Dr. Ahmed Wagdy Shehata of BLINC Lab, University of Alberta

Humans are extremely swift learners, we are able to grasp highly abstract notions, whether they come from art perception or pure mathematics. Current machine intelligence techniques demonstrate astonishing results in extracting patterns in information. Yet the abstract notions we possess are more than just statistical patterns in the incoming information. In this talk, we will discuss a way for machine learning and sensory feedback to benefit from each other so as to pave the way toward new horizons in learning.

Biography:

Dr. Shehata joined the BLINC Lab in 2018 as a postdoctoral fellow. He received a B.Sc. degree in Engineering Materials Science and an M.Sc. degree in Mechatronics Engineering from the German University in Cairo (GUC), Egypt, in 2012 and 2013, respectively. In 2015, he pursued his doctoral studies at the Institute of Biomedical Engineering, University of New Brunswick, Canada where he acquired his Ph.D. degree in Electrical Engineering in 2018. Between 2013 and 2015, he worked as an Assistant Lecturer at the German University Berlin-campus, Germany, where he taught several graduate-level courses in Mechatronics Engineering.

Dr. Shehata spent the summer of 2017 in the Artificial Hands Area Lab, The Biorobotics Institute, Scuola Superiore Saint’Anna, Pisa, Italy working on exploring sensory feedback to improve myoelectric prosthesis control for upper-limb amputees.

His research interests include prosthesis control, computational motor control for human-machine interfaces, and sensory feedback systems for upper- and lower-limb prosthesis.

Dr. Shehata is a registered Member of the Egyptian Engineers Syndicate, the IEEE Engineering in Medicine and Biology Society (EMBS), and the IEEE Robotics and Automation Society (RAS).

He enjoys long walks, playing soccer, and building autonomous aerial vehicles in his spare time.