Dr. Mohamed Bahloul is currently an Assistant Professor in Electrical Engineering at Alfaisal University and a member of the IEEE's Engineering in Medicine & Biology Society and Biophysical Society. He holds a Ph.D. in Electrical and Computer Engineering from King Abdullah University of Science and Technology, KAUST-Saudi Arabia (2022). He also earned three Master of Science degrees in Microelectronic Systems (2015), Embedded systems (2016), and Electrical Engineering-Electro-physics (2017) from the University of Montpellier-France, University of Sfax-Tunisia, National School of Engineering of Sfax-Tunisia and KAUST-KSA respectively. His B.Sc. degree is in Emerging Technologies in Electronics from the National School of Engineering of Sfax-Tunisia, earned in 2015.
Dr. Mohamed's research interests span across disciplines, incorporating his background and expertise in control, microelectronics, and embedded computing systems and his present work in integrating multi-scale modeling and artificial intelligence techniques in the biological, biomedical, and behavioral sciences. These interests include building and deploying smart health monitoring systems that are responsive to the complex variability of biological signals. He draws on theories and methods from estimation and systems control theory, data analysis, and numerical modeling. His interdisciplinary research expertise is relevant to expedite the technology transfer to a wide range of real-world applications and industries. Real-life phenomena ranging from, e.g., physics, engineering, chemistry, transportation, and health care, are exhibiting an ever-increasing scale and complexity.
Moreover, due to the stringent non-invasiveness, physical and/or economic constraints, as well as the inability to do controlled experiments, many processes are inaccessible, leading one to adopt the model and data-based methods for their effective analysis and control. A common thread in Dr. Mohamed's research lies in solving complex science and engineering problems by combining different model classes: data-driven, physics, and optimization. He is strongly interested in the real hardware implementation of such developed solutions that integrate hybrid modeling frameworks.