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Hatem Abou-Zeid
Also published under:Hatem Abou-zeid
Affiliation
University of Calgary, Calgary, AB, Canada
Topic
Channel State,Deep Neural Network,Neural Network,Transfer Learning,Deep Reinforcement Learning,Federated Learning,Global Model,Performance Metrics,Reward Function,User Equipment,Wireless Networks,Deep Learning,Deep Learning Models,Long Short-term Memory,Machine Learning,Accuracy Of Model,Base Station,Convolutional Neural Network,Deep Neural Network Model,Deep Reinforcement Learning Algorithm,Edge Devices,Edge Server,Inference Time,Learning Algorithms,Machine Learning Models,Model Size,Multiple-input Multiple-output,Optimization Problem,Precoder Design,Quality Of Experience,Radio Resource Management,Reinforcement Learning Agent,Reinforcement Learning Algorithm,Resource Allocation,Time Slot,Ultra-reliable Low-latency Communications,6G Networks,Artificial Neural Network,Convergence Rate,Deep Reinforcement Learning Agent,Efficient Algorithm,Gradient Descent,Inequality Constraints,Latency Requirements,Learning Models,Loss Function,Lyapunov Optimization,Markov Decision Process,Mobile Network Operators,Motor Imagery,
Biography
Hatem Abou-Zeid is an Assistant Professor at the University of Calgary. Previously, he worked at Ericsson and Cisco. His research expertise is in low-latency and extended-reality networking, robust machine learning, and semantic communications. His work led to 19 patent filings and several wireless access algorithms deployed in 5G networks.