesian categorical regression.
Bayesian dimensionality reduction: probabilistic 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
- Lijoi, A., Prünster, I. and Rebaudo G. (2022). Flexible clustering via hidden hierarchical Dirichlet priors has been accepted for publication in Scandinavian Journal of Statistics.
- BNP13 (Puerto Varas, Oct 24 – 28, 2022)
- ISBA 2022 (Montreal, June 25th - July 1st):
- I will present the working paper Graph-aligned Random Partition Model (joint work with Peter Müller).