Biography

I am a Research Associate Professor (研究副教授) with the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech) (南方科技大学) since 2025. Prior to this, I was a Postdoctoral Fellow and then Research Assistant Professor at SUSTech from 2019 to 2024. I obtained my Ph.D. degree from a joint doctoral program with Beijing University of Technology, China, and The University of New South Wales, Australia, in 2019.

Academic Research

My research interests include machine learning, industrial artificial intelligence, and operations research. Currently, I focus on developing machine learning methodologies, such as self-supervised learning, reinforcement learning, large pre-trained models, and heuristics, for computational optimization and decision-making problems. The goal is to build automated and autonomous computational solutions that address modeling and optimization challenges in AI- and robotics-driven industrial engineering.

My research outcomes have been published in top-tier journals, including the Journal of Machine Learning Research, Transactions on Machine Learning Research, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Emerging Topics in Computational Intelligence, and IEEE Transactions on Parallel and Distributed Systems.

As a Principal Investigator, I have secured competitive research grants from the National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, Shenzhen Municipal Human Resources and Social Security Bureau, as well as industry partners.

Industrial Applications

My research outcomes have been successfully applied to visual–tactile perception, autonomous planning and decision-making in intelligent robotics. The resulting technologies are under commercialization via visual–tactile robotic demonstration systems and seven-axis dual-arm mobile manipulation robots.

Student Supervision

I supervise Master’s students in Computer Science and Engineering through close, hands-on mentorship rooted in academic integrity and equal collaboration. I actively engage with students in research ideation, experimental guidance, and manuscript development, supporting their transition into capable researchers and technical experts. All the students under my supervision have achieved publications in flagship IEEE Transactions and JCR Q1 journals. The team provides sustained support for academic research, international conferences, and career advancement.

Selected Publications

• AutoOpt: A general framework for automatically designing metaheuristic optimization algorithms with diverse structures

  Zhao Q, Yan B, Hu T, Chen X, Yang J, Cheng S, Shi Y*

  IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2025, 9(5): 3690-3703.

• Automated metaheuristic algorithm design with autoregressive learning

  Zhao Q, Liu T, Yan B, Duan Q, Yang J, Shi Y*

  IEEE Transactions on Evolutionary Computation (TEVC), 2025, 29(5): 2004-2018.

• Automated design of metaheuristic algorithms: A survey

  Zhao Q, Duan Q, Yan B, Cheng S, Shi Y*

  Transactions on Machine Learning Research (TMLR), 2024. https://openreview.net/forum?id=qhtHsvF5zj (Survey Certificate Award)

• PyPop7: A pure-python library for population-based black-box optimization

  Duan Q, Zhou G, Shao C, Wang Z, Feng M, Huang Y, Tan Y, Yang Y, Zhao Q, Shi Y*

  Journal of Machine Learning Research (JMLR), 2024, 25: 1-28.

• Gridless evolutionary approach for line spectral estimation with unknown model order

  Yan B, Zhao Q, Zhang J*, Zhang J A, Yao X

  IEEE Transactions on Cybernetics (TCYB), 2024, 54(2): 935-947.

• Distributed evolution strategies with multi-level learning for large-scale black-box optimization

  Duan Q, Shao C, Zhou G, Zhang M, Zhao Q, Shi Y*

  IEEE Transactions on Parallel and Distributed Systems (TPDS), 2024, 35(11): 2087-2101.

• Evolutionary robust clustering over time for temporal data

  Zhao Q, Yan B, Yang J, Shi Y*

  IEEE Transactions on Cybernetics (TCYB), 2023, 53(7): 4334-4346.

• Evolutionary dynamic multi-objective optimization via learning from historical search process

  Zhao Q, Yan B, Shi Y*, Middendorf M

  IEEE Transactions on Cybernetics (TCYB), 2022, 52(7): 6119-6130.

• Adaptive sorting-based evolutionary algorithm for many-objective optimization

  Liu C, Zhao Q*, Yan B, Elsayed S, Ray T, Sarker R

  IEEE Transactions on Evolutionary Computation (TEVC), 2019, 23(2): 247-257.

Last update: 01/2026