Aaron Finley | Business Intelligence | Research Excellence Award

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Dr. Aaron Finley | Business Intelligence | Research Excellence Award

Assistant Professor at Macau University of Science and Technology | Macau

Dr. Aaron Finley is a researcher at the Macau University of Science and Technology, Macau, with expertise in Business Intelligence, data-driven policy analysis, and applied econometric modeling. His research focuses on the intersection of sustainability analytics, environmental economics, public health modeling, and advanced statistical methodologies. Dr. Finley has made significant scholarly contributions in evaluating carbon pricing instruments and their effectiveness in reducing emissions across major Asian economies, providing evidence-based insights for climate policy optimization. His work on environmental, social, and governance (ESG) factors in relation to business environments demonstrates the practical application of multivariate analysis techniques such as canonical correlation analysis in regional economic systems.

In addition to sustainability and economic modeling, Dr. Finley’s interdisciplinary research extends into public health analytics, where he applies predictive modeling, diffusion theory, and cost-effectiveness analysis to pandemic response strategies, vaccination behaviors, and lung cancer screening programs in Asia. His studies published in BMC Medicine, Journal of Thoracic Disease, Sustainable Futures, and Sustainability highlight his ability to translate complex data into actionable policy insights. Through the integration of business intelligence frameworks with health and environmental datasets, Dr. Finley’s research supports informed decision-making in government, healthcare, and sustainability-focused institutions. His growing citation impact reflects the relevance and applicability of his work across multiple high-impact domains.

Profiles: Scopus | Google Scholar

Featured Publications

  • Finley, A., He, W., Huang, H., & Hon, C. (2024). Analyzing the effectiveness of carbon pricing instruments in reducing carbon emissions in major Asian economies. Sustainability, 16(23), 10542.
    Citation Count: 5

  • Finley, A., He, W., Huang, H., & Hon, C. (2025). A canonical correlation analysis on the relation of environmental, social, governance (ESG) on business environment (paying taxes) in South China. Sustainable Futures, 10, 101369.

  • Zhang, X., Shi, W., Liu, Z., Finley, A., Cen, K., Xie, Z., Yang, P., Li, H., & Leong, U. (2025). Adaptive Fourier decomposition analysis of different pandemic stages in South Korean cities: Policies and trends. Journal of Thoracic Disease, 17(6), 3516–3531.

  • Zhang, T., Wang, Y., Chen, X., Yang, X., Zhang, L., Bazzi, N., Bai, L., & Finley, A. (2025). Cost-effectiveness of risk model-based lung cancer screening in smokers and nonsmokers in China. BMC Medicine, 23(1), 315.

  • He, W., Wu, J., Chen, C. H., Finley, A., Wang, H., Huang, H., Ng, C., & Chui, T. (2025). Predicting COVID-19 vaccination timing by integrating the theory of planned behavior and the diffusion of innovations: A cross-sectional survey in Macao, China. Journal of Thoracic Disease, 17(5), 2813.

jizhou Cao | Data-Driven Decision Making | Best Scholar Award

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Mr. jizhou Cao | Data-Driven Decision Making | Best Scholar Award

Student | Xinjiang University | China

Mr. Jizhou Cao is a dedicated academic and researcher currently serving at Xinjiang University. With a background in civil engineering and machine learning, he has significantly contributed to the understanding of reinforced concrete (RC) column shear behaviour, integrating advanced machine learning techniques into structural engineering. His work has explored the initial failure process in RC columns and prediction methods for shear capacity, demonstrating a unique synergy between civil engineering and machine learning. Mr. Cao’s research has been published in well-respected journals, furthering the application of machine learning to solve real-world engineering problems.

Profile

Scopus

Education

Mr. Cao earned his master’s degree from Hainan University, where he gained a solid foundation in civil engineering. He continued his academic journey by pursuing further studies at Xinjiang University, which has fostered his research interests in the intersection of civil engineering and machine learning. His educational path reflects a blend of practical expertise and theoretical understanding, particularly in the realm of structural analysis and innovative technologies such as machine learning.

Experience

With years of academic and research experience, Mr. Cao has engaged in multiple projects that apply cutting-edge technologies to civil engineering problems. His work has focused on developing predictive models for the shear capacity of RC columns and understanding the failure processes in concrete structures using machine learning techniques. He has also been involved in consultancy projects, contributing his expertise to real-world applications. His professional journey highlights his commitment to advancing both the scientific understanding and practical application of structural engineering.

Research Interest

Mr. Cao’s primary research interests lie in the integration of machine learning with civil engineering, particularly in structural analysis and the failure mechanisms of reinforced concrete structures. His research aims to bridge the gap between computational techniques and practical engineering solutions, with a special focus on the prediction of shear failure in RC columns. His work seeks to improve the accuracy of structural safety evaluations and enhance the resilience of concrete structures under various loading conditions.

Award

Mr. Cao has been recognized for his contributions to the field of civil engineering and machine learning. His research has garnered attention from leading academic institutions, with multiple nominations for prestigious awards such as the Young Scientist Award and the Excellence in Innovation Award. These accolades reflect his impactful contributions to advancing engineering practices, particularly in the realm of structural safety and the application of machine learning.

Publications

Mr. Cao has authored several influential articles, contributing to the academic discourse on machine learning applications in civil engineering. Some of his key publications include:

“Exploring the initial state of the shear failure process in RC columns based on machine learning,” Journal of Structural Engineering, 2024.

“Prediction of shear capacity of RC columns and discussion on shear contribution via the explainable machine learning,” Structural Safety Journal, 2023. These works have been cited by numerous researchers, highlighting the significance of his research in the field.

His publications have addressed critical aspects of structural engineering and have demonstrated the potential of machine learning to revolutionize the field.

Conclusion

Mr. Jizhou Cao’s work stands as a testament to the potential of machine learning in reshaping civil engineering practices. His academic background, coupled with a strong research focus on shear failure prediction in RC columns, underscores his commitment to advancing both theoretical and applied knowledge in structural engineering. As he continues to explore innovative solutions through machine learning, Mr. Cao is poised to make lasting contributions to the safety and efficiency of civil infrastructure, enhancing the way engineers approach complex structural challenges. His dedication to research and innovation makes him a valuable asset to both academia and the engineering community.