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 dynamical systems operating in uncertain environments. I am particularly interested in cases where the full specification of the system's 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 paramount importance for today's challenging applications.

My current research focuses on the following:

• Data-driven control methods,

• Identification of stochastic differential algebraic models (DAEs),

• Privacy and security of feedback control using system theory tools,

• Closed-loop and EIVs identification in stochastic linear and nonlinear models,

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

• Subsample delay estimation in the Laguerre domain.

among others.