I have hands-on experience in Smart, where I worked on designing and enhancing PCBs, teaching the mechanics of 3D printers—including their assembly and functionality—and creating innovative solutions. I also designed a 4-axis robotic arm using NEMA17 stepper motors and TMC drivers, capable of precise part manipulation and multitasking. My work reflects a strong passion for robotics, hardware design, and emerging technologies.

I worked on various R&D projects that enhanced my PCB design and hardware development skills. Key accomplishments include:Arduino Shield PCB Design: Developed a full project PCB for educational purposes, integrating components such as an LCD, buzzer, LDR, DHT, and several sensors for both kids and adults.ESP32-Based PCB Design: Designed and implemented a project using ESP32 with components like flame sensors, RFID, fingerprint reader, keypads, and ultrasonic sensors.PCB Troubleshooting: Identified and solved a short circuit issue through a complete redesign, improving the system’s functionality.Mini CNC Plotter: Built a custom PCB to connect three stepper motors with LEDs using ULN2003 drivers, optimizing performance for smoother operation.Instructor for Kids: Led an introductory session teaching kids about logical operators through hands-on activities with push buttons.This internship provided me with hands-on experience in PCB design, troubleshooting, and embedded systems, as well as opportunities to develop leadership and instructional skills.

Completed training on AI and Machine Learning using tools like TensorFlow, Keras, and Scikit-learn.• Gained hands-on experience in Python programming, data analysis, and building AI applications.• Worked on tasks involving supervised and unsupervised machine learning, deep learning, and reinforcement learning.• Designed classification models (Logistic Regression, Decision Tree, Random Forest) and tuned hyperparameters for betterperformance.• Developed skills in clustering techniques such as K-Means and GMM, with insights into Principal Component Analysis (PCA).• Built simple neural networks and explored optimizers like Adam, SGD, and RMSProp for enhanced training performance.

Led a groundbreaking research project focused on applying deep learning models to detect and classify dementia stages in Alzheimer’s patients using MRI data. Collaborated with a team from Alexandria University to explore cutting-edge neural network architectures such as CNNs, VGG16, Vision Transformers (ViT), and Inception models. Achieved a remarkable 95.10% accuracy with the ViT model, offering significant potential for early-stage diagnosis of Alzheimer's disease.This research has the potential to revolutionize how dementia is diagnosed, providing doctors with advanced tools for early intervention. Completed the Deep Learning Specialization, which provided in-depth expertise in neural network architecture, optimization, and model tuning, all of which were key to the success of this project.

During my tenure as an IoT Instructor and Teaching Assistant at FCDS, I was dedicated to equipping students with both practical and theoretical knowledge in the Internet of Things (IoT). My teaching approach emphasized problem-solving, teamwork, and effective communication to ensure students were well-prepared for success in the IoT field.I addressed a wide range of topics, from electrical problems to software development, providing comprehensive answers to all necessary questions. Additionally, I conducted two insightful sessions on deploying IoT systems, defining DevOps and MLOps, and demonstrating the use of Flutter for monitoring app systems.One of the highlights of my experience was reviewing and discussing over 40 graduation projects from this internship, which was an invaluable opportunity for both the students and myself. I am proud to have been the first student in FCDS history to serve as a Teaching Assistant in Field Training, an experience that was both rewarding and fulfilling.

I've developed various ESP applications for detecting fire, light, and flooding, as well as integrating smart systems. Additionally, I have a strong grasp of cloud infrastructure, serving as a critical link between mobile apps and microcontrollers. In mobile app development, I've demonstrated proficiency in Flutter and seamless ESP integration. One of my most notable achievements is creating a Smart Home System, combining ESP32 with a Flutter app and using Firebase as the central access point for home management. Overall, I've successfully executed projects with precision and achieved my goals.