Muhammad Yunis Daha

INTRODUCTION

Muhammad Yunis Daha recently completed his PhD at Ulster University, Belfast, UK, supported by the prestigious Vice-Chancellor’s Fellowship. His research focused on deep learning-driven solutions for signal detection and channel modeling in massive MIMO systems for B5G/6G networks. He developed scalable and complexity-aware DL models that achieve high detection accuracy with low latency under diverse channel conditions, addressing key physical-layer challenges in next-generation wireless networks. 

Before his PhD, Yunis completed an MSc (by Research) in Information Technology at Universiti Teknologi PETRONAS, Malaysia, supported by a graduate research fellowship. His master’s research involved designing a Python-based community detection algorithm to improve link failure recovery in Software-Defined Networks (SDN), which won first place in the UTP 3MT thesis competition (master’s category) and resulted in publications in international journals and conferences. 

Yunis has extensive teaching experience as a teaching assistant across undergraduate and postgraduate courses at both UTP, Malaysia, and Ulster University, UK. He completed the Advance HE-accredited Postgraduate Teaching Assistant (PGTA) and First Steps to Teaching (FST) programs and holds the Associate Fellowship of the Higher Education Academy (AFHEA), reflecting his commitment to research-informed teaching and learning.

He has published over 19 peer-reviewed articles (Impact Factor: 20+) in top-tier journals, including IEEE, Elsevier, Wiley (Transactions), and MDPI, and presented at numerous IEEE international conferences. 

His research interests include AI-driven physical-layer optimization, machine learning, deep learning, massive MIMO, 5G/6G wireless networks, channel modelling, digital twin, and data analytics.