Mahsa Hedayati is a Ph.D. student in Building Technology with a minor in Data Analytics and Machine Learning at Georgia Tech. Her research explores the intersection of artificial intelligence and human-centered design in decarbonizing the built environment and achieving net-zero emissions. She is currently developing machine learning frameworks to advance personalized thermal comfort prediction by integrating sensor-based and occupant-centric data with adaptive Personal Comfort Systems (PCS), optimizing comfort while reducing energy use and carbon emissions.

Before joining Georgia Tech, Mahsa worked as a technical designer at Perkins&Will, ISTUDIO, and KGD in Washington, D.C., specializing in high-performance building design, energy simulations, and LEED-certified projects. She holds a Bachelor of Architectural Engineering from BIHE in Iran and a Master of Science from Virginia Tech, where she received the Outstanding Master’s Degree Student Award. Her honors include the TechMade Fellowship, CMAA Scholarship, and the 2021 AIA COTE Top Ten Award, followed by serving as a jury member for the 2022 AIA COTE Top Ten competition.