
Education
Ph.D., Civil Engineering, Michigan Technological University
M.S., Data Science, University of New Haven
M.S., Construction Management, Syracuse University
Mini MBA, University of Tehran, Iran
B.S., Civil Engineering, Sharif University of Technology, Iran
About Rei
Dr. Reihaneh (Rei) Samsami is the Tagliatela Family Endowed Assistant Professor of Construction Engineering and Management and Coordinator of the M.S. in Construction Engineering and Management program at the University of New Haven. She brings more than 10 years of experience spanning academia, transportation infrastructure, construction technology, and applied artificial intelligence.
Her research focuses on the integration of Artificial Intelligence (AI), computer vision, unmanned aerial systems (UAS), Building Information Modeling (BIM), Digital Twins, and data-driven decision support for infrastructure inspection, construction monitoring, and asset management. She works closely with Departments of Transportation (DOTs), industry partners, and public agencies to develop practical solutions that improve safety, efficiency, and decision-making throughout the project lifecycle.
Selected Publications
Samsami, R., & Kang, S. J. (2026). Lightweight YOLO-Based Detection of Rooftop Thermal Bridges Using Uncrewed Aerial System Thermal Imagery: Comparative Evaluation of Modern YOLO Architectures. ASCE OPEN: Multidisciplinary Journal of Civil Engineering.
Razi, N., Badhan, S. J., & Samsami, R. (2026). Artificial Intelligence (AI) in Construction Management (CM): A Systematic Review of Models and Methods. Buildings.
Badhan, S. J., & Samsami, R. (2025). Artificial Intelligence (AI) in Construction Safety: A Systematic Literature Review. Buildings, 15(22), 4084.
Pokhrel, R., Samsami, R., Elmi, S., & Brooks, C. N. (2024). Automated Concrete Bridge Deck Inspection Using Unmanned Aerial System (UAS)-Collected Data: A Machine Learning Approach. Eng, 5(3), 1937-1960.
Research Interests
- Remote Construction Inspection
- Digital Project Delivery and Digital Twins
- Highway Asset Management
- Building Information Modeling
- UAS Data Collection and Analysis
- Automated Progress Monitoring
- Automated QC/QA of Heavy Highway Projects
