We are a systems science group that develop methodology to improve observation, inference, prediction, and discovery for Earth, Planets, Climate, and Life applications. Our approach emphasizes closed-loop Dynamic Data-Driven Systems Optimization, which informatively combines theory, data, experts, and analogs for solving problems associated with stochastic systems. Our work has lead to new approaches for improving key topics in data driven systems science, including estimation, control, learning, model reduction, uncertainty quantification, adaptive observation, detection, and decision making. ESSG is strongly interdisciplinary, comfortable developing both methodology and application. You can find us tinkering with "what works in the field" while stretching the 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. For example, here are a few who attended Z. Li's talk on his project work--
Left to Right: Margaret Trautner (18, Learning-Dynamics), Jacob Edison (16, Dynamics-UAS), Goran Zivanovic (6, Learning-Exoplanets), Aarti Dwivedi (12, Learning-Geophysics), Michael Barbehenn (visiting scientist, Ecology), Sai Ravela (EAPS), Ziwei Li (12, Learning-Atmosphere), Zhchang Zhan (12, Learning-Exoplanets) are present with our test aircraft. Photo by L. Hinkel.
ESSG is funded in part by the Office of Naval Research (ONR), the AFOSR Data-Driven Dynamic Application Systems (DDDAS) Program, the National Science Foundation (NSF), Lincoln Laboratory, Naval Undersea Warfare Center Division (NUWC), the Seaver Intitute, MIT's MISTI and ESI programs.
Here you will find the people, projects, and publications associated with our group. Enjoy!