Ayad Turky is currently a lecturer with the discipline of IT, College of Engineering and Science, Victoria University.

He received the PhD degree from RMIT University, Melbourne, Australia, in 2019.

Immediately after submission of his PhD thesis, he was appointed as a Research Fellow at RMIT University. He worked on a smart city project for the Mornington Peninsula Shire (MPS) of Victoria to optimise traffic and car parking conditions. The project involved development and integration of smart devices, e.g. parking, lighting, and amenity sensors in the high demand areas, to establish an open IoT platform. It has the ability to predict future scenarios and provide planning, routing recommendations accordingly. In June 2020, this project has won a second runner-up prize in the Nexus Smart Cities Competition organised by the City of Melbourne, Runway and ACASE.

In the past five years, he has produced 20 referred publications in high-quality journals and conferences (32 in total). He holds an h-index of 11 and his research has been cited more than 424 times (as of April 2021), showing a rapid growth since 2014.

His current research interests include the application of Artificial Intelligent and Machine Learning methods across different Combinatorial Optimisation Problems and Smart Technology.


Areas of expertise

  • Artificial intelligence
  • Machine Learning
  • Smart City
  • Optimisation problems

Contact details

Teaching responsibilities



Co-supervision of 2 masters candidates 


Turky, A., Sabar, N. R., Dunstall, S., & Song, A. (2020). Hyper-heuristic local search for combinatorial optimisation problems. Knowledge-Based Systems, 205, 106264.

Abdullah, S., Turky, A., Nazri, M.Z.A. and Sabar, N.R., 2021. An Evolutionary Variable Neighbourhood Search for the Unrelated Parallel Machine Scheduling Problem. IEEE Access, 9, pp. 42857-42867.

Sabar, N. R., Bhaskar, A., Chung, E., Turky, A., & Song, A. (2020). An adaptive memetic approach for heterogeneous vehicle routing problems with two-dimensional loading constraints. Swarm and Evolutionary Computation, 58, 100730.

Sabar, N. R., Turky, A., Song, A., & Sattar, A. (2020). An evolutionary hyper-heuristic to optimise deep belief networks for image reconstruction. Applied Soft Computing, 97, 105510.

Conference presentations

Turky, A., Rahaman, M. S., Shao, W., Salim, F. D., Bradbrook, D., & Song, A. (2020, June). Deep Learning Assisted Memetic Algorithm for Shortest Route Problems. In International Conference on Computational Science (pp. 109-121). Springer, Cham.