Multimodal Machine Learning and Climate Change Adaptation

Climate change is intensifying the frequency and severity of natural disasters across the globe, making societal adaptation an urgent priority. This talk delves into two key avenues where Machine Learning (ML) can play a transformative role in addressing climate adaptation challenges. The first part introduces a multimodal machine learning framework designed for natural disaster prediction. This flexible framework integrates diverse data types—such as images, text, and tabular data—enabling predictions across both short-term and long-term timeframes. The second part explores how ML-predicted risks can be integrated into catastrophe insurance pricing through Robust Optimization. Speakers

• Cynthia Zeng, Assistant Professor of Technology, Operations and Statistics, The Stern School of Business at NYUAD