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About Michelle

Dr. Michelle (Shelby) Bensi performs research related to natural hazards risk assessment, risk-informed decision-making, and infrastructure resilience.

Biography

Dr. Michelle (Shelby) Bensi is an assistant professor in the Department of Civil and Environmental Engineering at the University of Maryland. She is also a core faculty member of the University of Maryland Center for Disaster Resilience and affiliated faculty member of the University of Maryland Center for Risk and Reliability. Dr. Bensi’s research centers on the theme of application of probabilistic risk assessment concepts and tools to problems involving infrastructure and engineered systems. Dr. Bensi focuses primarily on topics related to the probabilistic assessment of natural hazards (with particular emphasis on the seismic, coastal, inland flooding, and severe weather hazard groups), risk-informed applications and decision-support systems, and infrastructure resilience. Dr. Bensi seeks innovative, cross-disciplinary solutions though application of machine learning and data analytic methods and by applying lessons and experiences across hazards groups.

Prior to joining the University of Maryland faculty, Dr. Bensi served as an engineer at the United States Nuclear Regulatory Commission (NRC) where she worked to resolve unique and challenging problems in reactor safety arising from the Fukushima Dai-ichi reactor accidents. Dr. Bensi was responsible for site-specific technical reviews for new and operating nuclear reactors, performed research and technical review activities related to probabilistic hazard assessment, and also led development of agency regulatory and staff guidance and a number of policy papers. Dr. Bensi chairs the project team responsible for the ANS/ASME Standard for external flooding probabilistic risk assessment at nuclear power plants. In that role, Dr. Bensi led the development of the first comprehensive and extensive revision of that Standard. Dr. Bensi holds a Ph.D. in Civil Engineering with Designated Emphasis in Computational Science and Engineering from the University of California, Berkeley.

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