System Risk, Reliability, and Resilience assessment

Critical infrastructure systems (water supply, transportation systems, food production, oil and oil products production, security services and others)
are fundamental for the economic development and growth of any nation. Critical infrastructure system networks are facing with growing number of disruptions
due to their age, condition, natural disasters, human-made accidents, and interdependence with other infrastructures. The goal of this theme is the development
of tools and methodologies (e.g. modelling and simulation platforms) to model and quantify risk (e.g. infrastructure vulnerabilities and consequences) and
resilience (e.g. impact of natural disaster on infrastructure, outcomes of the impact and the effects of mitigation actions).

Asset Management and Maintenance

Failure of municipal infrastructures may cause crucial consequences such as significant health, social and economic impacts that critically affect public confidence.
For this, the utility authorities or stakeholders are more concerned to transform from reactive to preventive buried infrastructure management program. The goal of this
research to develop effective maintenance/ rehabilitation/ replacement (M/R/R) program for the municipal infrastructures focusing condition assessment, failure prediction,
vulnerability assessment, consequence assessment, life cycle costing (LCC), life cycle assessment (LCA), capacity optimization, revenue optimization, and project management.

Interdependent Network Resilience Analytics

The interdependencies that exist within and among critical infrastructure systems continue to grow in number and complicate the understanding of propagating
system responses and cascading effects. The purpose of this research is to develop a multi-dimensional framework for the interdependent and
complex critical infrastructures and systems.

Sustainable System Analytics

This research aims on the wide range of analysis of energy, industrial, infrastructure, and natural systems, investigating their functioning principals and their
environmental and economic impacts to provide a stronger basis for innovation, investment, and policy.

Data Driven Decision-Making

The goal of this research is to apply different data mining approaches like missing information, data imputation, data analysis, data fusion, value of
information, pattern recognition, big data analytics leading to applications in process and service industries. Solutions to practical problems and
enhancement of engineering education will be emphasized.