Inferential Solutions for Stochastic Processes, Differential Privacy and Multi-Model Paradigms (and others)

The task of statistical inference, which includes the need for interpretation and uncertainty quantification, has become increasingly challenging due to the scale and complexity of data structures and types that are being collected. In this talk, Roberto Molinari will discuss three areas of methodological research that aim to address some of these challenges: (i) scalable inference for (composite) stochastic processes with complex data features (e.g. contamination, missing data); (ii) increased utility and exact inference for differentially private outputs which aim at protecting individual privacy; and (iii) multi-model inference and decision-making with a new paradigm for interpretation. Applications in engineering, medicine, biology, agriculture and social sciences, among others, will be used to highlight the practical need for these developments. To conclude, Roberto will also briefly discuss other projects that do not fall within these three areas, and highlight the future directions of his research.

The event, organized by Francesca Chiaromonte, professor at the Sant'Anna School and Penn State University, and scientific coordinator of the Department of Excellence L'EMbeDS, will also be accessible remotely at the following link.