Hamdi Abed

Researched the efficiency and scalability of existing reinforcement learning-based AutoML. Focused on finding an optimal CNN solution via ENAS and DARTs search algorithms to fit in a limited memory microcontrollers. ENAS model was implemented using Python-based ML framework TensorFlow. Dataset was handled using Sci-Kit Learn, Numpy, Pandas, and MatPlotLib. Evaluation logs were monitored using TensorBoard and WandB technologies. Models were containerized using Docker and trained on GPUs within Linux OS.

Was awarded the Stipendium Hungaricum full-funded scholarship program to complete Ph.D. studies. • Proposed Ph.D. topic: "Optimization of Automated Machine Learning models". • Develop deep neural networks structures and applications in various set of tasks. • Implement efficient deep learning models in Natural Language Processing (NLP) and sequential data modelling. • The research focuses on state-of-art models such as: Transformers, LSTM, RNN, CNN, and TCN.

Awarded the Stipendium Hungaricum full-funded scholarship program to complete MSc studies. • GPA: 4.88/5.0 • Studied Mechatronics and robotics systems, signal processing, and sensory systems. • Applications of electrical and electronics techniques, using LabView to create Mechatronics systems, and Control and supervisory systems design. • Graduation thesis topic for MSc: "LabView-based Cars detection using Machine Learning approaches".

A BSc in Electrical Engineering studies, including signal processing, digital signal processing, telecommunications, control systems, intelligent data analysis, digital design, electrical and electronic devices, probability theory, and statistcs.