Rebecca Morrison’s research focuses on probabilistic models of different types, including probabilistic graphical models to interpret and simplify large data sets, and Bayesian inference and reasoning to calibrate and validate predictive computational models. These topics are motivated by the goal to make reliable predictions of physical systems using computational models, physical constraints, and available data sets. Rebecca is also interested in dynamical systems and combinatorial game theory, and has worked on a number of applications at the intersection of physics-based and data-driven models, including combustion, epidemiology, satellites and reentry vehicles, and space weather.
