Eslam seyam | Risk Stratification Analysis | Best Researcher Award

Dr. Eslam seyam | Risk Stratification Analysis | Best Researcher Award

Imam Muhammad Ibn Saud Islamic University | Saudi Arabia

Dr. Eslam Abdelhakim Kamel Seyam is an accomplished academic and researcher in the fields of insurance, risk  management, and actuarial science. He currently serves as an Assistant Professor at the Depa rtment of Insurance and Risk Management, College of Business, Imam Mohammad Ibn Saud Islamic University, Saudi Arabia. With a rich background in teaching, research, and institutional development, Dr. Seyam has contributed significantly to academia through innovative curriculum design, quality assurance processes, and applied research. His work integrates mathematical rigor, statistical modeling, and data analytics to address contemporary challenges in the insurance sector, bridging theoretical foundations with practical solutions.

Education

Dr. Seyam’s academic foundation is deeply rooted in commerce, statistics, and insurance. He earned his Bachelor’s degree in Commerce with a specialization in Statistics and Insurance, followed by a Master’s degree in Insurance focusing on corporate governance and its impact on asset and liability management in insurance companies. His doctoral studies in Insurance were part of a joint program between Zagazig University, Egypt, and Michigan State University, USA, allowing him to collaborate with esteemed professors and engage in international research environments. This educational trajectory provided him with expertise in mathematical modeling, statistical analysis, actuarial methods, and insurance economics.

Experience

Dr. Seyam’s career spans progressive academic positions, beginning as a Demonstrator and Teaching Assistant in the Mathematics, Statistics, and Insurance Department at Zagazig University. He advanced to Assistant Professor, contributing extensively to teaching undergraduate and postgraduate courses in financial mathematics, risk management, actuarial statistics, applied statistics, and reinsurance. His responsibilities extended beyond teaching, as he played key roles in performance evaluation, quality assessment, and accreditation processes. He developed innovative systems for managing academic accreditation, designed technical support frameworks, and authored institutional guidelines to streamline accreditation activities. His teaching experience covers multiple universities, including Zagazig University, Zagazig National University, Suez Canal University, the Higher Technological Institute, and Imam Mohammad Ibn Saud Islamic University.

Research Interests

Dr. Seyam’s research interests encompass a broad spectrum of insurance and actuarial science domains. He focuses on decision-making under risk and uncertainty, credibility theory, and insurance analytics, integrating statistical and machine learning methods into quantitative risk management. His work explores health and mortality modeling, auto and life insurance, annuities, loss reserves, and property and casualty insurance. He also examines the implications of insurance regulations, macroeconomic factors affecting insurance performance, and the adoption of advanced accounting standards such as IFRS 17. His interdisciplinary approach merges statistical modeling, computational tools, and economic theory to produce impactful insights for the insurance industry.

Awards and Contributions

Throughout his career, Dr. Seyam has been recognized for his scholarly contributions through publications in peer-reviewed journals and conference presentations at prestigious platforms such as the American Risk and Insurance Association Annual Meeting. His academic outputs include books on risk management, insurance mathematics, and financial mathematics with practical implementations in R programming. In addition to research excellence, he has designed educational programs, training modules, and institutional strategies for enhancing accreditation standards in higher education. He has also delivered numerous training courses in areas ranging from statistical methods and investment theory to scientific publishing and academic conference organization, contributing to professional capacity building in academia and industry.

Publications

Predicting High-Cost Healthcare Utilization Using Machine Learning: A Multi-Service Risk Stratification Analysis in EU-Based Private Group Health Insurance

Authors: Eslam Abdelhakim Seyam
Journal: Risks
Year: 2025

Modeling the Determinants of Insurtech Adoption: Evidence from the Saudi Insurance Industry

Authors: Eslam Abdelhakim Seyam
Journal: International Journal of Innovative Research and Scientific Studies
Year: 2025

Analyzing the Impact of IFRS 17 on Insurance Reserves Management: A Multifaceted Assessment

Authors: Ahmed Nasr, Mahmoud Mohamed Elsayed, Eslam Abdelhakim Seyam
Journal: Journal of Financial and Business Research (مجلة البحوث المالية والتجارية)
Year: 2024

Comprehensive Analysis of Credit Risk Determinants in the Egyptian Property and Casualty Insurance Sector: Exploring Macroeconomic, Industrial, and Company Specific Factors

Authors: Eslam Abdelhakim Seyam
Journal: Scientific Journal for Financial and Commercial Studies and Research (المجلة العلمية للدراسات والبحوث المالية والتجارية)
Year: 2024

Proposed Models for Comprehensive Automobile Insurance Ratemaking in Egypt with Parametric and Semi-Parametric Regression: A Case Study

Authors: Eslam Abdelhakim Seyam, Hussien Elsalmouny
Journal: Journal of Statistics Applications & Probability
Year: 2022

Conclusion

Dr. Eslam Abdelhakim Kamel Seyam stands out as a dedicated educator, innovative researcher, and institutional leader in insurance and risk management education. His expertise in actuarial science, combined with advanced statistical and computational skills, positions him as a key contributor to the evolving landscape of insurance research and education. Through his teaching, publications, and institutional initiatives, he continues to shape the next generation of professionals equipped to navigate the complexities of global insurance markets. His work not only advances academic knowledge but also delivers practical frameworks for addressing real-world challenges in risk management and insurance analytics.

Yuanxuan Cai | Healthcare Data Analysis | Best Researcher Award

Ms. Yuanxuan Cai | Healthcare Data Analysis | Best Researcher Award

Ms. Yuanxuan Cai | Healthcare Data Analysis Huazhong University of Science and Technology | China

Ms. Yuanxuan Cai is a dedicated doctoral candidate in Pharmacy at Huazhong University of Science and Technology, with a strong academic background in pharmacoepidemiology and drug safety. Her research focuses on adverse drug event (ADE) risk assessment, pediatric pharmacovigilance, and neural network-based drug safety models. Yuanxuan has co-authored several peer-reviewed publications and contributed to multiple national and provincial research projects. She is highly skilled in data analysis, coding, and drug safety evaluation. With a passion for mental health and healthcare policy, she aims to improve public health outcomes through data-driven decision-making in clinical pharmacology and post-marketing drug surveillance.

Professional Profile

SCOPUS

Education 

Yuanxuan Cai is currently pursuing her Ph.D. in Pharmacy at Huazhong University of Science and Technology, Her coursework includes health policy, hospital management, and metrology analysis. She earned her Bachelor of Science in Pharmacy from the same university completing courses in medical statistics, pharmacology, and pharmaceutics. Throughout her academic journey, she has consistently demonstrated excellence in both theoretical and applied pharmaceutical sciences, receiving multiple scholarships and recognitions. Her educational background equips her with a strong foundation for advanced research in clinical drug safety and public health policy.

Professional Experience 

Ms. Cai has extensive experience participating in multiple high-impact research projects supported by the National Natural Science Foundation of China (NSFC) and provincial programs. Her work includes designing project frameworks, managing large-scale ADE data, performing signal detection using Python and SQL, and applying neural network models to evaluate drug safety. She has played key roles in studies on pediatric medications, oxaliplatin toxicity, and cephalosporin use in children. Yuanxuan is proficient in real-world data analysis, database management, and pharmacovigilance system development. Her hands-on experience with interdisciplinary teams reflects a commitment to improving healthcare safety through evidence-based approaches.

Awards and Recognition 

Yuanxuan Cai has received numerous accolades for her academic excellence and research contributions. These include the prestigious National Postgraduate Scholarship (2021), a continuous Postgraduate Academic Scholarship (2020–2024), and the Merit Student Award (2021). She also secured the 3rd Prize in the “Outstanding Knowledge and Action” competition and the 2nd Prize in the Qiaokou Innovation Scholarship (2022). As an undergraduate, she was honored with the Science and Technology Innovation Scholarship twice and was named an Outstanding Graduate in 2022. These recognitions highlight her commitment to academic rigor, innovation, and impactful research in pharmaceutical sciences.

Research Skills 

Yuanxuan Cai is proficient in advanced research methodologies, particularly in pharmacoepidemiology and big data analysis. She has hands-on experience with data cleaning and signal detection in ADE databases using Python and SQL. Her skill set includes constructing neural network models and applying logistic regression for predictive drug safety assessments. She has effectively used social network analysis and the Delphi method in pharmacovigilance research. Her coding proficiency extends to R for statistical analysis. Yuanxuan’s interdisciplinary expertise enables her to manage large datasets, evaluate drug combinations, and contribute meaningfully to clinical safety decision-making and public health improvement.

Publications

Suspected Adverse Drug Reactions Related to Statins: Analysis of Spontaneous Reports in Hubei Province, China (2014–2022)
  • Authors: Wang H, Bi H, Shangguan X, Chen Z, Huang R, Chen X, Cai Y*

  • Journal: Expert Opinion on Drug Safety

  • Year: 2025 (Accepted)

Predictive Model of Oxaliplatin-induced Liver Injury Based on Artificial Neural Network and Logistic Regression
  • Authors: Huang R, Cai Y, He Y, Yu Z, Zhao L, Wang T, et al.

  • Journal: Journal of Clinical and Translational Hepatology

  • Year: 2023

Risk Factors for Oxaliplatin-induced Hypersensitivity Reaction in Patients with Colorectal Cancer
  • Authors: Song Q, Cai Y, Guo K, Li M, Yu Z, Tai Q, et al.

  • Journal: American Journal of Translational Research

  • Year: 2022

Status and Safety Signals of Cephalosporins in Children: A Spontaneous Reporting Database Study
  • Authors: Cai Y, Yang L, Shangguan X, Zhao Y, Huang R

  • Journal: Frontiers in Pharmacology

  • Year: 2021

Safety of Traditional Chinese Medicine Injection Based on Spontaneous Reporting System from 2014 to 2019 in Hubei Province, China
  • Authors: Huang R, Cai Y, Yang L, Shangguan X, Ghose B, Tang S

  • Journal: Scientific Reports

  • Year: 2021

Desmond Bartholomew | Wavelet based analysis | Young Scientist Award

Mr. Desmond Bartholomew | Wavelet based analysis | Young Scientist Award

Federal University Of Technology Owerri | Nigeria

Author  Profile

Scopus

orcid

Google Scholar

Early Academic Pursuits

Mr. Desmond Bartholomew began his academic journey with a Bachelor of Technology in Statistics from the Federal University of Technology, Owerri (FUTO), Nigeria, where he graduated with First Class honors. His undergraduate thesis addressed the Estimation of Nonorthogonal Problem Using Time Series Dataset, laying a strong foundation in quantitative modeling and time series analysis. He pursued his postgraduate studies at the University of Port Harcourt, earning a Master of Science in Statistics. His master’s thesis investigated the Effects of Maternal Education, Weight, and Age on Child Birth Weight in Nigeria, reflecting an early focus on public health data analysis.

Professional Endeavors

Mr. Bartholomew’s career reflects a dynamic mix of academic, research, and industry experience. He currently serves as an Assistant Lecturer in the Department of Statistics at FUTO, where he plays an active role in teaching, supervising projects, and supporting graduate seminars. He also holds the position of Facilitator and Trainer at the Centre of Excellence in Sustainable Procurement, Environmental and Social Standards (CESPESS), where he imparts practical statistical training for both undergraduate and postgraduate students.

Earlier, he worked as a Graduate Assistant in the same department, where he was instrumental in curriculum development, statistical consulting, and creating course materials in R and Python. In the corporate sector, Mr. Bartholomew served as a Management Information Systems Analyst at Leadway Assurance Company, contributing to data visualization, Power BI dashboard creation, and business intelligence reporting.

Contributions and Research Focus

Mr. Bartholomew’s research interests intersect statistical modeling, data science, machine learning, environmental statistics, engineering applications, and public health analytics. His published works showcase applications of wavelet transforms, Bayesian inference, experimental design, and artificial intelligence in solving contemporary issues in finance, community health, and engineering. His publications span high-impact journals including Annals of Data Science, Earth Science Informatics, and the CBN Journal of Applied Statistics.

Impact and Influence

Mr. Bartholomew’s influence is evident through his cross-disciplinary collaborations, involvement in international conferences, and active membership in professional bodies such as the Professional Statisticians’ Society of Nigeria and Data Science Network, Nigeria. His commitment to data-driven decision-making and capacity building is shaping the next generation of statistical and data science professionals in Nigeria. He also contributes to administrative and strategic planning roles at FUTO, enhancing both departmental and institutional functions.

Academic Citations

Mr. Bartholomew’s research output is indexed on Google Scholar, ResearchGate, SCOPUS, and Web of Science, providing global access to his work. His publications are increasingly cited in areas like applied statistics, machine learning, and environmental health, marking his growing reputation within the academic and professional data science community.

Technical Skills

Mr. Bartholomew possesses advanced technical competencies in R, Python, Power BI, and QlikView, with hands-on experience in data visualization, machine learning modeling, and multivariate analysis. His ability to integrate statistical knowledge with modern tools equips him to manage complex datasets and generate actionable insights. He also holds certifications in web programming, environmental standards, and data science from global platforms like DataCamp and institutions like HIIT Nigeria.

Teaching Experience

His teaching portfolio includes a wide range of undergraduate and postgraduate statistics courses, such as Descriptive Statistics, Probability Theory, Statistical Inference, Biostatistics, Experimental Design, Multivariate Methods, and Bayesian Inference. He has also developed and led modules in R programming, data cleaning, and survey analysis, often merging theoretical knowledge with real-world applications.

Legacy and Future Contributions

Mr. Bartholomew has taken up several leadership roles including serving as the President of the 2015 Alumni Association, Timetable Officer, and Website Desk Officer for the Department of Statistics at FUTO. He is deeply involved in mentoring students, shaping academic policy, and enhancing technological integration within the university. His recent recognition for the Best Research Publication in 2024 highlights his dedication to excellence in scholarly communication. With a clear vision for advancing evidence-based policymaking through data, Mr. Bartholomew is poised to contribute significantly to both national and international research landscapes.

Publication

Optimizing Nigerian Bank Lending Systems: The Power of Discrete Wavelet Transform (DWT) in Denoising and Regression Analysis

Authors: C. J. Ogbonna, C. J. Ohabuka, D. C. Bartholomew, K. E. Anyiam, I. Adamu
Journal: Annals of Data Science
Year: 2025


Modeling the Geotechnical Properties of Small Soil Samples of Oil Polluted Soil: The Power of Supervised Machine Learning Models

Authors: A. N. Nwachukwu, D. C. Bartholomew
Journal: Soil and Sediment Contamination
Year: 2024


Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series

Authors: Desmond Chekwube Bartholomew, Chrysogonus Chinagorom Nwaigwe, Ukamaka Cynthia Orumie, Godwin Onyeka Nwafor
Journal: Annals of Data Science
Year: 2024


A Monte Carlo Simulation Comparison of Methods of Detecting Outliers in Time Series Data

Authors: Desmond C. Bartholomew
Journal: Journal of Statistics Applications & Probability
Year: 2022


Classical and Machine Learning Modeling of Crude Oil Production in Nigeria: Identification of an Eminent Model for Application

Authors: C. P. Obite, A. Chukwu, D. C. Bartholomew, U. I. Nwosu, G. E. Esiaba
Journal: Energy Reports
Year: 2021