Name
Position
School
Qualifications
Profile
Dr. Maryam Kouzehgar
Course coordinator and lecturer
School of Electrical Engineering
maryam.kouzehgar@eit.edu.au
PhD
Biography
Dr. Maryam Kouzehgar is a Lecturer with Engineering Institute of Technology (EIT), Melbourne Campus where she is actively collaborating with the School of Electrical Eng. and the School of Industrial Automation Eng.
Dr. Kouzehgar holds B. Sc., M. Sc., and Ph. D .degrees in Electrical Engineering (Control Systems) completing her PhD in 2015. She has over 15 years of academic experience in Australia and overseas, spanning teaching, research, postgraduate supervision, and curriculum development leadership.
Prior to joining EIT, she was a Senior Post-Doctoral Research Fellow at Singapore University of Technology and Design (SUTD), affiliated with SUTD–MIT International Design Center, where her work focused on intelligent systems, learning-based control, and AI-enhanced collaborative robotics.
At EIT, Dr. Kouzehgar is actively involved in course development initiatives, contributing as a member of Course Advisory Committees across Electrical, Automation, Computer Systems and Robotics programs to support interdisciplinary and industry-oriented curriculum. She also contributes to assessment moderation, accreditation-driven curriculum mapping, and doctoral and research proposal review panels. Her academic practice is driven by a commitment to high-quality engineering education, relevance to industry, and continuous improvement.
With regards to teaching, she mainly handles topics on control systems, robotics, smart grids, power electronics, industrial automation, and intelligent engineering systems, with a strong emphasis on applied learning and professional practice. Apart from teaching she is supervising Masters and Doctor of Engineering (DEng) students at EIT. Her research interests include control engineering, robotics, machine learning, multi-agent coordination, and smart grid technologies, with a focus on developing robust and adaptive solutions for complex engineering systems.
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Over 15 years of teaching experience at Tertiary level (Bachelor, Masters, Ph. D.)
| Currently Teaching courses/programs | MEE510 Power Conversion |
| MEE605 Smart Grids | |
| MEE606 Substation Design and Automation | |
| MEE607 Power Quality and Mitigation | |
| MIA500 Introduction to Industrial Automation | |
| ME700 Master Thesis Supervision | |
| DEng Supervision |
| Fields of Research: | control engineering, robotics, machine learning, smart grids |
| Research Interest: | multi-agent coordination, smart grids technologies, AI-enhanced robotics, and collaborative AI |
M. Kouzehgar, Y. Song, M. B. Prasetyo, Y. Loo, S. Li, M. Meghjani, R. Bouffanais, “Multi-Agent Dynamically Networked and Decentralized Pursuit-Evasion”, 5th IEEE International Symposium on Multi-Robot & Multi-Agent Systems (IEEE MRS 2025), December 2025, Singapore.
M. Kouzehgar, Y. Song, M. Meghjani and R. Bouffanais, “Multi-Target Pursuit by a Decentralized Heterogeneous UAV Swarm using Deep Multi-Agent Reinforcement Learning”, 2023 IEEE International Conference on Robotics and Automation (ICRA2023), London, United Kingdom, 29 May- 2 Jun. 2023, pp. 3289-3295, doi: 10.1109/ICRA48891.2023.10160919.
M. Kouzehgar, M. Meghjani, R. Bouffanais, “Multi-Agent Reinforcement Learning for Dynamic Ocean Monitoring by a Swarm of Buoys”, Global OCEANS 2020: Singapore – U.S. Gulf Coast, 5-14 Oct. 2020
M. Kouzehgar, Y.K. Tamilselvam, M. Vega Heredia, M. Rajesh Elara, “Self-Reconfigurable Façade-Cleaning Robot Equipped with Deep-Learning-Based Crack Detection based on Convolutional Neural Networks”, Automation in Construction, Vol. 108, 2019.
M. Kouzehgar, M. Rajesh Elara, M. Ann Philip, M. Arunmozhi, V. Prabakaran, “Multi-Criteria Decision-Making for Efficient Tiling Path Planning in Tetris-inspired Self-Reconfigurable Cleaning Robot”, Applied Sciences, Vol. 9, Issue 1, 63, 2019.
M. Kouzehgar, M. A. Badamchizadeh, “Fuzzy Signaling Game of Deception between Ant-Inspired Robots with Interactive Learning”, Applied Soft Computing, Volume 75, pp. 373-387, 2019.
M. Kouzehgar, M. A. Badamchizadeh, M. R. Feizi-Derakhshi, “Ant-Inspired Fuzzily Deceptive Robots”, IEEE Transactions on Fuzzy Systems, April issue, vol. 24, no. 2, 2016.