Pattern Recognition

Unsupervised adaptive weight pruning for energy-efficient neuromorphic systems Frontiers in Neuroscience, section Neuromorphic Engineering.

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Wenzhe Guo, et al., "Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems." Frontiers in Neuroscience 14, 2020, 1189. To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online adaptive weight pruning method that dynamically removes non-critical weights from a spiking neural network (SNN) to reduce network complexity and improve

Seven IVUL members recognized as Outstanding Reviewers by CVPR 2021

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The 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) has granted Outstanding Reviewer awards to seven members of IVUL: Alejandro Pardo, Ali Thabet, Chen Zhao, Mattia Soldan, Mengmeng Xu, Juan C. Pérez, and Silvio Giancola. CVPR, a premier annual computer vision event that will take place virtually from June 19th to June 25th, is the largest computer vision conference in the world, gathering researchers from academia and industry alike. The peer-review process through which the conference determines acceptance of papers relies on expert reviewers from across the world

Naeemullah Khan

Instructional Assistant Professor, King Abdullah University of Science and Technology
Education Profile: ​Junior Research Fellow, Lady Margaret Hall, University of Oxford, 2020 - 2022 Postdoctoral Research Scientist, University of Oxford, 2019 - 2022 Doctor of Philosophy in Electrical Engineering, King Abdullah University of Science and Technology, Thuwal, 2018 Master of Science in Electrical Engineering, King Abdullah University of Science and Technology, Thuwal, 2014 Research Interests: ​Prof Khan research interests include continual learning and robustness in deep neural networks, where models are trained from continual streams of data and the target is to learn from new

Israel Jesus Santos Filho

MS Student, Electrical and Computer Engineering ,work under the supervision of Professor Tareq Al-Naffouri at King Abdullah University of Science and Technology.

Abdullah Alharthi

Postdoctoral Research Fellow, Electrical and Computer Engineering
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