Pierfrancesco Alaimo Di Loro (2022-2023) - Coordinator
I am Pierfrancesco Alaimo Di Loro, and I am an Assistant Professor (RTDa) at LUMSA University. My studies have been focused on Bayesian modeling and Markov Chain Monte-Carlo methods, and during my PhD in "Sapienza" I developed a particular interest for the analysis of spatial and spatio-temporal phenomena. I visited Prof. Sudipto Banerjee at UCLA for 1 year, learning tricks and ropes of Bayesian Hierarchical Modeling for large geo-referenced data. Lately, I have been involved in the analysis and media communication of the Italian COVID-19 pandemic data, with emphasis on the interpretability, transparency and reliability of the statistical analyses. My current lines of research involve the modeling of phenomena characterized by complex dependence structures, whether the observations are organized at point-referenced locations or at nodes on a lattice.
Fabio Centofanti (2022-2023) - Secretary
I am a post-doc researcher at the University of Naples Federico II, where I also completed my PhD in Industrial Engineering (XXXIII cycle) in September 2021, under the supervision of Profs. Biagio Palumbo and Simone Vantini. My research interests regard statistical methodology for the analysis of complex data in the Industry 4.0 framework. I am currently working on functional data analysis methods for regression, clustering, and statistical process control, with a particular focus on robustness and interpretability. During my PhD, I was visiting student at the Department of Mathematics of the Politecnico di Milano and at the Department of Applied Mathematics and Computer Science of the Technical University of Denmark.
Filippo Ascolani (2023-2024)
I am a last-year PhD student in Statistics at Bocconi University, under the supervision of Antonio Lijoi and Igor Prünster. My research interests are focused especially on Bayesian nonparametric methodologies for complex data structures, both from a theoretical and methodological perspectives. I am also interested in theory of MCMC methods and their scalability in high dimensional problems. Moreover, I am a member of the BayesLab of the Bocconi Institute for Data Science and Analytics (BIDSA) and of the MIDAS Complex Data Modeling Research Network. I am also a teaching assistant for various Statistics and Probability courses. For additional information see my webpage!
Veronica Ballerini (2023-2024)
I am a postdoc at the Department of Statistics, Computer Science and Applications of the University of Florence, working with Fabrizia Mealli. In 2021 I obtained my Ph.D. in Economic Statistics at Sapienza University of Rome, supervised by Brunero Liseo. My research interests are varied, going from official statistics to causal inference; the common thread is the Bayesian approach. I develop methodologies for population size estimation via integration of multiple sources and in the presence of coverage errors. I am particularly devoted to the study of noncentral hypergeometric distributions for not-at-random missing data problems, which have many potential applications in economics and official statistics. On the causal side, I work on the formalization and implementation of models for causal analysis in randomized clinical trials with noncompliance, social experiments, and socioeconomic observational studies. You can find more info on my webpage!
Giorgia Zaccaria (2023-2024)
I am a Postdoctoral Research Fellow in the Department of Statistics and Quantitative Methods at University of Milano-Bicocca. In February 2022, I obtained my Ph.D. (XXXIV cycle) in Methodological Statistics at the Department of Statistical Sciences of Sapienza University of Rome. My research interests focus, mainly but not only, on model-based clustering and dimensionality reduction for modelling multidimensional phenomena. I am currently working also on the missing data problem in the context of mixture models with a specific covariance structure able to detect hierarchical relationships among variables. My post-doc is giving me the opportunity to approach the robust dimension reduction techniques and robust mixture models I would like to deepen in the near future. I am passionate about the methodological and computational aspects of novel approaches motivated by real-data problems. If you are interested, you can find more info about me on my webpage!