Veronica Ballerini (2023-2024) - Coordinator
I am a postdoc at the Department of Statistics, Computer Science and Applications "G. Parenti" of the University of Florence, working on the project "Bayesian Methods for Clinical and Observational Studies". My research interests go from official statistics to causal inference. Since my PhD (under the supervision of Brunero Liseo), I have developed 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 almost infinite 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, with a causal team led by Fabrizia Mealli. You can find more info on my webpage!
Filippo Ascolani (2023-2024) - Secretary
I am a Lecturer at the Department of Statistical Science at Duke University, while I received my PhD from 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 "de Castro" Statistics Initiative at Collegio Carlo Alberto. For additional information see my webpage!
Luca Aiello (2024-2025)
I am Luca Aiello, currently in the final year of my Ph.D. (XXXVI cycle) in Statistics within the ECOSTAT Ph.D. program at the University of Milano Bicocca. Throughout the course of my doctoral studies, I've had the opportunity of collaborating with several people. Among them there are my supervisor, Lucia Paci, my co-supervisor, Raffaele Argiento, and Sudipto Banerjee, who hosted me during a visiting period at UCLA in the previous academic year. My research focus is centered on Bayesian spatio-temporal models and methods, with a keen interest in environmental statistics and Markov Chain Monte Carlo (MCMC) techniques. Over the years, I've also done teaching assistancies in basic statistics courses at Politecnico di Milano, Università Cattolica del Sacro Cuore di Milano, and the University of Bergamo.
Marco Mingione (2024-2025)
I am Marco Mingione, and I am an Assistant Professor (RTD-A) at Roma Tre University. My academic journey kicked off at La Sapienza and it was followed by 1 year of research at the Institute of Applied Computing "M. Picone" (IAC-CNR), before starting my Ph.D. in 2018. During my postgraduate training, I developed a keen appreciation for Bayesian hierarchical modeling, further stimulated by visiting Prof. Sudipto Banerjee for 6 months at UCLA (Los Angeles). Lately, my research work has been focused on Hidden Semi-Markov models and Spatio-temporal Hawkes processes. Regardless of the field of application, my primary interest is to find highly interpretable solutions to practical problems with social implications, without neglecting the computational burden. This goes alongside the vital role that the statistician plays today in improving public awareness of science, and I wish our young community could be the one driving toward that goal.
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!