Rate My Professor Mohammadre Chambanbaz

Mohammadreza Chamanbaz is a Senior Research Fellow and RTCMA Net Zero Transition Project Lead in the Faculty of Engineering at the University of Sydney. He serves at the Rio Tinto Centre for Mine Automation within the Australian Centre for Field Robotics. His fields of research include control engineering, optimisation, control engineering mechatronics and robotics, and software engineering. Chamanbaz obtained his PhD in Control Science from the Department of Electrical and Computer Engineering at the National University of Singapore. Prior to his current role, he was a Senior Researcher at the Singapore University of Technology and Design. He earned his MSc in Control Engineering from K.N. Toosi University of Technology in Tehran, Iran, in 2011, and his BSc in Electrical Engineering-Power from Shiraz University of Technology in Iran in 2008.

Chamanbaz's academic interests center on robust optimisation, randomized algorithms, and control systems under uncertainty, with applications to multi-robot systems, swarm behaviors, autonomous robotics, and cyber-physical systems. Key publications include "Swarm-enabling technology for multi-robot systems" (Frontiers in Robotics and AI, 2017), "Optimal network topology for responsive collective behavior" (Science Advances, 2019), "Distributed system of autonomous buoys for scalable deployment and monitoring of large waterbodies" (Autonomous Robots, 2018), "Sequential randomized algorithms for convex optimization in the presence of uncertainty" (IEEE Transactions on Automatic Control, 2015), "Probabilistically robust AC optimal power flow" (IEEE Transactions on Control of Network Systems, 2019), and "Randomized constraints consensus for distributed robust mixed-integer programming" (IEEE Transactions on Control of Network Systems, 2020). His contributions extend to topics such as physics-based attack detection in cyber-physical systems and stochastic haul rate planning in mining operations using model predictive control. These works have been published in leading journals, advancing methodologies for distributed robust optimization and collective robotics.