Conference: Data-driven modeling and prediction of rare and extreme events

This workshop will convene experts in rare and extreme event detection and characterization representing a broad range of application domains and disciplines, including statistics, machine learning, applied mathematics, operations research, space weather, materials science, and climate modeling. Unanticipated rare and extreme events can be catastrophic in the domains of damaging high energy solar flares, sudden fuselage failure, and extreme terrestrial storms, causing significant loss of life and livelihood. Progress in modeling and predicting of such high risk events will require novel multidisciplinary approaches and this is what this conference seeks to uncover. It also seeks to catalyze new collaborations across these methodological and applicational domains. The goal is of convening experts with complementary backgrounds is to identify key challenges and opportunities, with an emphasis on methodologies that may be leveraged across domains. The focus on data-driven methods encompasses recent efforts in machine learning, including physics-informed machine learning and generative models, and how such tools may advance rare and extreme event forecasting. The agenda will also include physics-driven approaches, including simulations, both as a source of fundamental insights into the modeling of rare events and as a mechanism for generating data to complement real-world data used to train data-driven models. This two-day workshop will be held at the University of Chicago on November 20-21, 2024. It will feature lectures from experts across the spectrum of disciplines listed above, panel discussions, poster sessions, and lightning talks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.