Minwuye Mesfin Woldeamanuel, Ph.D., is an architectural and civil engineer and applied machine learning researcher specializing in deep learning–based image segmentation for concrete monitoring and advanced cementitious materials. He earned his Ph.D. (CGPA 4.5/4.5) and M.Sc. (CGPA 4.4/4.5) in Architectural Engineering from Chosun University, South Korea, and a B.Sc. in Civil and Environmental Engineering from Addis Ababa University. His research focuses on high-accuracy semantic segmentation models for isolating fresh and early-age concrete regions from construction-site images, achieving approximately 94% accuracy, and integrating these outputs with infrared thermography and machine learning for probabilistic strength prediction. In parallel, his advanced materials research addresses microstructural enhancement of slag-based UHPC, microbial mortar and concrete, and performance improvement of recycled-aggregate concrete. Dr. Woldeamanuel has authored over six peer-reviewed journal articles, developed a copyrighted standalone software for concrete strength monitoring, and is an active member of ACI, ASTM, KCI, and RILEM. He has received multiple graduate research and excellence scholarships.