Syed has a Ph.D. under Interdisciplinary Machine Learning Research Group (IMLRG) at The University of Newcastle, Australia. During his Ph.D., he has proposed advanced deep reinforcement learning solutions for large-scale complex network problems such as spectrum occupancy prediction, multi-agent dynamic spectrum access, and QoS routing. He has extensively worked on mathematical problem formulation, reward function design, implementation, and training of deep reinforcement learning solutions. He has in-depth knowledge of advanced deep reinforcement learning algorithms such as model-free, model-based, and data-driven. Besides that, he also has applied knowledge of deep learning methods some of which are convolutional neural networks and recurrent neural networks. His Ph.D. work is published in top-ranked venues. He is currently involved with various companies around the world where he is assisting in the design of state-of-the-art deep reinforcement learning solutions for highly complex problems.