convolutional neural network

Yazeed Alharbi

Ph.D., Computer Science

Computer Vision machine learning Deep learning generative adversarial network convolutional neural network

Yazeed Alharbi is a Ph.D. candidate under the supervision of Prof. Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career In 2015, Yazeed obtained his bachelor degree from Purdue University in the computer graphics and visualization track, with a minor in philosophy. In 2018, he received his master degree from King Abdullah University of Science and Technology (KAUST). He mainly learned about computer vision and the process of publication in that field. Research Interest Currently, Alharbi research is focused

Li Zhang

Ph.D. Student, Electrical and Computer Engineering

neural network accelerator convolutional neural network software/hardware co-design

Li Zhang is currently a Ph.D. student at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Li Zhang received the B.S. degree in microelectronic science and engineering from University of Electronic Science and Technology of China, China, 2018 and the M.S. degree in electrical engineering from King Abdullah University of Science and Technology, Saudi Arabia, 2019. Areas of Expertise and Research Interests Li Zhang is interested in quantized neural networks, neural network accelerator and

Abdullah Alharthi

Postdoctoral Research Fellow, Electrical and Computer Engineering

data analysis Pattern Recognition Sensor Fusion Deep learning Human Gait Analysis Applications convolutional neural network Perception Human-robot interaction

Dr. Abdullah Alharthi's research centers around pattern recognition as a means to tackle a diverse array of new challenges, including how to learn intelligent behavior in complex, dynamic environments. His areas of expertise include cognition, perception, theory of mind, gait, and human-robot interaction. His work delves into both human kinetics and the comprehension and resolution of how humans and robots interact with one another in a dynamic setting. Dr. Alharthi employs Deep Learning techniques to tackle problems related to image, object, and signal recognition and classification