DMS Colloquium: Dr. Roberto Molinari

Speaker: Dr. Roberto Molinari (Auburn University)

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

Abstract: 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 I 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. To conclude, I will also briefly discuss other projects that do not fall within these three areas and then highlight the future directions we plan to take.