Faculty and Staff Biography

Orimaye-Sylvester

Sylvester Orimaye

Assistant Professor, Health Data Science and Program Director
College of Global Population Health

Education

  • MONASH University, Melbourne, Victoria, Australia, Ph.D. in Information Technology (Natural Language Processing & Machine Learning)
  • Staffordshire University, Stoke-on-Trent ST4 2DE, United Kingdom, B.Sc (Hons) in Computing
  • East Tennessee State University, Johnson City, TN, United States,Master of Public Health (Biostatistics)

Specialty

Data Science, Public Health, Behavioral Health Outcomes, Machine Learning, Artificial Intelligence

Current Research

  • Sociodemographic & structural factors contributing to the association between depression & Alzheimer's disease.
  • Predicting perioperative depression and mild cognitive impairments in middle-aged adults using multimodal machine learning techniques.
  • Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers.

Vita Highlights

 

  • SAS® Global Certification Program, SAS® Certified Base Programmer for SAS®9 
  • SAS® Global Certification Program, SAS® Certified Clinical Trials Programmer using SAS®9

Interesting Facts

 

Sylvester Orimaye, Ph.D., MPH, is an Assistant Professor and the Director of the Data Science Program in the College of Global Population Health at University of Health Sciences and Pharmacy in St. Louis. Dr. Orimaye is an Applied Health Data Scientist, developing statistical models for predicting public health outcomes, focusing on rural health and health disparities among women, adolescents, aged and underserved populations. His research interest is centered around behavioral health outcomes, particularly identifying the link between depression and Alzheimer's disease. Dr. Orimaye has extensive experience in data analytics, clinical research, evaluation research and Artificial Intelligence. Notably, he has developed several data analytic strategies across various public health studies, including big data analytics in public health and optimization of data analyses pipeline on high-performance computing platforms.