Acting Dean, Faculty of ECE
Associate Professor, Dept. of CSE
Bangladesh Army International University of Science and Technology, Cumilla, Bangladesh
Twenty years of teaching and research in computer science and engineering, with a PhD from UESTC focused on bio-inspired optimisation algorithms for autonomous UAV path planning and swarm coordination.
Dr. Golam Moktader Nayeem holds a PhD in Computer Science and Technology from the University of Electronic Science and Technology of China (UESTC), awarded on 18 June 2026 under Professor Dr. Mingyu Fan, following doctoral residence in Sichuan from 2018–2020 and continued remote research thereafter under institutional arrangements necessitated by COVID-19 travel disruptions.
He currently serves as Acting Dean of the Faculty of Electrical and Computer Engineering and Associate Professor in the Department of Computer Science and Engineering at Bangladesh Army International University of Science and Technology (BAIUST), Cumilla having previously led the department as Head, steering it through the BAC self-assessment and accreditation-readiness process and BAETE readiness.
His research centres on swarm intelligence, bio-inspired optimisation, metaheuristics, and reinforcement learning, applied chiefly to path planning and coordination for autonomous UAVs and UAV swarms. Across 20 years he has taught Computer Architecture, Computer Networks, Data Structures, and Software Engineering, and supervised 25+ undergraduate theses and capstone projects.
The core of Dr. Nayeem's dissertation, "Bio-Inspired Algorithms for UAV and UAV Swarm," validated against CEC 2021/2022 benchmarks and real-world path-planning scenarios.
A hierarchical Q-learning flocking framework that coordinates entire UAV swarms — not just single vehicles — balancing cohesion, separation, and shared mission goals across the flock.
Swarm coordinationA Q-learning-augmented Grey Wolf Optimizer that lets each "wolf" learn from experience while hunting for the optimal UAV flight path, adapting its search strategy on the fly rather than following fixed rules.
20 citationsAn adaptive hybrid Particle Swarm Optimizer merged with Grey Wolf behaviour and Beetle Antennae Search, giving 3D UAV path planning a more responsive, obstacle-aware convergence.
15 citationsGoogle Scholar h-index 6 · i10-index 6 · highest-cited paper 87 citations. Six journal articles (three SCIE) and nine EI-indexed conference papers.
Supervised 25+ undergraduate thesis and capstone projects (2015–2026) spanning machine learning, computer networks, and data-driven applications — including work that contributed to a published EI paper (ECCE 2025). Mentored student teams for ICPC, IUPC, and hackathons with multiple regional placements.
Open to postdoctoral positions, faculty appointments, and collaborations in bio-inspired optimisation and autonomous UAV systems.