Analytics research

The analytics lab at the University of Oklahoma is located in the School of Industrial Engineering and is actively pursuing research in various aspects of data science and analytics:

  • Complex resilient network systems
  • Novel predictive and classification modeling approaches and applications
  • Applied metaheuristics

The current primary thrust of the lab is in the domain of “community resilience” in which all three research areas complement each other synergistically.  In particular, we are focused on investigating approaches to help communities (e.g., cities, towns) withstand and recover rapidly from disasters.  We have partnered with a variety of experts from Civil Engineering, Economics, Social Science, and Computer Science to study and quantify how disruptions in the complex interdependent infrastructure systems that underpin modern society impact economic measures and social norms.  We are developing new mathematical models to capture the many levels of complexities in the system; we are studying how “big data” can help inform emergency response and recovery efforts; and working towards novel machine learning methods and applications to support resilience in general.

Funded Resilience research:

2015 – 2020 NIST Center of Excellence Grant Award

Total amount: $20 million

Research efforts are devoted to develop the NIST-Community Resilience Modeling Environment (NIST-CORE).  Working with collaborators from Colorado State University, Rice University, Texas A&M University, University of Illinois at Urbana-Champaign, Oregon State University, University of Washington, and others to develop a platform for community resilience modeling and optimization.

2015 – 2018 NSF-Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) Collaborative Research

Total amount: $2.2 million

Resilience Analytics: A Data-Driven Approach for Enhanced Interdependent Network Resilience

This collaborative research integrates multiple disciplinary perspectives in engineering, computer science, and social science to address how community-driven data can help (i) understand the behavior of these interdependent networks before, during, and after disruptions, and (ii) more effectively reduce their vulnerability to and enhance their recovery after a disruption.

Two research components comprise this effort in resilience analytics. The first component creates a network model of the interdependence of infrastructure networks, the community networks that they serve, and the service networks engaged to respond after a disruption.  The second component integrates the interdependent network model with community-sourced data to develop a framework of data analytics to better understand and plan for resilience. This component builds on research in the field of socio-technical systems relating to the analysis of social media data monitored after a disruption. This project aims at taking a significant step forward in our understanding of how real-time data from social media and other sources can describe, predict, and prescribe practices to manage interdependent networks in crises.