Farah Alhayek

Designer & Instructor

Bilingual Arduino & Electronics Workshop for Youth

Designed and delivered a six-session bilingual Arduino and electronics course for ages 10–13, culminating in a simulated traffic-light project.

• Designed a 6-session bilingual (Arabic/English) electronics and Arduino curriculum for learners aged 10–13, delivered live online via Google Meet, using Tinkercad Circuits so students could build and test without physical hardware. • Built original slide decks and illustrated teaching aids (a cars-on-a-road analogy for circuit concepts); structured the course around progressive milestones culminating in a simulated traffic-light project.

AI Training Content Developer

Not specified

Created an annotated presentation on practical AI tools to support educator onboarding.

• Developed a training presentation on practical AI tools for educators, built around annotated software walkthroughs to support instructor onboarding.

Technical Volunteer

School Robotics Competition, Kuwait

Supported student teams with robot setup and troubleshooting and helped faculty run competition technical operations.

• Guided school-age student teams through robot setup, sensor troubleshooting, and task preparation under competition time pressure; supported faculty in running technical operations smoothly across all rounds.

Undergraduate Researcher

American University of the Middle East (AUM)

Designed a wearable assistive system for hearing-impaired users and developed a GRU model for sound localization across 24 directions.

• Designed a wearable AI-assisted system integrating sound recognition, sound source localization, and speech-to-text for hearing-impaired users. • Developed a GRU-based sound source localization model for 24 azimuthal directions with strong real-time localization performance.

Project Developer

AUM AI & Entrepreneurship Certification Program

Developed a GRU encoder-decoder digital twin model to detect anomalies across four industrial control system attack scenarios.

• Developed a GRU encoder-decoder digital twin model for anomaly detection in industrial control systems using multivariate sensor data across four attack scenarios.

BSc computer engineering

AUM