James Dong | Statistical modeling | Best Researcher Award

Dr. James Dong | Statistical modeling | Best Researcher Award 

Professor at University of Nebraska Medical Center, United States

Dr. Jianghu (James) Dong is a distinguished researcher and professor in the Department of Biostatistics at the College of Public Health, University of Nebraska Medical Center. His expertise lies in developing advanced statistical models for biomedical data and chronic disease research, with a strong focus on functional data analysis, survival analysis, and statistical genetics. With an extensive academic background and a wealth of experience in interdisciplinary collaborations, Dr. Dong has made significant contributions to the fields of public health, organ transplant studies, and COVID-19 research. His work has been widely published in peer-reviewed journals, making a profound impact on the statistical and medical research communities.

Profile:

SCOPUS

Education:

Dr. Dong’s academic journey began with a B.Sc. in Mathematics from Beijing Normal University in 1997, which laid the foundation for his career in statistics and biostatistics. He earned two M.Sc. degrees in Statistics: one from Renmin University of China in 2003 and another from the University of Alberta in 2005, where he honed his skills in advanced statistical modeling. Dr. Dong completed his Ph.D. in Statistics from Simon Fraser University in 2018, focusing on functional data analysis and survival models, particularly applied to biomedical data. His educational background reflects his dedication to developing statistical methods that have real-world applications in health sciences.

Experience:

Dr. Dong has built a robust career in academia and research, starting from his postdoctoral work and progressing to his current position as a professor in biostatistics. His interdisciplinary approach has led him to collaborate with professionals in medicine, public health, and engineering, working on critical healthcare problems. Throughout his career, he has worked on projects involving the analysis of complex longitudinal health data, organ transplantation outcomes, and decision-making models in chronic disease management. He has also contributed to research addressing global health challenges, such as the COVID-19 pandemic, applying his statistical expertise to develop predictive models and joint analyses.

Research Interests:

Dr. Dong’s research interests are broad and encompass several important areas of biostatistics. He specializes in functional data analysis, which allows for the analysis of data that vary over time, such as biomedical signals or patient outcomes. His work in longitudinal and survival analysis has led to the development of new methods for predicting patient outcomes in organ transplant studies and chronic diseases. In addition, Dr. Dong has a strong interest in statistical machine learning and its applications in healthcare, particularly for analyzing biomarkers and genetic data. His research extends to cost-effectiveness analysis and the creation of decision trees for health policy, making his contributions relevant to both theoretical and applied statistics.

Awards:

Dr. Dong’s research excellence has been recognized through various academic awards and grants throughout his career. While specific awards may not be listed here, his contributions to statistical modeling and health research have earned him respect and recognition within the academic and medical communities. His interdisciplinary research collaborations and impactful publications have consistently placed him at the forefront of public health research and biostatistics.

Publications:

Dr. Dong has authored numerous peer-reviewed articles, reflecting his extensive research contributions. Notable publications include:

Merani S, Urban M, Westphal S, Dong J, et al. (2023). Improved Early Post-Transplant Outcomes and Organ Use in Kidney Transplant Using Normothermic Regional Perfusion for Donation after Circulatory Death. J Am Coll Surg. Link.

Kyuhak O, Dong J, et al. (2023). Initial experience with an electron FLASH research extension (FLEX) for the Clinac system. Radiation Oncology Physics. Link.

Nyandemoh A, Anzalone J, Dong J, et al. (2023). What Risk Factors Cause Long COVID and Its Impact on Patient Survival Outcomes. arXiv. Link.

Dong J, et al. (2021). Jointly modeling multiple transplant outcomes by a competing risk model via functional principal component analysis. Journal of Applied Statistics. Link.

Du Y, Su D, Dong J, et al. (2023). Factors Associated with Awareness and Knowledge of Nonalcoholic Fatty Liver Disease. Journal of Cancer Education. Link.

Conclusion:

Dr. Jianghu Dong is an exceptional candidate for the “Research for Best Researcher Award” in biostatistics and public health. His academic background, innovative research, and contributions to the analysis of chronic diseases, transplantation outcomes, and the COVID-19 pandemic exhibit the high-level scholarship and practical impact that this award aims to recognize. His growing portfolio of applied statistical research in critical areas of healthcare showcases his potential to continue advancing the field of biostatistics, making him a fitting choice for this prestigious award.

Mohamed Eliwa | Mathematical and applied statistics | Best Researcher Award

Assoc Prof Dr. Mohamed Eliwa | Mathematical and applied statistics | Best Researcher Award

Associate Prof in Mathematical Statistics | Qassim University (Saudi Arabia) – Mansoura University (Egypt) | Saudi Arabia

Short Bio

👨‍🏫 Dr. Mohamed Saber Eliwa, also known as Eliwa, M.S., is a dedicated associate professor specializing in mathematical statistics. He serves in the Department of Mathematics at Mansoura University, Egypt, and the Department of Statistics and Operation Research at Qassim University, Saudi Arabia. Additionally, he holds an honorary research position at the International Telematic University Uninettuno in Italy. His diverse interests encompass calculus, linear algebra, probability distributions, biostatistics, and more.

Profile

SCOPUS

Education

🎓 Dr. Eliwa obtained his Ph.D. in mathematical statistics from Mansoura University, Egypt, in March 2017, under the supervision of Prof. Mir Massom from Ball State University, USA. Prior to this, he completed his pre-doctorate in 2015, his master’s in mathematical statistics and computer science in 2014, his pre-master’s in 2011, and his bachelor’s degree in 2010, all from Mansoura University, achieving “Very Good Honor” with the top rank in his class.

Experience

🔬 Since February 2022, Dr. Eliwa has been an associate professor at Qassim University. He has extensive teaching experience, including part-time online courses at the University of Nizwa, Oman, and various teaching roles at Mansoura University since 2011. He also teaches at the Misr Higher Institute for Commerce and Computers and the Nile Higher Institute for Engineering and Technology in Mansoura.

Research Interest

🔍 Dr. Eliwa’s research interests are broad and include calculus, linear algebra, probability distributions, biostatistics, applied probability, censored and recorded data, reliability analysis, applied statistics, estimation theory, and simulation. His work contributes significantly to both theoretical and applied aspects of these fields.

Awards

🏆 Dr. Eliwa has been recognized for his scientific excellence and international publishing. He is an active member of the Syndicate of Scientists in Mansoura and the Nile Sports Club. His contributions to the field have earned him positions on editorial boards and as a reviewer for numerous prestigious international journals.

Publications

📚 Dr. Eliwa has an extensive list of publications in various high-impact journals. Some notable ones include:

  1. A bivariate probability generator for the odd generalized exponential model: Mathematical structure and data fitting (2024) in Filomat, cited by articles in the same journal.
  2. Modelling veterinary medical data utilizing a new generalized Marshall-Olkin transmuted generator of distributions with statistical properties (2024) in Thailand Statistician.
  3. Different statistical inference algorithms for the new Pareto distribution based on type-II progressively censored competing risk data with applications (2024) in Mathematics.
  4. On q-generalized extreme values under power normalization with properties, estimation methods and applications to COVID-19 data (2024) in REVSTAT-Statistical Journal.
  5. A novel nonparametric statistical method in reliability theory: Mathematical characterization and analysis of asymmetric data in the fields of biological sciences and engineering (2024) in Heliyon.
  6. A discrete extension of the Burr-Hatke distribution: Generalized hypergeometric functions, different inference techniques, simulation ranking with modeling and analysis of sustainable count data (2024) in AIMS Mathematics.
  7. Failure rate, vitality, and residual lifetime measures: Characterizations based on stress-strength bivariate model with application to an automated life test data (2024) in Statistics, Optimization & Information Computing.