We are a systems science group advancing methodology for modeling, observation, and inference to improve prediction and discovery in Earth, Planet, Climate, and Life applications. Our approach exploits feedbacks between theory, data, experts, and analogs for informative solutions to nonlinear high-dimensional problems in stochastic system settings, which includes estimation, control, learning, model reduction, uncertainty quantification, adaptive observation, detection, and decision-making. Some examples of our work include new statistical-physical approaches to model natural hazard risk in a changing climate, building autonomous stratospheric observatories to study extreme events, and using machine learning to discover governing fluid dynamical equations.
ESSG is strongly interdisciplinary and comfortable developing both methodology and application. You can find us tinkering with "what works in the field" while stretching the methodological contours of systems science. At our heart, we are motivated to bring systems science to investigations of the earth and environment.
Participants come from many pedagogical areas, including EAPS, EECS, Mathematics, and Mechanical and Aerospace Engineering.