Syed is currently awaiting thesis examination of his 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. 

Degrees

Experience