Researcher

Dr Ali Ahrari

My Expertise

Engineering design by optimisation

Evolutionary algorithms

Multimodal optimisation

Bi-level optimisation

Biography

Ali Ahrari received his Bachelor's and Master's degrees in mechanical engineering from the University of Tehran in 2006 and 2009, respectively. He received his Ph.D. in mechanical engineering from Michigan State University (MSU) in 2016 and subsequently worked as a research associate at MSU until June 2018. He has won some international competitions on optimization such as "Competition on niching methods for multimodal optimization" in 2016...view more

Ali Ahrari received his Bachelor's and Master's degrees in mechanical engineering from the University of Tehran in 2006 and 2009, respectively. He received his Ph.D. in mechanical engineering from Michigan State University (MSU) in 2016 and subsequently worked as a research associate at MSU until June 2018. He has won some international competitions on optimization such as "Competition on niching methods for multimodal optimization" in 2016 and 2020, which was held at CEC and GECCO conferences, as well as international student competition on structural optimization (ISCSO) in 2017 and 2018 and received a 1000 euro cash prizes. Since July 2018, he is working at UNSW-Canberra as a research associate in the Canberra Evolutionary Optimization group. His current research concentrates on evolutionary dynamic and noisy optimization with a focus on multimodal and multiobjective problems. 


My Grants

$3718 from the UNSW high-performance computing (HPC) resource allocation scheme


My Qualifications

Doctor of Philosophy in Mechanical Engineering (December 2016), Michigan State University, East Lansing, MI, USA (GPA: 4.00/4)
Master of Science in Mechanical Engineering-Applied Design (September 2009), University of Tehran, Tehran, Iran
Bachelor of Science in Mechanical Engineering-Solid Mechanics (September 2006), University of Tehran, Tehran, Iran


My Awards

2020            Winner of competition on niching methods for multimodal optimisation at IEEE WCCI/CEC'2020 (500 USD cash prize from IEEE) and GECCO'2020

2018            Winner of 2018 ISCSO competition on structural optimisation (1000 € prize)

2017            Winner of GECCO'2017 competition on multi-modal optimisation

2017             Winner of the 2017 ISCSO competition on structural optimisation among 60+ participants (1000 € prize)

2016             Winner of GECCO'2016, CEC'2016 competitions on multimodal optimisation

2016             Passed the FE/EIT mechanical engineering exam in the state of Michigan, USA

2013-2016   Graduate Office Fellowship (This fellowship was awarded multiple times)

2012             Richard H. Brown – ME Endowment Award

       


My Research Activities

I carry out research on developing optimization algorithms, both classical methods and evolutionary algorithms, and their specialization for engineering problems. Engineering Design by optimization is my favorite subject. I have also some experience in machine learning, specifically Decision trees and neural networks.  Currently, I am doing research on “Reactive planning under disruptions and dynamic changes”, which is funded by the Australian Research Council. In the past, I have worked on evolutionary optimization-related projects funded by General Motors Company, Ford Motor Company, REWIND, NSF, and a few other projects that were not funded.

 

We may have a funded position for a graduate student (PhD or Master’s by research). If you have a background, expertise, and interest in design optimisation, evolutionary algorithms, or even machine learning, feel free to email me with your resume included. We are in Canberra, Australia.


My Research Supervision


Areas of supervision

Theory and application of evolutionary optimisation and swarm intelligence methods

Optimal design and multidisciplinary design optimisation


Currently supervising

1 PhD student at SEIT, UNSW-Canberra

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