Mohammed Khouj |
Associate Professor, Electrical Engineering |
Dean of Engineering & Information Technology |
College of Engineering |
Major of Electrical Engineering - College of Engineering - D |
m.khouj@UBT.EDU.SA |
With over 25 years of industrial experience, several academic qualifications, and various personal skills development programs, Mohammed is keen to combine all these gained achievements for better community service.
Mohammed is a Ph.D. who is pursuing his research in favor of the University of Business and Technology, Jeddah, Saudi Arabia. His Ph.D. was conferred by the Department of Electrical and Computer Engineering at the University of British Columbia in 2014. Both the B.Sc. in Electrical Engineering and the M.Sc. in Industrial Engineering were obtained from King Abdul Aziz University back in 2001 and 2007. His research field is a real-time simulation of complex systems with a particular interest in resource allocation optimization within interdependent systems. He is currently studying and analyzing the modeled interconnected critical infrastructure systems in both study-state and transient-state. The research aims to optimize the system outcomes by properly reallocating the limited available resources using machine learning.
Specialties: Administration, Business Development, Project Management, Finance, Engineering, Programming, Sales, Marketing, and Research & Development
King Abdullah Foreign Scholarship Program (Saudi Arabia) in January 2009 by The Ministry of Education in Saudi.
- Engineering Management
- Industrial Management
- Operation Research
- Entrepreneurship
- Feasibility Analysis
This information will be updated soon !
Mohammed's principal field of research is a real-time simulation of complex systems with a special interest in resource allocation optimization within interdependent systems. He is currently studying and analyzing the behavior of the modeled interconnected critical infrastructure systems in both study-state and transient-state. The goal of the research is to optimize the system outcomes by properly reallocating the available limited resources using machine learning.
This information will be updated soon !
This information will be updated soon !
This information will be updated soon !
This information will be updated soon !
This information will be updated soon !