Abdulilah Mayet | Neuropharmacology | Excellence in Research

Abdulilah Mayet | Neuropharmacology | Excellence in Research

Assoc. Prof. Dr. Abdulilah Mayet, King Khalid University, Saudi Arabia

Assoc. Prof. Dr. Abdulilah Mohammad Mayet is an accomplished expert in Electrical Engineering, currently serving at King Khalid University πŸ‡ΈπŸ‡¦. With a Ph.D. from KAUST πŸŽ“, he has pioneered research in MEMS/NEMS, nanofabrication, and AI-integrated sensor systems πŸ€–. A prolific inventor with 8️⃣ patents and 28+ publications πŸ“š, Dr. Mayet has collaborated with global institutions including UC Irvine πŸ‡ΊπŸ‡Έ and Cornell University. His leadership spans academia, industry, and innovation πŸš€, notably as CEO of Qimam Abha Company and a Fulbright Scholar. Fluent in Arabic and English πŸ—£οΈ, he inspires innovation across engineering and technology frontiers 🌍.

Publication Profile

Orcid

Education

Assoc. Prof. Dr. Abdulilah Mayet holds a distinguished academic background in Electrical and Electronics Engineering ⚑. He earned his Ph.D. in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) in April 2016 πŸ“…, following his M.Sc. in Electrical Engineering from the same institution in June 2011 🧠. His academic journey began with a B.Sc. in Electrical and Electronics Engineering from King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia in June 1991 🏫. This strong educational foundation has powered his cutting-edge contributions to nanotechnology, MEMS/NEMS, and semiconductor innovation πŸ”¬πŸš€.

Experience

Assoc. Prof. Dr. Abdulilah Mayet brings over 25 years of pioneering experience in nanotechnology, MEMS/NEMS, and VLSI design πŸ’‘. Currently, he serves as a Supervisor of the VLSI Design Group and a Visiting Professor at UC Irvine, while also being a Fulbright Scholar Fellow πŸ‡ΊπŸ‡Έ. As an Associate Professor at King Khalid University, he leads initiatives in nanofabrication, AI-driven FPGA teaching, and research commercialization πŸ§ͺ. Formerly CTO at SEMC, he managed a 4,000mΒ² fab facility. His career spans innovations in NEM switches, amorphous metals, and MEMS platforms, with leadership roles at the Misk 2030 Leaders Program and Saudi Leadership Society πŸŒπŸ”¬.

Awards

Assoc. Prof. Dr. Abdulilah Mayet has made remarkable contributions to science and engineering, notably by independently conceptualizing and demonstrating the first-ever fully amorphous metal fabricated in a CMOS fab πŸ§ͺπŸ”¬. He has published 28 journal papers (24 as first/corresponding author) and 7 conference papers πŸ“. His research has attracted over SAR 26 million in grants, supporting startups and lab establishments πŸ’°. With 8 intellectual properties registered, he continues pushing innovation πŸš€. Dr. Mayet has taught a wide range of advanced courses and mentored MSc students πŸŽ“, while actively shaping curriculum development at both undergraduate and graduate levels πŸ“–πŸ‘¨β€πŸ«.

Research Focus

Assoc. Prof. Dr. Abdulilah Mayet focuses on cutting-edge research in intelligent measurement systems, non-destructive testing (NDT), and sensor-based technologies within the realm of electrical and electronic engineering ⚑. His work integrates artificial intelligence πŸ€–, machine learning, and ANNs for enhanced precision in multiphase flow analysis, pipeline diagnostics, and material characterization. He also explores nanoelectronics, MEMS/NEMS, and gamma-ray-based detection systems for industrial and biomedical applications 🧠🏭. His multidisciplinary approach bridges engineering with sustainability, fluid mechanics, and healthcare innovation, making his contributions vital to the energy, oil & gas, and emerging technologies sectors πŸŒπŸ’‘.

Publication Top Notes

Multiphase Flow’s Volume Fractions Intelligent Measurement by a Compound Method Employing Cesium-137, Photon Attenuation Sensor, and Capacitance-Based Sensor

Combination of a Nondestructive Testing Method with Artificial Neural Network for Determining Thickness of Aluminum Sheets Regardless of Alloy’s Type

Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness

An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/UWOC System

Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN

Proposing a Method Based on Artificial Neural Network for Predicting Alignment between the Saudi Nursing Workforce and the Gig Framework

The Role of Biocomposites and Nanocomposites in Eliminating Organic Contaminants from Effluents

An Intelligent Approach to Determine Component Volume Percentages in a Symmetrical Homogeneous Three-Phase Fluid in Scaled Pipe Conditions

Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System

Experimental Analysis to Detect Corona COVID-19 Virus Symptoms in Male Patients through Breath Pattern Using Machine Learning Algorithms

Application of Artificial Intelligence for Determining the Volume Percentages of a Stratified Regime’s Three-Phase Flow, Independent of the Oil Pipeline’s Scale Thickness