Our simulation environments can represent and analyze extremely large, complex infrastructure networks, including telecommunications, transportation, power systems, and commodity markets.
Modern information technology can support evidence based policies using simulations to synthesize data. Synthetic data provide a natural representation of situations and hypothetical outcomes, suitable for use by policy-makers.
Recent advances in high performance computing have created entirely new opportunities to understand epidemics and devise new ways to control them. Our research in computational epidemiology aims to understand the spread of diseases and efficient strategies to mitigate their outbreak.
We are studying data from Twitter, blogs, and other social media to understand the spread of phenomena like social unrest, behavioral changes like smoking initiation and cessation, and theoretical questions like the role of network structure and information visibility in cascade propagation and emergent collective action.
Communities are looking for the ability to manage change in the face of crisis. To study resiliency, we demonstrate synthetic information techniques that allow unprecedented scaling and detail for behavioral, bio-social & socio-technical systems studies.
Our modeling frameworks and tools address problems in systems biology and medical and health informatics. We have made significant progress in developing high performance computing-based, highly resolved models for systems biology.