Dr. Magdi Ragheb is an Associate Professor University of Illinois, USA. he earned his PhD in Nuclear Engineering/Computer Sciences from Univ. of Wisconsin, Madison, 1978. Dr. Magdi Ragheb has his expertise in Generalized Formulation of Monte Carlo Estimators over Terminating Markov Chains, Dissociating Gases for Fusion Reactors cooling, A new fusion fuel cycle: TT Fusion, Knowledge Engineering Tools for the conduct of Monte Carlo Shielding Calculations and the interpretation of shielding experiments, Implementation of Knowledge Engineering Methodologies on Supercomputers with application to Nuclear Reactor Safety Analysis, Computer Simulation of Particle Transport in Fast-Neutron Cancer Therapy. Consultation Aid on a Distributed System of Symbolic-Processing Machines and Supercomputers for faster-than-real-time nuclear reactors accidents monitoring and simulation, Decision Aid System for Emergency Preparedness based on Knowledge Engineering Concepts, Fuzzy Logic and Possibility Theory for machine learning using analogy and decision-making under uncertainty. Numerical Modelling of Thermal-Hydraulics and Reactor Transient Phenomena Coupled Probabilistic-Possibilistic Methodologies for Fault Diagnosis Engineering Anticipatory Systems Synthesis and Genetic Algorithms

Generalized Formulation of Monte Carlo Estimators over Terminating Markov Chains, Dissociating Gases for Fusion Reactors cooling, A new fusion fuel cycle: TT Fusion, Knowledge Engineering Tools for the conduct of Monte Carlo Shielding Calculations and the interpretation of shielding experiments, Implementation of Knowledge Engineering Methodologies on Supercomputers with application to Nuclear Reactor Safety Analysis, Computer Simulation of Particle Transport in Fast-Neutron Cancer Therapy. Consultation Aid on a Distributed System of Symbolic-Processing Machines and Supercomputers for faster-than-real-time nuclear reactors accidents monitoring and simulation, Decision Aid System for Emergency Preparedness based on Knowledge Engineering Concepts, Fuzzy Logic and Possibility Theory for machine learning using analogy and decision-making under uncertainty. Numerical Modelling of Thermal-Hydraulics and Reactor Transient Phenomena Coupled Probabilistic-Possibilistic Methodologies for Fault Diagnosis Engineering Anticipatory Systems Synthesis and Genetic Algorithms