Dr. Ali Ahrari is a Lecturer at the School of Systems and Computing, University of New South Wales, Canberra. He obtained his PhD in Mechanical Engineering from Michigan State University in 2016, achieving a GPA of 4.00/4.00. Prior to that, he earned his MSc in Mechanical Engineering-Applied Design and BSc in Mechanical Engineering-Solid Mechanics from the University of Tehran in 2009 and 2006, respectively. Following his doctorate, Ahrari served as a professional aide at Michigan State University until June 2018. He then joined UNSW Canberra as a research associate/fellow in the Canberra Evolutionary Optimization group from July 2018 to May 2022. Subsequently, he was a research fellow at the University of Sydney for 15 months before returning to UNSW Canberra in August 2023 in his current lecturing role.

Ahrari's research focuses on evolutionary algorithms, particularly multimodal and multi-objective optimization, multidisciplinary design optimization, swarm intelligence, constrained optimization, dynamic optimization, surrogate-assisted optimization, robust optimization, and noisy optimization. He has produced 28 peer-reviewed publications, 25 as first author, including four in the IEEE Transactions on Evolutionary Computation: "Revisiting Implicit and Explicit Averaging for Noisy Optimization" (2023), "Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations" (2022), "Adaptive Multilevel Prediction Method for Dynamic Multimodal Optimization" (2021), and "A Novel Parametric Benchmark Generator for Dynamic Multimodal Optimization" (2021). Notable earlier publications include "Grenade Explosion Method—a Novel Tool for Optimization of Multimodal Functions" (2010, Applied Soft Computing, 176 citations) and "Multimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations" (2017, Evolutionary Computation, 101 citations). His work has earned him the Australian Research Council Discovery Early Career Researcher Award (DECRA, 2023, AUD 329,278) for developing an efficient computational solver for complex engineering problems. Ahrari has also won numerous international competitions, such as the IEEE CEC and GECCO multimodal optimization competitions (2016, 2017, 2020, 2022) and ISCSO structural optimization competitions (2017, 2018). He chairs the IEEE CIS Task Force on Multi-modal Optimization, serves on the editorial board of Applied Soft Computing, and leads organization of major competitions including the GECCO 2024 Benchmarking Niching Methods for Multimodal Optimization.

Professional Email: a.ahrari@unsw.edu.au