Workshop — Modern Statistical and Machine Learning Approaches for High-Dimensional Compound Spatial Extremes

t is undisputed that the Earth is experiencing a period of changing climate, and due to climate change, it is expected that extreme events (e.g., heat waves, floods, storm surges, hurricanes, wildfires, earthquakes) may be more frequent, severe, and interdependent in the future, thus leading to larger impacts. An important step towards resilient extreme events monitoring and evaluating the hazards to infrastructure is to develop novel and rigorous mathematical and statistical framework that can identify areas vulnerable to compound extreme phenomena. In the current Big Data era, the difficulty of this task is often exacerbated with the data being increasingly massive and complex.