Tristan Perez uses his experience in science, engineering, and mathematics to combine knowledge from different areas to help others understand their problems so that together we can develop solutions that can transform their practices. For 18 years, Tristan has been conducting transdisciplinary research in collaboration with industry, defence, and government in areas such as marine and aerospace technology, econometrics, mining, renewable energy, robotics, warfare, and agriculture
Tristan is a Professor at the Queensland University of Technology (QUT)’s Institute for Future Environments. He is also a Chief Investigator at The ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research interests are in the resilience of cyber-physical systems. This work involves understanding and managing behaviours of complex dynamical systems, uncertainty quantification, analytics, decision making, optimisation, and control.
Tristan is originally from Argentina and immigrated to Australia in 1999 after he completed my Electronics Engineering degree (a six-year program). He did a PhD in the area of Mathematical Control Theory at the University of Newcastle, Australia, in 2003. He spent four years in The United Kingdom and Norway working on aspects of fault-detection, inference, optimisation, mathematical modelling and control in marine technology applications. In 2007, he joined the ARC Centre of Excellence for Complex Dynamic Systems and Control (CDSC) at the University of Newcastle, Australia. At CDSC, developed research in mine-planning optimisation, econometrics, and dynamics and motion control of marine and aerospace vehicles in collaboration with BHP, the Australian Department of Defence and Boeing. In 2014, he was appointed Professor at the QUT where he led the developments in digital agriculture and agricultural robotics. His current research portfolio includes projects in defence and agriculture.
Sa, I., Ge Z., Dayoub F., Upcroft B., Perez T., & McCool C.
(2016). DeepFruits: A fruit detection system using deep neural networks. Sensors. 16(8), doi: 10.3390/s16081222
Schiffner, I., Perez T., Srinivasan M. V., & Osorio D.
(2016). Strategies for pre-emptive mid-air collision avoidance in budgerigars. PLOS ONE. 11(9), doi: 10.1371/journal.pone.0162435
Johansen, T. A., Perez T., & Cristofaro A.
(2016). Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment. IEEE Transactions on Intelligent Transportation Systems. 17(12), 3407-3422. doi: 10.1109/TITS.2016.2551780