WASAN FAIZ ALTHOBAITI

Dynamic professional with expertise in Artificial Intelligence, Machine Learning, and Deep Learning (CNN), leveraging PyTorch for advanced data analysis. Proficient in developing Digital Twin Systems and IoT & Smart Systems, with a strong foundation in Embedded Systems & Arduino. Skilled in Networking and Cybersecurity Fundamentals, complemented by leadership abilities and strategic planning acumen. Adept at team collaboration, problem solving, and public speaking, with a focus on driving innovation through strategic planning and public relations.

• Machine Learning with PyTorch – Tuwaiq Academy (20 Hours)
• Artificial Intelligence Technologies – Technical and Vocational Training Corporation (15 Hours)
• Cisco Certified Ethical Hacker – 70 Hours
• IBM Cybersecurity Fundamentals
• IBM Data Analysis
• IBM Artificial Intelligence Fundamentals
• Thakaa Artificial Intelligence Program – King Saud University
• Power BI Intermediate – MCIT & ICG Academies

• Medical X-ray Classification using CNN – Deep learning model for automated medical image classification.,
• Smart Monitoring System using Arduino – IoT system integrating sensors and automated monitoring.,
• Nadeem – AI platform monitoring elderly wellbeing and alerting caregivers.

• First Place – Technical Track (University Level) – Iktashifni AI Project,
• Second Place – National Level – Hajj Health & Safety Hackathon 2025 (Manarah AI & AR system),
• Second Place – Western Region – Metals Pioneers Hackathon (SafeGen system)

• Founder & Leader – Athar Club, Taif University ; Founded a technical–social club focused on humanizing technology and promoting responsible innovation. The club has become one of the most active student clubs at Taif University.
• Leader – IEEE Student Club
• Leader – AI & Programming Club
• Leader – Awards & Appreciation Committee, SPE Taif Chapter

• Spidex – AI & Digital Twin Robotic Monitoring System (Patent No. 59430885 – 2025)
• Developed an intelligent monitoring system consisting of three spider-inspired robotic units connected through a hybrid dual-network architecture. Each unit collects environmental data through sensors and sends it to a real-time database for processing. A Digital Twin simulation environment built in Unity replicates the real system and visualizes robot movement and environmental data. A monitoring dashboard displays live analytics and supports data-driven operational decisions.