Biography

EDMUND ESSAH AMEYAW, Ph.D. Assistant Professor of Data Science | Department of Pharmaceutical Sciences & Data Science Program, College of Pharmacy, Howard University

I hold a Ph.D. in Mathematics with a specialization in Biostatistics and Statistical Modeling from Howard University. I currently serve as Assistant Professor of Data Science, jointly appointed in the Data Science Program and the Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC.

My research interests span the mathematical and statistical foundations of machine learning (maximum likelihood estimation with random effects, clustered binary outcome modeling, and mixed models), AI-driven biomedical prediction in oncology, diabetes, endocrinology, and ophthalmology, large language models and generative AI in pharmaceutical sciences, and equitable AI and bias auditing in health-related predictive models.

My expertise in AI/ML and dedication to leveraging technology to promote health equity have been recognized through significant federal appointments and funding. I serve as Co-Principal Investigator under the NIH-funded AIM-AHEAD Program (1OT2OD032581), where I function as a Subject Matter Expert (SME)/Instructor/Concierge for the Data Science Training Core (DSTC), contributing to the development of programs that train researchers in AI/ML. I also serve as Co-Principal Investigator supporting the Data Science Core and Research Education Core under the NIH/NCI U54 Howard-Hopkins Comprehensive Partnerships to Advance Cancer Health (HHCAPH) Program (September 2024 – August 2029).

My contributions to the field are underscored by a growing portfolio of publications, presentations, and grants. Recent and selected works include:

• Hayes, J., Olawode, E., Andy, A., & Ameyaw, E. (in press). AI-assisted interpretation of Markush structures in pharmaceutical patents: A review of emerging tools, datasets, and challenges. Journal of Cheminformatics.
• Ameyaw, E., & Kwagyan, J. (2025). Health literacy levels and self-rated health in the state of Delaware: A cross-sectional study. Discover Social Science and Health. https://doi.org/10.1007/s44155-024-00136-7
• Adekiya, T. A., et al. (2024). PSMA-targeted combination brusatol and docetaxel nanotherapeutics for the treatment of prostate cancer. Biomedicine & Pharmacotherapy, 177, 117125.
• Mncube-Barnes, F., Ameyaw, E., et al. (2024). Prepandemic protein intake: Analysis of the National Health and Nutrition Examination Survey, 2017–2020. Current Developments in Nutrition, 8, 102727.
• Taylor-Bishop, D. C., Mncube-Barnes, F. M., Ameyaw, E. E., et al. (2023). Determinants of dental care utilization, unmet dental care need, and barriers among women of reproductive age in the United States. Oral Health and Dental Science, 7(3), 1–7.
• Taylor-Bishop, D., Mncube-Barnes, F., Ameyaw, E., & Cherry-Peppers, G. (2022). Evaluation of barriers to access treatment for gum disease: A cross-sectional study. Oral Health and Dental Sciences, 6(4), 1.
• Ameyaw, E., Adjei, E., & Kwagyan, J. (2022). Application of machine learning for predicting outcomes in a random effect clustered bivariate model. Proceedings, Joint Statistical Meeting (JSM) 2022, Washington, DC.
• Ameyaw, E., & Kwagyan, J. (2021). Numerical approximation of the marginal likelihood of random effect model for clustered bivariate binary outcomes. Presented at JSM 2021.
• Tutu, R. A., Boateng, J. K., Busingye, J. D., & Ameyaw, E. (2017). Asymmetry in an uneven place: Migrants' lifestyles, social capital, and self-rated health status in James Town, Accra. GeoJournal, 82(5), 907–921.

An integral part of my professional practice is adeptness in software tools including R, Python, SAS, WEKA, RapidMiner, Stata, and SPSS, enabling extraction of meaningful insights from complex datasets.

My commitment to scholarly excellence is demonstrated through active peer review service for BioMed Central (BMC) Women's Journal and numerous other publications, as well as my role as Topic Editor for the Research Topic "Likelihood-Based Machine Learning and Hierarchical Modeling for Clustered, Correlated, and Biomedical Data" in Frontiers in Applied Mathematics and Statistics. I hold active membership in the American Statistical Association (ASA).

https://scholar.google.com/citations?hl=en&user=nvhTE5MAAAAJ&view_op=list_works&gmla=AP6z3OZ3kYrYreqiRKSScX85nZJcA6XzJiUlpVCpEDLBMIK30FZ2jHJS2W7wCZU7Rp7awqjVrRQWiXNx_-ReMruf 

https://www.frontiersin.org/research-topics/79119/likelihood-based-machine-learning-and-hierarchical-modeling-for-clustered-correlated-and-biomedical-data

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https://www.researchgate.net/profile/Edmund-Ameyaw

https://orcid.org/my-orcid?orcid=0000-0002-6999-3077