Utility Mobile

Network Systems Science and Advanced Computing

Projects

All Projects

The emergence of undervaccinated geographical clusters for diseases like measles has become a national concern. Several measles outbreaks have occurred in recent months, despite high Measles, Mumps, and Rubella (MMR) coverage in the United States (95%). Such undervaccinated clusters can act as reservoirs for infection that can transmit the disease to a broader population, magnifying their importance far beyond what their absolute numbers might indicate.
Past Project
This project brings together a systems science approach, combines agent-based stochastic epidemic models, and techniques from machine learning, high performance computing, data mining, and spatial statistics, along with novel public and private datasets on immunization and incidence, to develop a novel methodology for identifying critical undervaccinated clusters.
This project has led to the development of a broad class of highly scalable libraries for problems in multiple areas, including network science, computer vision, bioinformatics and climate science. Team members have contributed by developing scalable algorithms for network generation and subgraph detection, which have been applied to problems in public health.
This Expeditions project will enable novel implementations of global infectious disease computational epidemiology by advancing computational foundations, engineering principles, theoretical understanding, and novel technologies.
Past Project
This project provides a methodology and theory development that spans the information ecology, dis/mis-information, and human risk behavior in the context of COVID-19.  The primary methods use computational models of psychological and cognitive processes, dis/mis-information propagation detection, natural language processing techniques and agent-based modeling to provide a forecasting tool for exploring what-if scenarios and situational assessment.
Past Project
In this project, we are developing methods and tools to combine Earth Observations data, population models, and health data to create better estimates of vulnerability, using Hurricane Harvey as our case study.
Genomic and Proteomic Architecture of Atherosclerosis (GPAA) is a collaborative project which aims to comprehensively study the genomic and proteomic architecture of atherosclerosis and identify genes/proteins that are associated with the development of atherosclerosis. This work will provide novel insights into the pathogenesis of atherosclerosis and facilitate development of reliable methods for diagnostics and targeted therapeutic interventions.
This project incorporates social behavior into mathematical models of infectious disease transmission dynamics to help improve our understanding of the impact of different control and prevention strategies for pandemics and epidemics.
Timely forecasts and expert-guided scenario projections for infectious diseases are key to evidence-based decision-making and risk communication. The United States Centers for Disease Control and Prevention (CDC) has coordinated epidemic forecasting exercises through its Epidemic Prediction Initiative (EPI) since 2013-14. Such collaborative efforts have been critical for the COVID-19 response in the United States (US) and Europe.