Iris Tien


Dr. Iris Tien is Williams Family Associate Professor in the School of Civil and Environmental Engineering at the Georgia Institute of Technology. Tien joined the faculty 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.

Dr. Tien’s research is in probabilistic methods for modeling and reliability assessment of civil infrastructure systems. Her research leverages her unique interdisciplinary expertise encompassing traditional topics of civil engineering, sensing and data analytics, stochastic processes, probabilistic risk assessment, and decision making under uncertainty. Her work on interdependent infrastructure systems modeling and analysis has twice won 1st Place Paper Awards in resilient critical infrastructure. Dr. Tien has been selected by the National Academy of Engineering to participate in three Frontiers of Engineering Symposia. She was also selected to organize the session on Resilient and Reliable Infrastructure at the U.S. Frontiers of Engineering Symposium; and speak on Community Resilience at the National Academies Frontiers of Science, Engineering, and Medicine Symposium.

Dr. Tien’s work has been published in journals ranging from engineering to medicine, and is funded by both state and national agencies, including the National Science Foundation, Department of Homeland Security, Georgia Department of Transportation, U.S. Department of Transportation, and National Institute of Standards and Technology. Dr. Tien was awarded the prestigious Early Achievement Research Prize by the International Association for Structural Safety and Reliability (IASSAR), and her published work has been selected as Editor’s Choice selections in both the ASCE Journal of Infrastructure Systems and the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems.

  • Probabilistic methods for modeling and reliability assessment of civil infrastructure systems
  • Risk analysis
  • Structural and infrastructure health monitoring
  • Sensing and data analytics, signal processing, machine learning
  • Stochastic processes
  • Decision making under uncertainty
  • Smart cities, interdependent systems, and resilient communities

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