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 at King Abdullah University of Science and Technology (KAUST), where he is supervised by Professors Taous‑Meriem Laleg‑Kirati and Eric Feron. His research integrates classical control theory with modern AI and machine‑learning techniques to create advanced control and estimation frameworks for complex process systems, particularly optimal feeding in aquaculture and bacteria monitoring in wastewater treatment plants.
Aljehani earned his M.S. in Electrical Engineering from KAUST (2019), developing control strategies for distributed solar collectors and a virtual sensor for solar‑irradiance estimation. He holds a B.S. in Electrical Engineering from University of Dayton (2016).
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