Daniel Quevedo

Prof. Daniel Quevedo is a Professor in Cyberphysical Systems at the School of Electrical Engineering and Robotics, Queensland University of Technology (QUT). He previously led the Chair in Automatic Control at Paderborn University, Germany. He holds a PhD from the University of Newcastle (Australia) and degrees from Universidad Técnica Federico Santa María (Chile). His research focuses on networked cyberphysical systems, cybersecurity, and model predictive control, with over 240 publications and three patents. He has attracted over $2M in research funding and is a Fellow of the IEEE. His work emphasizes cross-disciplinary collaboration in fields like power electronics, telecommunications, and behavioral economics.

• Educations:
  • PhD in Automatic Control (2005), University of Newcastle, Australia
  • M.Sc. and B.Eng. (2000), Universidad Técnica Federico Santa María, Chile

Research Interests: Prof. Quevedo’s work bridges theoretical systems control with practical applications. Key areas include networked cyberphysical systems, cybersecurity (e.g., deception attacks, privacy preservation), and energy-efficient control strategies. His contributions span event-triggered control, encrypted cooperative systems, and power converter optimization. He collaborates globally with institutions like ABB, TU Berlin, and Indian Institute of Technology Bombay.

Grants & Awards: Notable grants include DFG-funded projects on network-informed control and ARC grants on predictive control. His 2018 IEEE Axelby Award highlights his impact on systems control theory. He has also held visiting professorships and editorial roles for journals like IEEE Transactions on Automatic Control.

Teaching & Supervision: At Paderborn University, he modernized control systems curricula, integrating blended learning and industry-relevant projects. He has supervised over 10 PhD/MSc theses and mentored postdoctoral researchers in areas like cyber-physical security and distributed control.

Labs & Teams: He is affiliated with QUT’s Centre for Robotics, focusing on interdisciplinary robotics and cyberphysical systems research.