Abstract:
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Statistical models are playing an increasingly important role in risk analysis. However, with the increased ability to collect and analyze real-time data, the field of risk analysis is entering a new phase based on real-time probabilistic risk analysis. I will illustrate this new paradigm in the context of managing risks associated with complex systems, including space shuttles, illicit nuclear materials crossing national boundaries, and dangerous medical drugs. Analyses depend all the way from small sets of data to terabytes of data on millions of containers entering the U.S. to medical information about drugs. In this talk, I will discuss a number of challenges in creating unified decision systems from a risk-analysis perspective, share some of the successes and corresponding challenges in data analytics, and propose new methods based on supervised and nonsupervised learning. I also will discuss the criticality of such risk analysis on public policy and global resilience.
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