Menglu Liang | Bayesian methods | Best Researcher Award

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Dr. Menglu Liang | Bayesian methods | Best Researcher Award

Assistant Professor, University of Maryland, United States

Dr. Menglu Liang is an Assistant Professor of Biostatistics at the University of Maryland, with a strong background in biostatistics, epidemiology, and public health. Her journey into the field of biostatistics began during her studies at Johns Hopkins University, where she first encountered survival analysis in large cohort studies. Dr. Liang’s academic and research career has focused on developing advanced statistical models for real-world health challenges, particularly in the areas of cardiovascular disease, cancer, and public health. She has received extensive training at prestigious institutions, including Beijing University of Chinese Medicine, Peking University, Johns Hopkins University, the University of Minnesota, and Penn State University. Her work has resulted in numerous impactful publications in top-tier journals, and she is highly regarded for her interdisciplinary collaborations with clinicians, epidemiologists, and statisticians to address pressing health issues.

Profile

Education

Dr. Menglu Liang completed her undergraduate studies in Preventive Medicine at Beijing University of Chinese Medicine, graduating in 2011. She further pursued a Master’s degree in Public Health (MPH) from Peking University, Beijing, in 2014. Her passion for applying statistical methods in public health led her to Johns Hopkins University, where she earned a Master of Science in Epidemiology in 2016. Dr. Liang’s academic path continued with a Master of Science in Statistics from the University of Minnesota in 2019, and she earned her PhD in Biostatistics from Penn State University in 2023. Her doctoral research, titled “Modeling and Dynamic Prediction for Recurrent Time-to-event Data with Competing Risks,” focused on advanced Bayesian techniques for survival analysis and statistical modeling. Dr. Liang’s education across multiple disciplines and prestigious institutions has provided her with a comprehensive foundation in biostatistics, epidemiology, and public health.

Experience

Dr. Menglu Liang has built an impressive academic and professional career, culminating in her current position as an Assistant Clinical Professor of Biostatistics at the University of Maryland. Prior to this, she gained invaluable experience as a Graduate Assistant at Penn State University (2019–2023) and the University of Minnesota (2018–2019), where she developed advanced statistical models and conducted research on cardiovascular disease and clinical epidemiology. Dr. Liang’s early career included roles at Johns Hopkins University, where she worked as a Data Analyst (2016–2017) and a Graduate Assistant (2015–2016), contributing to significant research in epidemiology and biostatistics. Throughout her career, she has demonstrated a commitment to collaborative research and statistical consulting, working closely with clinicians and researchers to tackle complex health issues. Dr. Liang has also served as a mentor to students and researchers, providing guidance in statistical modeling, data analysis, and scientific writing.

Awards and Honors

Dr. Menglu Liang has received numerous awards and honors that recognize her outstanding contributions to biostatistics and public health research. In 2022, she was awarded the prestigious Travel Award by the International Chinese Statistical Association, highlighting her commitment to advancing statistical methods in health research. She was also the recipient of the Statistical Significance Award in the JSM Statistical Significance Competition, which acknowledges innovative research in statistical methodology. Dr. Liang’s scholarly achievements have been recognized through her publications in top-tier journals, where her work on dynamic prediction models and Bayesian statistical methods has garnered significant attention. She has presented her research at various national and international conferences, demonstrating her leadership in advancing the application of statistical techniques to public health and clinical research. Her consistent recognition underscores her academic excellence and her ability to contribute to high-impact research in the field.

Research Focus

Dr. Menglu Liang’s research focuses on the application of advanced statistical methods to public health, epidemiology, and clinical research. Her primary areas of interest include survival analysis, Bayesian hierarchical modeling, and the development of dynamic prediction models for recurrent time-to-event data with competing risks. Her work often integrates complex statistical methods with real-world data to address key health challenges, particularly in cardiovascular disease, cancer, and public health policy. Dr. Liang is particularly interested in the intersection of statistical modeling and clinical research, where she collaborates with clinicians and epidemiologists to improve predictive models and decision-making processes. She has also applied Bayesian network meta-analysis techniques in dental research and developed spatial-temporal models to study the effects of extreme heat on health outcomes. Dr. Liang’s research is driven by the goal of making meaningful contributions to public health through the application of innovative statistical techniques to real-world problems.

Publication Top Notes

  1. Association of a Biomarker of Glucose Peaks, 1,5-Anhydroglucitol, With Subclinical Cardiovascular Disease 🩺📊
  2. Tackling Dynamic Prediction of Death in Patients with Recurrent Cardiovascular Events 💓🔍
  3. Bayesian Network Meta-Analysis of Multiple Outcomes in Dental Research 🦷📈
  4. A Spatial-Temporal Bayesian Model for Case-Crossover Design with Application to Extreme Heat and Claims Data 🌡️📉

 

 

Majid Hashempour | Statistics | Best Scholar Award

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Assist. Prof. Dr Majid Hashempour | Statistics | Best Scholar Award

Academic faculty of the university,University of Hormozgan,Iran

Majid Hashempour is an Assistant Professor in the Department of Statistics, Faculty of Basic Sciences, at Hormozgan University, Iran. With a deep expertise in statistical inference, he has contributed significantly to the development of statistical models, focusing primarily on cumulative residual entropy (extropy) and its applications in various fields. He completed his Ph.D. in Statistics at Ferdowsi University of Mashhad in 2016, following his M.Sc. in Mathematical Statistics from Shiraz University. Dr. Hashempour has a rich academic career, teaching at university level and collaborating with researchers worldwide. His scholarly work focuses on the creation of dynamic statistical models for analyzing inaccuracy, reliability, and survival data. He is widely published in leading statistical journals and continues to make notable contributions to statistical theory and applications.

Profile

Orcid

Google Scholar 

Scopus

 

Education 

Dr. Majid Hashempour holds a Bachelor’s degree in Statistics from Ferdowsi University of Mashhad (1997-2002), an M.Sc. in Mathematical Statistics from Shiraz University (2002-2004), and a Ph.D. in Statistics with a focus on Statistical Inference from Ferdowsi University of Mashhad (2012-2016). His doctoral thesis was titled “Dynamic Version of Weighted Cumulative Residual Extropy and Its Applications.” During his academic journey, he specialized in the study of extropy, a measure of uncertainty and inaccuracy, which he later applied to dynamic systems, survival analysis, and statistical modeling. His education laid the foundation for his expertise in theoretical and applied statistics, contributing significantly to his current role as an assistant professor and a researcher in statistical inference.

Experience 

Dr. Majid Hashempour is currently serving as an Assistant Professor at Hormozgan University, where he has been teaching and conducting research since his appointment. He is involved in the Department of Statistics, Faculty of Basic Sciences, and has a reputation for his rigorous research in statistical inference, particularly in areas concerning extropy, residual analysis, and reliability modeling. Dr. Hashempour’s academic career has been marked by a commitment to educating the next generation of statisticians while pursuing significant scholarly research. He collaborates with a network of national and international researchers, contributing to numerous high-impact journal publications. Beyond teaching, Dr. Hashempour is active in guiding graduate students and conducting workshops in advanced statistical methods. His experience spans both academic and applied statistics, including the development of statistical tools for real-world applications like survival data analysis and risk assessment.

Research Focus 

Dr. Hashempour’s research primarily revolves around the field of statistical inference, with a particular focus on extropy-based models and their dynamic versions. Extropy, a measure of inaccuracy and uncertainty, serves as a core concept in his work, with applications ranging from reliability analysis to survival modeling. He is particularly interested in dynamic versions of cumulative residual extropy and past inaccuracy measures, exploring their properties, applications, and estimation techniques. His work often involves advanced statistical methods for analyzing order statistics, failure-time data, and reliability systems. Additionally, he explores the development of new lifetime distributions, such as the two-parameter extensions of the half-logistic family, and investigates their theoretical and practical properties. Dr. Hashempour’s research also includes applications in various industries, such as aircraft maintenance data, where statistical models are applied to assess risks and optimize decision-making processes. His contributions continue to shape statistical theory and practice.

Publications 

  1. Dynamic Version of Weighted Cumulative Residual Extropy and Its Applications 📊📈
  2. Dynamic Version of Past Inaccuracy Measure Under PRHR Model Based on Extropy 🔍💡
  3. Extropy-Based Dynamic Cumulative Residual Inaccuracy Measure: Properties and Applications 🧠🔢
  4. Extropy: Dynamic Cumulative Past and Residual Inaccuracy Measures with Applications 📉🧮
  5. On Weighted Version of Dynamic Cumulative Residual Inaccuracy Measure Based on Extropy 📊📝
  6. Modified Cumulative Extropies of Doubly Truncated Random Variables 🔢🔒
  7. A New Two-Parameter Extension of Half-Logistic Distribution: Properties, Applications and Different Methods of Estimations 📉🎲
  8. On the Dynamic Residual Measure of Inaccuracy Based on Extropy in Order Statistics 📚📊
  9. Extropy-Based Inaccuracy Measure in Order Statistics 🧮📈
  10. A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data 🌧️🔬
  11. Extropy: Characterizations and Dynamic Versions 📊🔍
  12. Residual Inaccuracy Extropy and Its Properties 📉🔎
  13. A New Measure of Inaccuracy for Record Statistics Based on Extropy 📑📐
  14. A Weighted Topp-Leone G Family of Distributions: Properties, Applications for Modelling Reliability Data and Different Methods of Estimation 📊⚙️
  15. Weighted Cumulative Past Extropy and Its Inference 🧮🔍
  16. On Interval Weighted Cumulative Residual and Past Extropies 📉🔢
  17. On Dynamic Cumulative Past Inaccuracy Measure Based on Extropy 🧠📊
  18. An Extended Type I Half-Logistic Family of Distributions: Properties, Applications and Different Methods of Estimations 📊🎲
  19. On Weighted Cumulative Residual Extropy: Characterization, Estimation and Testing 🧮📑
  20. A New Two-Parameter Lifetime Distribution with Flexible Hazard Rate Function: Properties, Applications and Different Methods of Estimations ⏳🔢
  21. Mixture Representations of the Extropy of Conditional Mixed Systems and Their Information Properties 🧠📚
  22. Dynamic Systems with Baseline Exponential Distribution Based on Sequential Order Statistics Under a Power Trend for Hazard Rates 📉⚡
  23. Statistical Inference on the Basis of Sequential Order Statistics Under a Linear Trend for Conditional Proportional Hazard Rates 🧑‍🏫📊
  24. Bayesian Inference on Multiply Sequential Order Statistics from Heterogeneous Exponential Populations with GLR Test for Homogeneity 🧑‍🏫📐
  25. Evidences in Lifetimes of Sequential R-out-of-N Systems and Optimal Sample Size Determination for Burr XII Populations 📈🧮

Erandi Nanayakkara | Biostatistics | Excellence in Research

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Dr. Erandi Nanayakkara | Biostatistics | Excellence in Research

Researcher, Vcitoria University of Wellington, New Zealand

Dr. Erandi Nanayakkara is a dedicated research statistician and lecturer with expertise in statistical modeling, data science, and teaching. With a Ph.D. in Statistics from Victoria University of Wellington, New Zealand, she specializes in microsimulation models for health research, particularly lung cancer. She has a strong background in education, mentoring, and research supervision, actively engaging students and helping them develop practical skills. Dr. Nanayakkara is known for her adaptability, innovative approach to problem-solving, and effective communication. She has collaborated with diverse teams and contributed to a variety of projects, from epidemiology to business analytics. Her professional journey includes teaching, research, and administrative duties across various universities, with a commitment to student success and academic development. She is proficient in multiple programming languages and statistical tools, making her a versatile contributor to interdisciplinary research.

Profile

Google Scholar

Education

Dr. Erandi Nanayakkara holds a Ph.D. in Statistics from Victoria University of Wellington, New Zealand, where she focused on the development and Bayesian calibration of a microsimulation model for lung cancer. Her dissertation explores the natural history, screening, and treatment of lung cancer in the New Zealand context. Prior to her Ph.D., Dr. Nanayakkara completed a Master of Philosophy (M.Phil.) in Mathematical Modelling from the University of Kelaniya, Sri Lanka, with a thesis on the stochasticity in SIR models for epidemic diseases. She also holds a B.Sc. Special Degree (First Class Honors) in Mathematics, Statistics, and Computer Science from the University of Ruhuna, Sri Lanka. Her academic background reflects a strong foundation in both theoretical and applied statistics, particularly in the fields of health modeling, epidemic disease transmission, and statistical data analysis.

Experience

Dr. Erandi Nanayakkara has extensive experience in teaching, research, and academic administration. She worked as a Teaching Assistant at Victoria University of Wellington, where she fostered active student engagement and supported over 60 students each trimester in undergraduate statistics courses. As a Graduate Researcher, she developed a lung cancer microsimulation model incorporating screening and treatment components, using programming languages such as R, SQL, and C++. Dr. Nanayakkara also served as a Lecturer in Statistics at the Open University of Sri Lanka, where she delivered both classroom-based and online lectures, developed curriculum, and mentored students. Her role involved designing and delivering engaging content, moderating papers, and evaluating student performance. Earlier in her career, she worked as a Research Assistant at the University of Kelaniya, where she performed advanced data analysis and contributed to research reports. She has also lectured at other institutions, enhancing her cross-cultural communication and time management skills.

Research Focus

Dr. Erandi Nanayakkara’s research primarily focuses on the development and application of statistical models to real-world problems, especially in health and epidemiology. Her Ph.D. research centered on the development of a microsimulation model for lung cancer, which incorporates Bayesian calibration to reflect the specific context of New Zealand. She has also worked extensively on stochastic models, particularly in the context of epidemic diseases, focusing on SIR (Susceptible, Infected, Recovered) models and their applications in understanding disease transmission dynamics. Additionally, Dr. Nanayakkara’s research explores survival analysis, particularly in customer attrition and insurance policy persistence. Her work integrates advanced statistical techniques with computational modeling to generate actionable insights in public health, epidemiology, and business analytics. She is also passionate about mentoring students, promoting academic growth, and bridging the gap between theoretical and applied statistics in various fields of research.

Publication Top Notes

  1. “A calibrated model for lung cancer natural history: the case for New Zealand” 🫁📊
  2. “Simulation Model Development for Lung Cancer Screen Detection: A case study for New Zealand” 🧑‍⚕️💻
  3. “Analysing the customer attrition using survival techniques” 📉👥
  4. “Development and Bayesian calibration of a microsimulation model for lung cancer: natural history, screening and treatment” 🫁🔬
  5. “Stochasticity in SIRV Models for the Transmission of Epidemic diseases” 🦠📚
  6. “Analyzing persistence of life insurance policy using survival model” 💼💡
  7. “Stochasticity in SIRV Models with Vaccination and Reversion for the Transmission of Epidemic diseases” 🦠💉