As the Gulf Coast heads into the most active stretch of the Atlantic hurricane season — August through September — forecasters warn the region could face heightened storm activity this year, fueled by warm ocean waters and a changing climate. Rice University researchers are at the forefront of using artificial intelligence (AI) to address these urgent climate challenges. The Advancing AI for Climate Risk and Urban Resilience (AI4UrbanResilience) research group, led by civil and environmental engineering scholar James Doss-Gollin and supported by the Ken Kennedy Institute, unites climate scientists, engineers and AI experts to develop transformative solutions for:
- Extreme weather modeling and disaster response
- Climate hazard prediction and risk assessment
- Flood modeling and mitigation strategies
- Urban energy and transportation resilience
While traditional hazard models offer valuable insights, they are often limited in resolution, efficiency and adaptability to different regions. AI4UrbanResilience integrates AI and machine learning with physics-based models to produce open-source, high-resolution and computationally efficient tools for managing complex, interconnected systems under extreme weather stress. Key focus areas include:
- Synthetic Hazard Generation: Producing large AI-generated datasets of realistic synthetic weather patterns for urban downscaling.
- Infrastructure Systems Response: Assessing hazard impacts on critical systems with AI- and optimization-enhanced methods.
- Multiscale Earth Observation and Data Assimilation: Merging diverse datasets to improve model accuracy and enable real-time initialization.
- Trustworthiness and Validation: Ensuring transparency and reliability of physics-informed AI through rigorous evaluation.
This work aligns with Rice’s initiative to generate sustainable futures, positioning the university as a nexus for AI-powered climate adaptation. By connecting domain experts with AI researchers, the AI4UrbanResilience group aims to help communities in Houston and worldwide prepare for, withstand and recover from climate-driven disasters.
The following experts are available for media interviews:
- James Doss-Gollin’s work enables AI methods for climate modeling and risk management under extreme weather. He combines physical and statistical methods to understand challenges with a changing climate, including extreme rainfall, urban flooding and energy resilience to inform effective decision-making. Doss-Gollin leads the AI4UrbanResilience cluster at Rice.
- Sylvia Dee is a climate scientist whose research explores the influence of changing climate patterns on extreme weather, including drought, flood and heat, especially in the Gulf Coast region. She uses climate models to evaluate and understand Earth's changing water cycle and future risks for human and natural systems.
- Avantika Gori is an expert on hurricane climatology as well as coastal hazards due to changing climate conditions and urban development. Her research explores hurricane risks related to extreme winds, storm surge, heavy rainfall and coastal flooding using a combination of physical models, probabilistic methods, AI and machine learning.
- Arlei Silva is a computer scientist whose research focuses on developing algorithms and models to learn from complex datasets. His work on climate forecasting informs disaster response and emergency management while addressing changing climate impacts on urban infrastructure.
- Jamie Padgett is a civil engineer who applies probabilistic methods for risk assessment and risk mitigation of structures and infrastructure systems when exposed to multiple hazards such as hurricanes and other extreme weather events.
- Noemi Vergopolan is a computational hydrologist whose research aids actionable decision-making for monitoring and forecasting hydrological extremes (droughts and floods) and their impacts at the local scale. She uses physics-informed AI and satellite observations for advancing water and food security.
- Sang-ri Yi is a civil engineer who works toward building resilient urban infrastructure by developing new computational methods for uncertainty quantification, primarily for complex, large-scale simulation models for natural hazards including wind risks.
- Xinwu Qian is a civil engineer who specializes in mathematical modeling and data-driven methods for transportation system challenges, particularly relevant for evacuation planning.
To schedule an interview with Rice’s experts, contact media relations specialists Alex Becker at alex.becker@rice.edu or Silvia Cernea Clark at silviacc@rice.edu.
AI4UrbanResilience is one of the interdisciplinary research clusters supported by the Ken Kennedy Institute to bridge departmental expertise and advance responsible AI and computing at Rice University.