Professional Experience:
Research Assistant
Organization: Ulster University, Belfast, United Kingdom | October 2025 – February 2026
Key Responsibilities: Design and implementation of a secure communication framework for drone systems.
PhD Research
Organization: Ulster University, Belfast, United Kingdom | September 2022 – September 2022
Key Responsibilities: Develop scalable DL-based algorithms for signal detection and channel modelling in 6G networks.
Graduate Research Assistant
Organization: University Teknologi PETRONAS, Malaysia | November 2019 – March 2022
Key Responsibilities: Design an optimized community-detection-based routing algorithm for link-failure recovery in SDN.
Teaching Experience:
Lab Demonstrator:
Organization: Ulster University, Belfast, United Kingdom | January 2023 – March 2025
SUBJECT Subject Level TA Year
EEE412 - Mechatronics Undergraduate 2023
COM771 - Cyber Security Postgraduate 2023
EEE426 - Engineering Programming Undergraduate 2024
EEE527 – Embedded Systems Postgraduate 2024
EEE426 - Engineering Programming Undergraduate 2025
Lab Demonstrator:
Organization: University Teknologi PETRONAS, Malaysia | November 2019 – March 2022
SUBJECT Subject Level TA Year
Introduction to Computer Programming Undergraduate 2019
Cloud Computing Undergraduate 2020
Structure, Algorithm & Programming Foundation Year 2021
Probability and Random Processes Undergraduate 2021
Structure, Algorithm & Programming Foundation Year 2022
Drone Sentinel: A Secure Communication Framework for Drone Systems
Funding Agency: Innovate UK Cyber-ASAP Funded Project | October 2025 – February 2026
Organization: Ulster University, Belfast, United Kingdom
Description: The Innovate UK–funded Cyber-ASAP project is an ongoing initiative that deals with the design and implementation of a secure communication framework for drone systems.
Edge Intelligence for Beyond 5G Network
Funding Agency: Ulster University, Belfast, United Kingdom | September 2022 – December 2025
Organization: Ulster University, Belfast, United Kingdom.
Tools and Technologies: MATLAB, Origen, LaTeX
Description: This PhD research project explores the integration of AI, specifically machine learning and deep learning, into Beyond 5G and 6G wireless systems. It focuses on enhancing ultra-reliable low-latency communications by optimizing physical layer functions such as signal detection and channel modeling in massive MIMO systems.
Contribution :
Developed multiple AI-based models for signal detection tailored for B5G/6G systems.
Introduced complexity-aware deep learning architectures for real-time and scalable deployment.
Enhanced model adaptability by incorporating channel imperfections and loss.
Validated efficiency through extensive simulations & comparison with state-of-the-art methods.
Published results in ISI-indexed Q1/Q2 journals and various IEEE conferences.
Fast Failure Recovery of SDN with Robustness-Aware Rule Placement Scheme
Funding Agency: Ministry of Higher Education, Malaysia.
Organization: University of Technology PETRONAS, Malaysia. | March 2020 August 2021.
Tools Used: Python, Mininet, POX Controller, Open v-Switch version 1.0, Origen, LaTeX.
Description: This project focused on developing a robust routing mechanism to improve fault tolerance and recover from single and multiple link failures in Software-Defined Networking.
Contribution :
proposed a novel community detection-based routing algorithm for link failure recovery in SDN.
Improved failure recovery time and minimized packet loss in SDN environments.
Smart Grid Control Panel Simulator using a GSM Module.
Organization: COMSATS University Islamabad, Wah Campus, Pakistan
Designation: BS Final Year Project
Tools Used: MATLAB, GSM Module, Arduino MEGA, Proteus.
Description: This project aimed to create a smart grid control panel simulator by scaling down a traditional 11kV grid station using Arduino UNO, GSM Module, MATLAB, ZigBee, and Proteus.
Contribution :
Designed and implemented the simulator, integrating hardware and software components to enable real-time monitoring and control of grid operations within a compact setup.