Increasing recurrence and disruptive forces of extreme events such as climatic changes, natural disasters, and pandemics reveal the extent of vulnerability of our communities and built environment. Our OASIS research team focuses on quantitative modeling of complex systems and their interactions with the communities, leveraging advanced machine learning algorithms and robust optimization techniques. Specifically, our goal is to examine systemic/stochastic impacts of various chronic/acute shocks on our socio-technical systems, develop risk-informed decision models, and investigate cost-effective adaptation measures to advance resilience and sustainability of our communities and infrastructure systems. Our research program is highly multidisciplinary and we are working on several projects in the areas of healthcare, energy systems and built environment, extreme events such as climatic changes, natural disasters, pandemics. 

Critical Infrastructure Systems - Risk & Resilience Modeling

Resilient physical and social systems must be robust, redundant, resourceful, and capable of rapid response. The concept of a “lifeline system” was developed to evaluate the performance of large, geographically distributed networks during earthquakes, hurricanes, and other hazardous natural events. Lifelines are grouped into six principal systems: electric power, gas and liquid fuels, telecommunications, transportation, waste disposal, and water supply. Taken individually, or in the aggregate, all of these systems are intimately linked with the economic well-being, security, and social fabric of the communities they serve. Lifeline systems are interdependent, primarily by virtue of physical proximity and operational interaction. Accurate vulnerability assessment of such lifeline systems is cardinal to enhance infrastructure resilience. Our team focuses on addressing some of the pressing challenges that exist in the domain of infrastructure resilience and vulnerability using mixed-method approaches such as both simulation-based and data-driven approaches, Bayesian network analysis, multi-stage risk assessment and predictive analytics. 

PROJECTS: 1) Vulnerability assessment of interdependent critical infrastructure systems under incomplete information using a simulation-based hybrid generalized approach; 2) Predicting sewer pipe failure risk in a region and identifying the key factors that increases the probability of such failures

Energy Systems: Climate Resilience Modeling​

The U.S. energy system is increasingly vulnerable to the adverse impacts of climatic events characterized by climate patterns shift such as hot and humid summers, warmer and temperate winters, shifts in precipitation patterns and more extreme climatic shocks such as hurricanes, droughts/heatwaves or winter snowstorms. Since energy infrastructure is the cornerstone of our society, ensuring its resilience in face of such extreme events is of utmost importance. We focus in investigating the various risk factors and their impacts on the energy sector leveraging various cutting edge statistical and machine learning models. More specifically, we not only aim to model the impact of various externalities on the energy sector–including electricity, natural gas, renewable energy, etc.—but also quantify the uncertainties using probabilistic approaches. Our models help the stakeholders in informed-decision making and policy analysis related to adequate resource planning and allocation, capacity expansion of the energy infrastructure system, both utility demand- and supply- side management, among others.

PROJECTS: 1) Develop a multiparadigm framework to capture the climate-energy nexus under deep uncertainty and implement the climate change impact assessments for the energy sector at various regional levels in the U.S.; 2) Modeling the risk of natural disasters’ impacting the energy systems; 3) Develop a risk-informed decision framework to help the utility regulators in resilience investment decisions of the electric grid.

Modeling Impact of Extreme Weather Events on Socio-technical systems

Extreme events including climatic change, natural disasters and health hazards such as the recent pandemic is affecting the life of people in far-reaching ways across regions, impacting communities and a multitude of infrastructure sectors such as energy, public health, ecosystems, water supply among others. Climate change manifested in terms of different types of natural disaster events such as wildfires and droughts due to prolonged extreme heat and heatwaves, seas-level rise combined with higher frequency and intensities of coastal storms, flooding, decreased air quality, etc. These events have immensely affected health and wellbeing of human and other living creatures on this earth. Our team focuses on investigating the impact of various extreme events on human health, infrastructure systems and communities to propose intervention strategies that would minimize the climate-induced impacts and enhance climate-resilience of the natural- and built-environment, as well as our communities.

PROJECTS: 1) Predicting impact of geophysical and anthropogenic factors on wildfire spread; 2) Understanding wildfire-induced failure risk of interconnected infrastructure systems; 3) Develop a risk-informed decision framework to minimize wildfire-induced power outage risks; 4) Assessing hazard-induced service inoperability in electricity sector; 5) Modeling impact of natural hazard induced disasters in country-level economic growth.

Population Health & Healthcare Analytics​

The growing interest in big data and predictive analytics for improving the healthcare system is reflected by a surge in long-term investment in developing new technologies using artificial intelligence and machine learning to forecast future events (possibly in real time) to improve the health of individuals and/or performance of the healthcare system. Big data analytics and predictive modeling in healthcare is evolving into a promising field for providing insight from large data sets and improving outcomes while reducing costs. In the era of data evolution, when there is an increasing availability of big data along with a growing effort from scientific communities to improve data sharing, the importance of leveraging advanced statistical and machine learning algorithms to uncover trends, patterns and make predictions for different perspectives of the healthcare system considering deep uncertainties of the future cannot be overstated. We focus on developing data-centric models to address some of the pressing multifaceted challenges in the healthcare discipline including, but not limited to, modeling the mortality and morbidity risks in a region, new approaches in disease spread simulation, formulating mental health risk, etc. Outcomes of such research studies can be used for informed decision making to advance and reform health policies, resource allocations, and others.

PROJECTS: 1) Understanding complex interactions between health behaviors and adolescent suicide attempts; 2) Develop predictive analytics framework to model the community health and built environment nexus; 3) Evaluating socio-environmental factors associated with suicide disparities across metropolitan areas

Investigating community safety and social justice issues​

Social disorganization theory proposed by Shaw & McKay (1929) is a common explanation for criminal activities in a society. Although the national crime statistics provide important information about the overall crime trends, they fail to provide insights about the geographical distribution of such crime rates. However, understanding the geographical, socioeconomic and environmental contexts of a crime setting is of utmost importance in order to make informed and effective judicial decisions. The overarching objective of these projects are to develop a strategic decision support system to help in efficient criminal justice response and adequate allocation and distribution of resources in terms of community policing, patrolling, etc. Unlike the previous approaches, in this work data-driven predictive models for various types of crime rates and crime costs across rural and urban areas are being developed leveraging non-linear statistical learning techniques and optimization models.

PROJECTS: 1) Multifaceted Risk Assessment Approach Using Statistical Learning to Evaluate Socio-environmental Factors Associated with Regional Felony and Misdemeanor Rates; 2) Identifying and Analyzing the Spatiotemporal Attack Patterns of Major Terrorist Organizations; 3) Assessing Crime Risk on Women in Developing Countries