Muhammad Yunis Daha

INTRODUCTION

Muhammad Yunis Daha is a passionate, research-driven PhD scholar focused on advancing machine learning, deep learning, and next-generation wireless technologies (B5G/6G). He is currently completing his PhD and holds an Associate Fellowship of the Higher Education Academy (AFHEA) at the School of Computing, Engineering, and Built Environment, Ulster University, Belfast, United Kingdom. His PhD research explores the integration of AI, particularly deep and machine learning architectures, into massive MIMO systems to tackle critical challenges in signal detection and channel estimation for beyond 5G/6G networks. His work emphasizes scalable, complexity-aware deep learning models designed for ultra-reliable, low-latency communications (URLLC) with real-time deployment capability. His research has been published in several ISI-indexed Q1/Q2 journals and presented at various IEEE conferences.

Prior to his PhD, Yunis completed his MSc in Information Technology (by Research) at Universiti Teknologi PETRONAS (UTP), Malaysia, where he was awarded a Graduate Research Assistantship. He contributed to a government-funded project titled "Fast Failure Recovery of SDN with Robustness-Aware Rule Placement Scheme," focusing on link failure recovery in Software-Defined Networks (SDN). His proposed community detection-based algorithm achieved significant improvements over existing benchmark methods.

Alongside his research, Yunis has extensive teaching experience as a teaching assistant at both UTP and Ulster University. He has supported undergraduate and postgraduate courses in digital electronics, signal processing, and wireless communication. He has also completed the Advance HE-accredited Postgraduate Teaching Assistant (PGTA) and First Steps to Teaching (FST) programs, aligned with the UK Professional Standards Framework.

His core research interests include machine learning, deep learning, massive MIMO, signal detection, channel estimation, and AI-driven physical layer optimization for B5G and 6G networks.