We develop methods to characterize, predict, observe and infer properties of natural systems using a symbiosis of theory, data and computation that heavily incorporates and contributes to Intelligent Systems methodology. Toward this end, we research the classics including nonlinear dynamics, stochastic processes, estimation and control, and the modern, including machine learning, and information and pattern theories. You will find that the several projects listed here emphasize the dynamic coupling between phenomenology and physics, simulation and observation, humans and algorithms, and data with models; we call this a Dynamic Data Driven Applications Systems paradigm.
ESSG is strongly interdisciplinary, comfortable developing both methodology and application. You can find us tinkering with "what works in the field," but we are a methodologically inclined group comfortable stretching contours of systems science. At our heart, we are motivated to bring systems engineering to investigations of the earth and environment.
Participants come from many pedagogical areas including EAPS, EECS, Mathematics, and Mechanical and Aerospace Engineering. ESSG is funded in part by the AFOSR Data-Driven Dynamic Application Systems (DDDAS) Program, the National Science Foundation (NSF), Lincoln Laboratory, Naval Undersea Warfare Center Division (NUWC), and MIT's MISTI program.
Here you will find the people, projects, and publications associated with our group. Enjoy!