Academic Credentials
  • Ph.D., Mechanical Engineering, University of Maryland, Baltimore County, 2023
  • M.S., Mechanical Engineering, University of Maryland, Baltimore County, 2013
  • B.S., Mechanical Engineering, University of Maryland, College Park, 2008
Professional Affiliations
  • National Society of Black Engineers (NSBE)
  • American Society of Mechanical Engineers (ASME)

Nathanael Seay earned his Ph.D. in Mechanical Engineering from the University of Maryland Baltimore County, focusing on improving the durability of artificial joints. He has extensive expertise in engineering software and hardware, including the innovative application of machine learning and artificial intelligence across diverse industries. At the University of Maryland Baltimore County, he developed novel solutions for enhancing the durability of artificial joints through groundbreaking research in titanium carbide surfaces and plasma-enhanced chemical vapor deposition.

His research developed a novel titanium carbide surface on a titanium alloy substrate using plasma-enhanced chemical vapor deposition (CVD). This work aimed to address the significant issue of wear in joint replacements by investigating how micro-textured surfaces could extend their lifespan. His analysis of the interface revealed crucial tribological features, such as crystalline channels with nanocrystalline characteristics. Through extensive mechanical testing and sophisticated modeling techniques like molecular dynamics and finite element analysis, Nathanael's study offered valuable insights into the adhesive properties and resistance to crack propagation at the interface, suggesting a potential breakthrough in joint replacement technology. His expertise spans advanced material characterization, computational modeling, and surface engineering, with proficiency in material science and computational tools essential for high-performance applications across diverse industries.

Nathanael is proficient in a range of computational models and experimental techniques aimed at improving system reliability and operational efficiency. His skills include deploying complex machine learning models to predict material behaviors and system failures. He is skilled in software and programming languages relevant to machine learning, such as TensorFlow and Keras, and engineering analysis tools like MATLAB and SolidWorks. His technical acumen extends to material analysis ensuring a robust application of his multidisciplinary skills to practical engineering challenges. Nathanael is proficient in using material science tools like SEM, XRD, AFM, and nanoindentation.