Rubing Li | Business | Young Scientist Award

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Dr. Rubing Li | Business | Young Scientist Award

PhD student/Lecturer, Wuhan Univerity/Jianghan University, China

Rubing Li is a dedicated accounting researcher with expertise in corporate tax avoidance and corporate governance. She earned her Ph.D. in Accounting from Wuhan University in 2016 and participated in a Ph.D. Cotutelle program at Macquarie University, Australia. She holds an MSc in Economics, Accounting, and Finance from the University of Bristol and a Bachelor of Commerce in Accounting from Griffith University. Rubing has served as a tutor at Jianghan University and has received multiple academic excellence awards. She actively participates in international conferences, including the European Accounting Association and AFAANZ. Her research explores the regulatory impacts on corporate tax strategies. She is an ACCA-certified professional and a member of the Golden Key International Honor Society. Beyond academia, Rubing contributes to community service and volunteer initiatives.

Publication Profile

Orcid Profile

🎓 Education 

Ph.D. in Accounting (2016) – Wuhan University, China. Ph.D. Cotutelle Program (2017) – Macquarie University, Australia. MSc in Economics, Accounting & Finance (2015) – University of Bristol, UK (Merit). Bachelor of Commerce (2013) – Griffith University, Australia (GPA: 6.06/7, Major: Accounting)

💼 Experience 

Tutor (2015-2016) – Jianghan University, Wuhan, China Taught Accounting Principles to undergraduate students. Developed course materials and guided students in financial reporting

🏆 Awards & Honors 

Griffith University Academic Excellence Award (2011-2012, 2012-2013) – Top 5%. Golden Key International Honor Society – Lifetime Member. Association of Chartered Certified Accountants (ACCA) – Exemptions from F1-F9. National Study Abroad Fund Award (2017). Paper selected for the European Accounting Association (2019)

🔬 Research Focus

Corporate Tax Avoidance – Investigating the causes and economic consequences. Corporate Governance – Analyzing financial transparency and compliance. Regulatory Impact on Taxation – Studying China’s tax policies through quasi-natural experiments

Publication Top Notes

  • Title: Regional Administrative Monopoly and Corporate Tax Avoidance: Evidence from a Quasi-Natural Experiment in China
  • Author: Rubing Li
  • Journal: Asia-Pacific Journal of Accounting & Economics
  • Publication Date: January 30, 2025
  • DOI: 10.1080/16081625.2025.2459700
  • ISSN: 1608-1625 / 2164-2257

 

Abouzar Bazyar | Finantial Mathematics | Best Researcher Award

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Dr. Abouzar Bazyar| Finantial Mathematics | Best Researcher Award

IRAN, Persian Gulf University Iran

Abouzar Bazyari is a dedicated academic at Persian Gulf University in Bandar Būshehr, Iran. Since 2011, he has been part of the Department of Statistics within the Faculty of Intelligent Systems Engineering and Data Science. Abouzar’s research focuses on risk models, ruin probabilities, and statistical methods. With numerous publications in top journals, he has contributed extensively to the fields of statistical modeling and insurance risk processes. His academic work emphasizes understanding the behavior of risk models under various conditions, exploring solutions for complex statistical challenges, and enhancing theoretical frameworks in risk management.

Publication Profile

Scopus

Orcid

🎓 Education

Abouzar Bazyari holds advanced degrees in statistics and related fields, showcasing his deep expertise in mathematical and statistical theories. His academic journey has been focused on building a comprehensive understanding of statistical models and their applications in risk analysis. He is committed to enhancing statistical methods for real-world problem-solving, particularly in areas such as insurance and financial modeling. Abouzar’s education underscores his commitment to contributing to statistical sciences through research and practical applications.

💼 Experience

Since 2011, Abouzar Bazyari has been a distinguished faculty member at Persian Gulf University, teaching and conducting research in the Department of Statistics. His role involves designing courses, mentoring students, and collaborating on research projects. His academic experience is rooted in the analysis of statistical processes related to risk management. With years of expertise in both teaching and research, Abouzar has established a strong reputation in the field, particularly through his work on ruin probabilities and statistical modeling. His professional contributions span both education and impactful research publications.

🏆 Awards & Honors

Abouzar Bazyari has received numerous accolades for his contributions to statistical sciences, including recognition for his work on risk modeling and ruin probabilities. His research articles are frequently published in prestigious journals such as the Journal of Statistical Planning and Inference and Communications in Statistics. These honors highlight his impact in the statistical community, particularly in the development of theories related to risk management and statistical inference. Abouzar’s work continues to be highly regarded for advancing the understanding of statistical challenges in risk models.

🔬 Research Focus

Abouzar Bazyari’s research primarily revolves around the study of ruin probabilities and statistical risk models. His work explores the intricacies of risk processes, including discrete-time models, insurance risk processes, and capital injections. Abouzar aims to enhance the theoretical understanding of risk management through the application of advanced statistical techniques. His recent publications delve into compound binomial models, state-space models, and Markov chain properties, all while seeking optimal solutions for insurance and financial risk management. His research continues to influence both theoretical advancements and practical applications in statistical modeling.

Publication Top Notes

  • On the Ruin Probabilities for a General Perturbed Renewal Risk Process
    Journal of Statistical Planning and Inference, 2023 | Preprint
    Contributors: Abouzar Bazyari

 

  • On the Ruin Probabilities in a Discrete Time Insurance Risk Process with Capital Injections and Reinsurance
    Sankhyā A: The Indian Journal of Statistics, 2023 | Preprint
    Contributors: Abouzar Bazyari

 

  • Inequalities on the Ruin Probability for Light-Tailed Distributions with Some Restrictions
    Communications in Statistics – Theory and Methods, 2023-11-22 | Journal article
    Contributors: Abouzar Bazyari

 

  • Ruin Related Quantities in a Class of State-Space Compound Binomial Models
    Journal of Statistical Modelling: Theory and Applications, 2023-07-11 | Journal article
    Contributors: Abouzar Bazyari

 

  • Optimal Ruin Probabilities in the Excess Loss Reinsurance Model
    Journal of Statistical Sciences, 2023-06-15 | Journal article
    Contributors: Abouzar Bazyari

 

  • Infinite Time Ruin Probability in the Risk Model of Excess Loss Reinsurance
    Journal of Statistical Sciences, 2022 | Journal article
    Contributors: Abouzar Bazyari

 

  • Ruin Probabilities for Two Risk Models with Asymptotically Independent and Dependent Classes
    Journal of the Iranian Statistical Society, 2022-08-22 | Journal article
    Contributors: Abouzar Bazyari

 

  • On the Evaluation of Ruin Probabilities in a Generalized Dual Binomial Risk Model Using Markov Property
    Communications in Statistics – Theory and Methods, 2022-07-12 | Journal article
    DOI: 10.1080/03610926.2022.2093910
    Contributors: Abouzar Bazyari

 

  • Ruin Probabilities in a Discrete-Time Risk Process with Homogeneous Markov Chain
    Journal of Statistical Modelling: Theory and Applications (JSMTA), 2022-07-05 | Journal article
    Contributors: Abouzar Bazyari

 

  • The Bayesian Wavelet Thresholding Estimators of Nonparametric Regression Model Based on Mixture Prior Distribution
    Journal of Statistical Sciences, 2021 | Journal article
    Contributors: Abouzar Bazyari

 

  • An Asymptotic Two-Sided Test in a Family of Multivariate Distribution
    Journal of Statistical Theory and Applications, 2020 | Journal article
    DOI: 10.2991/jsta.d.200511.001
    Contributors: Abouzar Bazyari