Primary Convener:
Ahmed Mustafa, Urban Systems Lab, New York University

Convener:
Timon McPhearson, Urban Systems Lab, New York University
Ashish Shrestha, Urban Systems Lab, New York University
Madhavi Jain, Urban Systems Lab, New York University

Early Career Convener:
Sally El Hajjar, The New School

Chair:
Ahmed Mustafa, Eugene Lang College, The New School
Timon McPhearson, Urban Systems Lab, New York University
Ashish Shrestha, Urban Systems Lab, New York University

Modeling climate hazards in urban environments requires advanced expertise, significant computational resources, and access to localized data—factors that can make it prohibitively expensive and inaccessible for many communities. These models often rely on high-emission computing workflows and are not always designed to produce actionable insights. To drive effective resilience and adaptation strategies, modeling frameworks must not only integrate physics-based and AI approaches but also be scalable, low-emission, and embedded within practical risk assessment and decision-making processes.This session welcomes contributions for novel methods to leverage machine learning for extreme heat and urban flood modeling (pluvial, fluvial, and coastal); multi-hazard and compounding risk assessment in urban systems; and innovative data processing and computational techniques that address scalability and challenges with natural hazard modeling.

Index Terms
1942 Machine learning
1952 Modeling
4301 Atmospheric
4303 Hydrological

Neighborhoods:
1. Science Nexus