DR. HANNUUN EADIELA BINTI YAACOB
Department of Decision Science
Faculty of Business and Economics
hannuun_yyum.edu.myView CV | |
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Biography | |
Dr. Hannuun Yaacob currently holds the position of a Senior Lecturer in the Department of Decision Science within the Faculty of Business and Economics at the University of Malaya. She earned her Ph.D. in Statistics from the University of Sheffield, UK. Her primary research interests encompass Bayesian statistics and biostatistics. In her previous research project, she focused on statistical genetics, where she developed a Bayesian statistical model to identify potential causal genes for breast cancer. Presently, her research revolves around preference-based measures and health-related quality of life, with a keen interest in the application of statistics in social science research. Dr. Hannuun's work effectively bridges the gap between statistical analysis and the human experience, significantly contributing to our understanding of preferences, aspirations, and the tools used for measurement. An enthusiastic user of the R software, she has also served as a guest speaker at the R conference in 2022 and continues to primarily employ R software in both her research and teaching endeavors. Beyond her academic pursuits, Dr. Hannuun finds solace in reading and traveling, embracing a balanced and holistic life. Her unwavering dedication to advancing social science research serves as an inspiration to emerging scholars in the field, leaving an indelible mark on the academic community. |
Publication
Finance
Project Title | Progress | Status |
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Optimizing Resource Allocation and Training Opportunities in RUKA: A Data-Driven Approach to Enhance Doctor Training and Patient Care |
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This information is generated from Research Grant Management System |
Prevalence of Preterm Birth and Perinatal Outcomes in a Tertiary Hospital in Malaysia
Bayesian multivariant fine mapping using the Laplace prior
The utility of the Laplace effect size prior distribution in Bayesian fineāmapping studies
The utility of the Laplace effect size prior distribution in Bayesian fine-mapping studies
Utilizing the information from GWAS data to inform priors in Bayesian fine-mapping association studies