Mohamed Abdelbari

Golden Visa Holder with expertise in AI, embedded systems, robotics, and IoT applications. Experienced in deploying machine learning models, developing intelligent systems, and leading technical projects with proven results in industrial and research settings.

Teaching Assistant for Artificial Intelligence and Engineering Applications at Khalifa University (2026-02 – 2026-05)

• Guided students in deploying machine learning models on microcontrollers, including model optimization, quantization, and real-time inference.
• Developed structured tutorials and implementation guides to support student learning and independent project development.

Lab Instructor of Electrical, Computer, and Biomedical Engineering at Abu Dhabi University (2026-01 – 2026-02)

• Conducted and supervised undergraduate laboratory sessions in Electrical, Computer, and Biomedical Engineering courses.
• Guided students in programming, embedded systems, circuit design, and senior projects.
• Designed, evaluated, and graded laboratory experiments, practical exams, and technical assessments.
• Provided academic mentoring and technical troubleshooting support.
• Managed laboratory setup, equipment calibration, safety compliance, and inventory coordination.

Machine Learning Intern at ADNOC (2024-07 – 2024-08)

• Developed a real-time debris event detection system using live sensor data for predicting the presence of debris in a sour gas wells.
• Pioneered a real-time debris detection system leveraging live sensor data and AI, enhancing productivity and revenue by 8–12%.
• Achieved an accuracy of 98.3% using LSTM-based AI models for predictive analysis of debris events.
• Streamlined real-time data predictions by optimizing "on-demand" forecasting during downtime intervals via a GUI.

Undergraduate Research Assistant at Mubadala (2024-01 – 2024-06)

• A multimodal deep detection, localization and detection system for drone and controller
• Designed and implemented a multimodal system with RF and visual sensors, achieving 97%+ detection accuracy for drones through advanced deep learning and AI-driven RF analysis.
• Enhanced detection reliability by integrating the Dempster-Shafer fusion method and real-time tracking with a custom user interface.
• Innovated drone neutralization mechanisms using high-power lasers and RF jamming, ensuring regulatory compliance and privacy protection.

Masters Degree in Electrical and Computer Engineering – Khalifa University (2024-08 – 2026-08)

Bachelor of Science in Computer Engineering – Abu Dhabi University (2020-08 – 2024-09)