Fahad Aljehani
Designing advanced control algorithms and estimation techniques to optimize water quality systems, specifically aquaculture and wastewater treatment, blending control theory with cutting-edge AI and machine learning to enhance performance, sustainability, and cost-efficiency.
Biography
Fahad Aljehani is a Ph.D. candidate in Electrical and Computer Engineering. His academic journey began at Dayton University in the USA, where he earned a Bachelor's degree in Electrical Engineering from 2012 to 2016. He then joined King Abdullah University of Science and Technology (KAUST) and completed his Master's degree in Electrical Engineering from 2017 to 2019. Currently, he is pursuing his Ph.D. at KAUST, focusing on the design of advanced control algorithms and estimation techniques to optimize process control systems.
Research Interests
Fahad's research focuses on designing advanced control algorithms and estimation techniques to optimize process control systems, specifically in aquaculture and wastewater treatment. He utilizes a multidisciplinary approach that combines classical control theory with cutting-edge artificial intelligence and machine learning methodologies to develop adaptive, efficient, and scalable models that enhance system performance, improve sustainability, and reduce operational costs.
Area of interests:
- Optimal control for a class of nonlinear systems
- Estimation and observer design methods in nonlinear systems
- Reinforcement learning and dynamics programming
- Computer vision
- Machine learning
Qualifications
Education
- Master of Science (M.S.)
- Electrical Engineering, King Abdullah University of Science and Technology, Saudi Arabia, 2019
- Bachelor of Science (B.S.)
- Electrical Engineering, Dayton University, United States, 2016
- English
- Native or bilingual proficiency
- Arabic
- Native or bilingual proficiency