As artificial intelligence reshapes industries from medicine to finance, the construction sector has been slow to harness its potential. Amir Hossein Oliaee is working to change that. His research sits at that frontier, building systems that bring the predictive power of modern machine learning to bear on some of construction project delivery's most persistent unsolved problems

Oliaee earned a bachelor's degree in Architectural Engineering from Ferdowsi University of Mashhad in 2018 and a master's degree in Construction and Project Management from Islamic Azad University of Mashhad in 2021, where his thesis applied artificial neural networks to superstructure cost estimation. He joined Texas A&M University's Department of Construction Science as a doctoral candidate. 

His dissertation investigates how agentic AI systems can improve the accuracy and reliability of quantity estimation in large-scale construction programs. Working closely with engineers from the Texas A&M Transportation Institute (TTI) and the Texas Department of Transportation (TxDOT), his research is grounded in the realities of how transportation projects are planned, bid, and executed, ensuring that computational advances translate into practical tools that agencies can actually deploy.

Oliaee's honors include the Hagler Institute Graduate Fellowship, the Merry K. '84 and William L. Raba '86 Graduate Fellowship, the Moss & Associates Aggie Scholarship, and the Houchins Family Publication Endowed Award. 

As a Hagler Institute Graduate Fellow, Oliaee is collaborating with distinguished visiting scholar Dr. John Schaufelberger, a leading authority on construction cost estimating and project delivery.

Alongside his research, Oliaee serves as Assistant Director of Texas A&M's NSF-funded Research Experience for Undergraduates (REU) program, where he mentors undergraduate students in applying data science methods to construction and transportation research.