Dr. Ahmmad believes that responsible and innovative application of statistical science has the power to transform public health. His research philosophy emphasizes transparency, rigor, and collaboration—combining classical biostatistics with modern data science tools to create interpretable and actionable knowledge. He is committed to promoting health equity and using data to inform ethical, evidence-based policies and interventions, particularly in underserved and vulnerable populations.
Education
PhD, Biostatistics and Data Science, University of Mississippi, 2022
Professional Certificate, Statistical Genetics, University of Washington, 2022
MS, Biostatistics and Data Science, University of Mississippi, 2020
MSc, Statistics Biostatistics and Informatics, University of Dhaka, 2010
BSc, Statistics Biostatistics and Informatics, University of Dhaka, 2008
Interdisciplinary and Emerging Signature Programs
Cancer Biology
Cardiovascular
Environmental & amp; Global Health
Women & Children's Health
Allergy, Immunology & Infectious Disease
Metabolic Regulation and Disorders
Research Interests
Dr. Md Roungu Ahmmad's research focuses on advancing statistical and data science methodologies to address complex questions in biomedical, epidemiologic, and public health research. His work lies at the intersection of biostatistics, machine learning, and population health, with a strong emphasis on translating data into meaningful insights for clinical and policy applications.
His current research areas include:
Cancer Epidemiology and the Exposome: Developing predictive and causal models to understand how environmental exposures interact with genetic and behavioral factors to influence cancer risk and recurrence.
Neurodevelopmental Disorders: Investigating the role of sleep, family engagement, and comorbid conditions in Autism Spectrum Disorder using nationally representative survey data such as the NSCH.
Chronic Disease Modeling: Applying joint models and survival analysis to study chronic conditions like cardiovascular disease, diabetes, and congenital disorders.
Statistical and Machine Learning Methodologies: Integrating supervised and unsupervised learning techniques (e.g., Random Forest, SVM, PCA) with traditional biostatistical models to improve early detection, risk prediction, and outcome classification.
Large-Scale Data Analysis: Utilizing population-level data (e.g., SEER, NHANES, NSCH, cBioPortal) to investigate public health trends and disparities.
Dr. Ahmmad's research is collaborative, interdisciplinary, and geared toward improving health outcomes, especially among vulnerable and underrepresented populations. His work contributes to the development of reproducible and interpretable models that inform evidence-based decision-making in public health, clinical practice, and health policy.
Memberships
Members (American Statistical Association, 2018 - Present)
Members (American Statistical Association, 2020 - Present)
Members (American Statistical Association, 2007 - Present)
Recent Publications
Md Roungu Ahmmad Modeling Multivariate Longitudinal and Multiple Time to Event Outcomeshttps://www.proquest.com/openview/8c797118ae086127aa3e62431a5062b1/1?pq-origsite=gscholar&cbl=18750&diss=y. , 2022.
Roungu Ahmmad1*, Paul A Burns2, Ashraful Alam3, Jeannette Simino1, Wondwosen Yimer1 and Fazlay Faruque2 Understanding the Impact of Social Engagement Activities, Health Protocol Maintenance, and Social Interaction on Depression During Covid-19 Pandemic Among Older AmericansNeurological Disorders. 11(1) : 1-7, 2023.
Roungu Ahmmad* , Fazlay Faruque Distribution of Obesity-related Health Outcomes across the Urban-Rural Commuting Area in Mississippi, Alabama, Louisiana, and GeorgiaDiabetes Case Reports. 8(2) : 1-8, 2023.
