Bilal Majeed

Bilal Majeed is a PhD researcher in Computer Science at the University of Limerick, working at the intersection of machine learning, evolutionary computation, electronic design automation, and digital hardware design. His doctoral research explores how machine learning and grammatical evolution can automate the generation of synthesisable hardware description language (HDL) code for digital sequential circuits. The objective is to develop intelligent methods that help make digital design workflows more efficient while producing designs suitable for FPGA-targeted implementation. His work combines computational intelligence with practical electronic design. Using Python-based experimental workflows, Bilal develops and evaluates methods for generating and validating HDL for components such as finite-state-machine-based circuits and sequence detectors. His research has resulted in peer-reviewed publications on the automatic generation of synthesisable HDL and behavioural-level hardware descriptions. He has also worked in industry-linked research settings with Intel and S3 Group, gaining experience in rigorous experimental design, model evaluation, documentation, and translating research outputs into practical engineering workflows. Bilal has an interdisciplinary background in electrical and electronic engineering, embedded systems, machine learning, and AI-enabled hardware. Before beginning his PhD, he worked as a Research Assistant at Lahore University of Management Sciences, contributing to the development of a non-invasive handheld breast-cancer screening device. This project combined thermographic sensing, embedded hardware, biomedical signal processing, data acquisition, MATLAB-based machine learning, and prototype development. The work strengthened his interest in applying intelligent systems to real-world sensing and healthcare challenges. Bilal is currently a Research Assistant in ADAS Systems at the University of Limerick, working on the MIMRAD5D project. His research focuses on automotive perception datasets, sensor configuration, computer vision, radar–camera fusion, and machine-learning models for obstacle detection and tracking. The role extends his interdisciplinary background in machine learning, embedded systems, computer vision, sensor-driven experimentation, and hardware-oriented design. Alongside his PhD, previously, Bilal has worked as a Research Assistant in UL’s Esports Research Lab, where he applied statistical analysis and machine learning to large-scale real-world datasets. He developed reproducible data pipelines and analytical workflows using Python, pandas, NumPy, scikit-learn, and TensorFlow, collaborating with Logitech on research outputs and publication-oriented work. Bilal also contributes as a part-time lecturer in the Department of Computer Science and Information Systems at UL. His interests include machine learning for hardware design, embedded intelligence, computer vision, biomedical devices, reproducible experimentation, and the practical translation of academic research into engineering applications.