Mohamed Rasheed-Hilmy Abdalmoaty

My research aims to develop systematic methods and tractable algorithms that are capable of delivering reliable data-driven decision and control systems of complex interconnected dynamical systems operating in uncertain environments. Of particular interest are cases where the full specification of the system's (model) structure or the use of an exact probabilistic setup is not feasible. In these realistic scenarios, developing theoretically justified robust and “optimal” methods is of of paramount importance for today's challenging applications.

My current research focuses on the following:

• Deep learning methods for state and parameter estimation in nonlinear dynamical models,

• Identification of large-scale stochastic nonlinear models,

• Analysis of simulated linear prediction error methods,

• Closed-loop identification methods for stochastic nonlinear models,

• Identification of errors-in-variables stochastic linear and nonlinear models,

among others.