Abdullah J. Almutairi
Electrical Engineer - Signal Processing & Machine Learning
+966 583 364 583Riyadh, Saudi ArabiaAbdullahAlmutaiirr@gmail.comlinkedin.com/in/Abd-AlmutairiProfessional Summary
Electrical Engineering graduate from King Saud University (GPA 4.4/5.0) with a strong foundation in Signal Processing, Digital Systems, and AI applications. Ranked 6th in graduating class with Second Class Honors. Experienced in building machine learning pipelines for complex sensor data and coordinating technical requirements in large-scale infrastructure projects.
Education
King Saud University - B.Sc. Electrical Engineering
GPA: 4.4 / 5.0 - Ranked 6th in class - Second Class Honors
Experience
Project Engineering Co-op - NESMA & Partners Group
Supported planning, coordination, and execution of infrastructure projects. Monitored performance and analyzed technical requirements.
Graduation Project
ML-Based Sagnac Fiber Events Detection
Developed an AI-driven signal classification system for detecting and categorizing disturbances in Sagnac fiber-optic sensing networks. Built a machine learning pipeline using 400,000+ labeled samples.
Technical Skills
Digital & Embedded Systems
- Digital Logic Design & Microprocessors (Academic Theory & Lab)
- Hardware Description Languages: Verilog / VHDL (Academic Basics)
DSP & Signal Processing
- DSP Algorithm Design (MATLAB / Simulink)
- Digital Filter Design (FIR / IIR Filters)
- Physical Layer Signal Processing
Machine Learning & AI
- CNN-based Signal Classification
- Machine Learning for Embedded Systems (Edge AI)
- Predictive ML for Hardware and Asset Reliability
- TensorFlow/Keras, Python, Model Optimization
Telecom / RF
- RF Signal Processing & Wave Propagation
- Beamforming Algorithms & Spectrum Management
- Physical Layer Security (PLS), Wireless Communications
Photonics / Optoelectronics
- Optoelectronics & Fiber-Optic Sensing (Sagnac Interferometry)
- Distributed Acoustic Sensing (DAS) Signal Extraction
Additional Projects
- Digital Signal Processing: Voice Recovery & Filter Design
- RF & Radar Systems: Radar Signal Processing for RF sensing systems
- Brain Tumor Classification: Brain Tumor MRI (CNN) - 95% accuracy
Volunteer & Leadership
- Member, Electrical Engineering Club (2021-2023)
- Co-Author, Statics Course Guide (2022)
Languages
Arabic (Native), English (Professional)