Title: "Bayesian Methods for Achieving a Sustainable Resilience of Infrastructure and Communities"

Abstract
The protection of critical infrastructure has recently garnered attention with an emphasis on analyzing the risk, improving the resilience, and planning for the sustainability of such networks. Critical infrastructure systems are essential to our economy and society, however, they frequently face disruptions leading to cascading failures across other systems. One challenge is the ability to make accurate predictions of post-disruption systems behavior that capture time and uncertainty dynamics. This talk will cover Bayesian methods developed to address challenges in risk-based predictive analytics. The methods integrate hierarchical Bayesian models with kernel functions to account for uncertainty, prior knowledge, and systems information. In addition, Bayesian updating of infrastructure network response under multiple hazard scenarios is discussed. Case studies to illustrate these methods include resilience modeling of power and water systems.

Bio
Hiba Baroud is an assistant professor in the Departments of Civil and Environmental Engineering and Earth and Environmental Sciences, and the Littlejohn Dean’s Faculty Fellow. Her work explores data and decision analytics to model the resilience and sustainability of critical infrastructure systems and communities. Her research applications are focused on smart cities as well as developing countries. Hiba’s prior experience includes a summer research with IBM at the Watson Research Center, a fellowship at George Washington University Center for International Business Education and Research, and a visiting position in the Department of Geography and Environmental Engineering at Johns Hopkins University.