- Ph.D., Civil Systems Engineering, University of California, Berkeley, 2014
- M.S., Civil and Environmental Engineering, University of California, Berkeley, 2010
- B.S., Civil and Environmental Engineering, University of California, Berkeley, 2008
Iris Tien joined the faculty in the School of Civil and Environmental Engineering at the Georgia Institute of Technology in 2014 after receiving her Ph.D. in Civil Systems Engineering from the University of California, Berkeley, in 2014. She received her M.S. in Civil and Environmental Engineering in 2010, and graduated High Honors with a B.S. in Civil and Environmental Engineering and a Minor in English in 2008 from UC Berkeley.
Tien has a unique interdisciplinary background that encompasses traditional topics of civil engineering, sensing and data analytics, signal processing, machine learning, probabilistic risk assessment, stochastic processes, and decision making under uncertainty. For her research work in developing Bayesian network methods for system modeling and reliability analysis, Tien was awarded the Paper Award from the ASCE Engineering Mechanics Institute Probabilistic Methods Committee in 2013. In addition, Tien has conducted research on wireless sensor networks in the monitoring of structures under seismic hazard as well as in gait analysis for the diagnosis of Parkinson’s disease.
Tien’s work has been published in journals ranging from engineering to medicine, and is funded by both state and national agencies, including the Center for Information Technology in the Interest of Society, Georgia Department of Transportation, and National Science Foundation. Tien is a recipient of the Regents’ and Chancellor’s Scholarship, University of California Chancellor's Fellowship for Graduate Study, National Science Foundation Graduate Research Fellowship, and National Science Foundation Engineering Innovation Fellowship.
- Probabilistic methods for modeling and reliability assessment of civil infrastructure systems
- Stochastic processes
- Risk analysis
- Structural and infrastructure health monitoring
- Sensing and data analytics, signal processing, machine learning
- Decision making under uncertainty