• Ph.D., Research Fellow (Lv. B)
• Data Science Institute
• University of Technology Sydney
• Sydney, NSW, Australia
Danial Yazdani received his Ph.D. degree in computer science from Liverpool John Moores University, Liverpool, United Kingdom, in 2018. He is a data and system analyst and algorithm and simulation designer with 10+ years of research experience in academia. He is currently a Research Fellow at the Data Science Institute, University of Technology Sydney, Sydney, Australia. Prior to that, he was a Research Assistant Professor with the Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China. His current research interests include learning and optimization in dynamic environments. He is an invited committee member of the IEEE Task Force on Evolutionary Computation in Dynamic and Uncertain Environments and the IEEE Task Force on Large-Scale Global Optimization. He was a recipient of the 2023 IEEE CIS Outstanding PhD Dissertation Award, the Best Thesis Award from the Faculty of Engineering and Technology, Liverpool John Moores University, and the SUSTech Presidential Outstanding Postdoctoral Award from the Southern University of Science and Technology.
• Learning and Optimization in Dynamic Environments
• Evolutionary Computation
• Machine Learning
2023-06-14Accepted Article in ACM TELO
Our paper "A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems" is accepted for publication in the prestigious ACM Transactions on Evolutionary Learning and Optimization. The paper is Open Access and can be downloaded from here . The MATLAB (R2021a) source code of of the proposed algorithm can be found in here.
2022-07-23IEEE CIS Outstanding PhD Dissertation Award
I am delighted to announce that I am selected to receive the 2023 IEEE Computational Intelligence Society (CIS) Outstanding PhD Dissertation Award for my PhD thesis entitled "Particle swarm optimization for dynamically changing environments with particular focus on scalability and switching cost."
2022-06-07Accepted Article in IEEE TEVC
Our paper "Robust Optimization Over Time by Estimating Robustness of Promising Regions" is accepted for publication in the prestigious IEEE Transactions on Evolutionary Computation (Impact Factor of 16.497, CORE A*). The paper is Open Access and can be downloaded from here .