ABD AL RAHMAN M. ABU EBAYYEH

Biography

Abdalrahman M. Abu Ebayyeh is currently working as a Teaching Fellow in Applied Machine Learning at the Electrical and Electronics Engineering Department, Imperial College London. He joined the department in June 2022.

Previously, he had worked as Laboratory Technologist and teaching assistant in higher education institutes in the UAE. He was responsible on teaching several courses such as MATLAB, Statics, Vibrations, C++ Programming, Pneumatics and Hydraulics and CAD/CAM. Furthermore, he was supervising the labs and preparing the practical experiments for the students for the Electromechanical Engineering subjects. He was also assigned to train local students to participate in Emirates Skills competition under the Mobile Robotics Category, where they learned how to program the robot using LabView and MyRio kit.

Dr. Abu Ebayyeh received the BSc degree in Mechatronics Engineering from the University of Jordan, Amman, Jordan in 2013. He received the MSc degree in Aerospace Engineering with Distinction from Queen Mary University of London in 2015. He also received a PhD in Electronics and Computer Engineering from Brunel University London in 2022, where his research focused on the application of deep learning and computer vision in industrial automation. During his PhD, he worked on a funded EU Horizon project called iQonic that deals with identifying defects in optoelectronic wafers in different laser industries across Europe. He used many machine vision techniques such as image processing, deep learning (CNNs, CapsNet and convolutional autoencoders) to identify the defects in the wafers. His solutions are implemented and published in several journal articles. As a PhD student in Brunel, he worked as graduate teaching assistant (GTA) in the departments of Electronic and Computer Engineering and Computer Science. He mainly assisted in teaching the Artificial Intelligence and Deep Learning modules.

Dr. Abu Ebayyeh holds the Associate Fellowship in Higher Education Academy (AFHEA) since October 2020. His current research interests include machine learning, deep learning, computer vision, industrial automation, and robotics.