Research Fellow (Urban Microclimate Modeling and Graph Neural Networks)

Model Development:

• Design and implement a hybrid physics-AI spatiotemporal modeling framework for translating satellite-derived Land Surface Temperature (LST) data into high-resolution ambient air temperature maps.
• Develop and train Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) architectures to model complex spatial and temporal dependencies in urban microclimate data, incorporating attention mechanisms, continual learning (e.g., Elastic Weight Consolidation), and feature attribution methods (SHAP, Integrated Gradients).

Data Collection and Validation:

• Design and execute field validation campaigns across diverse HDB precincts and test-bed sites (NUS campus, SIT Punggol Digital District), coordinating sensor deployment, drone-based thermal measurements, and mobile sensing data from SBS Transit bus networks.
• Process and integrate high-resolution satellite LST imagery ...