Abstract: Prediction of storm-surge hazard and impacts within planning (pre-disaster), emergency management and post-disaster settings has emerged as a key priority in natural hazard risk mitigation efforts. Migration towards coasts as well as concerns related to the future effects of climate change, further stress the importance of research efforts that attempt to address this priority. Numerical advances in storm surge prediction is one of the more important such efforts. These advances have produced high-fidelity simulation models that permit a detailed representation of hydrodynamic processes and therefore support high-accuracy forecasting. Unfortunately, the computational burden of such numerical models is large, requiring thousands of CPU hours for each simulation, something that limits their applicability for hurricane risk assessment. This prohibits their broader use in regional planning or emergency response management efforts. This seminar will examine how machine learning advances have been recently promoted to address this challenge, and how the integration of such techniques can be established to better serve the needs of the relevant decision makers (planners, emergency managers). Some emphasis will be initially placed on technical aspects for integrating surrogate modeling techniques to provide surge predictions using a database of high-fidelity, synthetic storms, with the goal of maintaining the accuracy of the numerical model utilized to produce this database, while providing greatly enhanced computational efficiency. This ultimately supports great versatility in leveraging high-fidelity modeling to support regional flood studies (supported by FEMA or Army Corps of Engineers) and real-time emergency response management (supported by NOAA). The integration of dimensionality reduction techniques, multi-fidelity approaches and graph-based neural network predictions are discussed in this context. The discussion then moves on to examining how these developments and the advantages they can offer need to be promoted in order to gain acceptance by the community they intend to serve (aforementioned agencies and also planner and emergency response managers), corresponding in this case to non-technical end-users with respect to the underlying computational statistics methods. This is an essential step in the interaction between Computer Science (CS) and the people/organizations it should assist, and needs careful planning in order to promote broad adoption and actual empowerment of the end-users. Discussion focuses on the feedback loop that needs to be established in this interaction in order to achieve the desired outcome.