Muhammad Ashar Javid

PROFESSIONAL PROFILE Electrical Engineering student and researcher specializing in next-generation wireless communications, with a focus on Reconfigurable Intelligent Surfaces (RIS) and 6G networks. Proven track record of high-impact research, including an accepted paper at IEEE WCNC 2026 and an international research internship through MITACS Globalink. Expertise in integrating Large Language Models (LLMs) and Agentic AI into wireless frameworks to optimize network performance. Conducting research on the intersection of quantum-native communication and intelligent signal processing. EDUCATION Electrical Engineering School of Electrical Engineering and Computer Science , Islamabad , 3.6 (2026) INTERNSHIP EXPERIENCE University of Alberta, Canada 09-Jun-2025 - 31-Aug-2025 1. Leveraged geometric deep learning to analyze non-Euclidean seismic data across distributed sensor arrays. 2. Processed large- scale geophysical datasets to identify precursor patterns often missed by traditional linear models. 3. Worked in a high-stakes research environment at UAlberta, honing technical communication and cross-functional problem-solving skills. FINAL YEAR PROJECT Realtime RIS Optimization for 6G networks using Multi Agent Framework The era of 6G requires technologies like RIS, NOMA and Backscatter for ultra-reliable latency aware systems with data rates approaching terabytes per second. Such technologies require efficient management of resources in dynamic and ever evolving environments. Traditional statictical techniques and DRL based schemes are specialised for singular kind of optimization however require heavy computational resources to cater for a dynamic environment. We propose a LLM-reasoning based multi-agentic technique MAFRO working through semantic communication using ACPN for efficient resource management in next generation wireless networks achieving high level of fairness and energy efficiency. TECHNICAL EXPERTISE Intelligent Systems & Advanced Signal Processing Expertise lies at the intersection of traditional engineering and frontier artificial intelligence, specializing in the fusion of signal processing with Multi-Agentic AI frameworks. By leveraging Large Language Models (LLMs) to enhance decision-making and autonomous coordination, I develop robust solutions fo ...

Electrical Engineering student and researcher specializing in next-generation wireless communications, with a focus on Reconfigurable Intelligent Surfaces (RIS) and 6G networks. Proven track record of high-impact research, including an accepted paper at IEEE WCNC 2026 and an international research internship through MITACS Globalink. Expertise in integrating Large Language Models (LLMs) and Agentic AI into wireless frameworks to optimize network performance. Conducting research on the intersection of quantum-native communication and intelligent signal processing.