Dr Nahina Islam is a Senior Lecturer and researcher in the College of Information and Communication Technology at Central Queensland University, Australia. She has a strong track record in teaching and research, supported by multiple industry- and government-funded projects, high-quality publications, and recognised achievements in artificial intelligence, deep learning and machine learning applications, Internet of Things (IoT), smart farming, smart technologies, green energy, and wireless communications.
She is the Cluster Lead of the AI and Data Science Research Cluster within CQU’s CML-NET Research Centre, a research lead at CML-NET, and a member of the Institute for Future Farming Systems, where she works closely with interdisciplinary teams and industry partners to support the translation of research into practice. Dr Islam also serves as a reviewer and track chair for leading international journals and conferences and is an active member of IEEE, ACS, and IEEE Women in Engineering.
She holds a PhD in Telecommunication Engineering from RMIT University, a Master’s degree from La Trobe University, and a Bachelor’s degree from Bangladesh University of Engineering and Technology (BUET).
Dr. Islam completed her PhD from RMIT University in 2017, Masters in Electronic Engineering from Latrobe University in 2012 and Bachelor degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET) in 2009. Her PhD research topic was ‘Energy Efficient Wireless Communication’, where she proposed energy efficient algorithms for terrestrial networks as well as Aerial base stations (UAVs). She developed a reinforcement learning (i.e. Markov decision process) based decision making algorithm towards an energy efficient system. This algorithm was applied to different applications, such as, to design energy-efficient base stations in terrestrial communication network and Aerial network; and to take energy efficient and delay aware handover decision in LTE-A heterogeneous network. Currently, she is working on several industry and government funded projects on the area of data analytics, IoT, image processing and Artificial Intelligence (AI). Her research work is focused on various applications of AI and IoT-based smart farming and smart environment.
Bachelor of Science in Electrical and Electronic Engineering, Bangladesh University of Engineering and technology, Bangladesh.
Masters in Electronic Engineering, Latrobe University, Australia.
PhD in Communication Engineering, RMIT University, Australia.
Sessional academic, RMIT University, Australia.
Research Assistant, Monash University, Australia.
Sessional academic, Charles Sturt University, Australia.
Lecturer, Melbourne Institute of Technology, Australia.
AEA grant success: Fresh funding for AI, drone research in weed management ( 07 July 2025)
AEA grant announcement (4 July 2025)
Autonomous weed-targeting AI drones a sky-high success (EATP Project demo news) (24 February 2025)
Farming Ahead- IoT-augmented drones to take war on weeds to the skies (15 February, 2023)
Grain Central- CQU’s drones take war on weeds to the skies. (10 February, 2023)
Member of Australian Computer society (ACS)
Member of IEEE
Prog Languages: Python, C/C++, Embedded C, Java, MATLAB, Verilog, MySQL, SQLite, MS Access ▪ Professional Word Processing: MS word, Latex, Tex ▪ General Office Software: MS Project, Visio, Word, PowerPoint; Adobe Photoshop ▪ Software: PSPICE, Quartus, NIOS, CST, Altium, CelPlanner, Labview. ▪ Statistical Analysis: MS Excel
Guest Editor, Special Issue “Artificial Intelligence and Automation in Sustainable Smart Farming” in Remote Sensing (Impact factor 4.509, Scopus Cite score 6.1), Deadline for Submissions: 31 August 2020.
https://www.mdpi.com/journal/remotesensing/special_issues/AI_smart_farming
I am currently accredited for supervision in the following:
Artificial Intelligence and Image Processing - Image Processing
Communications Technologies - Wireless Communications
Internet of Things (IoT), Machine learning, deep learning, Image Processing, Smart farming