During my PhD, I am studying clustering methods in Bayesian nonparametric statistics.
During my Master’s studies, I focused primarily on statistics and probability theory, combining both theoretical and applied perspectives.
Among the most relevant courses, I attended Bayesian statistics, Stochastic modelling for statistical applications, Probability theory, and Database and algorithms.
My Master’s thesis, titled “Gibbs sampling methods for smoothing in Fleming–Viot driven hidden Markov models,” explored novel approaches to the computation of the smoothing distribution by introducing ad hoc weights that incorporate information from the underlying process.
Final grade: 110/110 cum laude.
During my Bachelor’s studies, I completed several courses in theoretical mathematics, gaining a solid foundation across different areas of the discipline.
My Bachelor’s thesis, titled “Tempo di primo passaggio del Moto Browniano integrato con estensioni”, analyzed the first-passage time of Brownian motion and integrated Brownian motion under different parameter settings.