Physics-informed AI for energy security, climate resilience, and infrastructure intelligence

I am a Postdoctoral Research Fellow at the Guangdong Technion–Israel Institute of Technology (GTIIT). My current project develops low-cost multirotor drones as mobile wind-sensing platforms. The project estimates wind speed and direction from drone telemetry, attitude response, and reference calibration, then reconstructs local wind fields using Physics-Informed Neural Networks (PINNs) and flow-consistency constraints.

In parallel, I work on physics-guided forecasting for wind-energy and grid-reliability systems, and climate-risk intelligence for urban heat and infrastructure resilience. Across these directions, the common goal is to build learning systems that remain physically credible, data efficient, and useful under real measurement noise and practical deployment limits.

This work connects renewable-energy integration, aerial environmental sensing, and climate adaptation while retaining the broader relevance of Sustainable Development Goal (SDG) 7, SDG 11, and SDG 13.