Samuel Takyi, PhD

Samuel holds a Ph.D. and ME in Civil Engineering from Florida State University, where he focused his graduate work on applying machine learning techniques to address transportation and infrastructure challenges. He also earned a graduate certificate in engineering data analysis and holds a bachelor’s degree in geomatic engineering. His doctoral research used machine learning and aerial/satellite imagery to detect and assess roadway impacts following hurricanes and employed gravity-based models to measure spatial accessibility to critical facilities. Samuel’s work integrates advanced analytics with practical transportation applications to support disaster response, infrastructure resilience, and accessibility planning.

While at Florida State University, Samuel contributed to several Florida Department of Transportation and U.S. Department of Transportation-funded projects. These efforts included leveraging computer vision and aerial imagery to detect and map roadway features, supporting asset management, and enhancing roadway safety. He brings expertise in machine learning, computer vision, GIS, and transportation data analysis, and is passionate about developing innovative, data-driven solutions to improve mobility and infrastructure systems. Outside of work, Samuel enjoys listening to music and spending time with his family.