Modeling Uncertainty and Its Implications in Complex Interdependent Networks
Abstract
Our work is motivated by the need for “what-if” analysis in large complex interdependent and networked applications such as the critical infrastructure network (electric, water, gas grids). The research goal is to develop methodologies and algorithms to proactively model and reason about non-linear cascading risks to facilitate this analysis. Networked applications often operate under uncertainty in environmental response and the temporal state and action choices of the nodes are captured in the form of structured and unstructured text data as well as image data. We propose a network-centric approach that will contribute to advances in reasoning about uncertainty, large-scale text and image data analysis as well understanding of complex networks. This work is expected to lead to innovative extensions and train students to be experts in the following research areas: topic modeling and information extraction from text data; image extraction, identification of topological features for network analysis and studying the interactions between stakeholders at varying levels of the network. Specifically, we plan to study this problem in the context of Major Defense Acquisition Program (MDAP) network.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 09, 2016
- Source ID
- N002441510006
Entities
People
- Anita Raja
Organizations
- Cooper Union
- Defense Threat Reduction Agency