Artificial Intelligence
Our AI cluster develops advanced data-driven approaches to understand, predict, and attribute climate and environmental health risks across scales. By integrating satellite retrievals, ground measurements, and multi-scale model outputs, we generate high-resolution insights into environmental exposures, aerosol–cloud interactions, and food security. We leverage AI to bridge processes from urban to global systems, enabling attribution of key drivers—including climate variability, urbanization, and air pollution—while improving forecasts, early warning, and climate-resilient public health strategies.
Research Focus
- Developing AI models to predict climate extremes, air quality, urban heat, and aerosol–cloud interactions, while attributing their underlying physical and anthropogenic drivers.
- Advancing satellite retrievals and integrating multi-source data (satellite, in-situ, and models) to produce consistent, high-resolution environmental and urban datasets.
- Quantifying environmental exposures, including heat stress, air pollution, and urban climate effects, and assessing their impacts on human health and food security.
- Using interpretable and physics-informed AI to understand climate–air quality–health interactions, including aerosol–cloud–radiation processes and urban influences.
- Building AI-enabled early warning, forecasting, and decision-support systems with improved uncertainty quantification for climate resilience and public health planning.
Asst Prof. Tao Huang.jpg?sfvrsn=61d99442_0 "Assoc.Prof. Cheng LONG")
Assoc Prof. Cheng Long