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Research

We forecast social, health, and infrastructure outcomes by modeling complex scenarios

How can we best fight infectious disease outbreaks? When will a social media protest become a civil crisis? We investigate large-scale biological, social, and technological systems at play in the world. By simulating scenarios of unfolding situations, we provide insights to help decision-makers refine science-based responses. In the process, we produce high-tech tools and foundational knowledge that advance the science of biocomplexity.

Areas of Focus

We apply our computational modeling tools and combined expertise to tackle a broad swath of issues affecting human life – from improving disaster resiliency in city infrastructures to identifying previously unknown organisms in our environments and bodies.

We develop tools to distill vast medical and ecological data and share them with research scientists, healthcare professionals and policymakers.

By computing predictive models, we provide public-health policymakers with data and analysis to identify and respond to epidemics.

We explore the information content in structured sequence data to reveal how changes in one influence the time evolution of the other. This contributes unique insights into viral dynamics at the host population level.

We investigate the structure of large bio-networks such as long noncoding RNA molecules. This contributes to the understanding of cancer development.

We study the abstraction to shape of a biological system - using it as a mathematical language to measure its complexity of interaction and provide ways of decomposing it into more fundamental blocks. This contributes to the understanding of ncRNA and structure switching.

Tackling community issues such as economic mobility and improving access to food, housing, and jobs to advance community health and well-being.

Improving the well-being of citizens across the nation through the health of our national systems from food to defense to technology.

Assessing the use of non-survey data flows to supplement or enhance current development of Official Statistics.

Research Spotlight

Modeling for COVID-19

We've modeled several epidemics in the past 20 years, including H1N1 and Ebola. Today, collaborating with researchers from across the Institute and the University of Virginia, we're modeling scenarios to help governments and the global research community understand and mitigate the spread of COVID-19.

Student Opportunities

Computing for Global Challenges Program

From human health to infrastructure, our world is facing enormous threats. To understand them, we need and use data. This undergraduate program is a training ground for the next generation of data-driven researchers. Working in teams with peers and scientists from the Institute's Network Systems Science and Advanced Computing division, students learn a new way to address real-world issues while working on critical research projects.

All Projects

The National Resource for Mechanistic Modeling of Cellular Systems will offer deep insights into mechanisms by which cells function and the cellular basis of disease through its computational modeling, enabled by the Complex Pathway Simulator (COPASI) and Virtual Cell (VCell) technologies. Projects by our user base focused on cancer, neurological disorders, diabetes, kidney disease, cardiology, and aging, all utilize mathematical models developed through VCell and COPASI.
We aim to conduct transformative research on physical and social aspects of coastal vulnerability embedded in interdependent infrastructures by developing a highly integrated modeling framework. We establish the notion of decentralized resilience as a cascade-proofing mechanism in Critical Interdependent-infrastructure Systems and Processes (CRISP) to expand the landscape of thinking and planning resilience.
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.