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), working at the intersection of machine learning, wind-energy systems, environmental sensing, and climate resilience. My background combines academic research, teaching, and applied AI engineering, with PhD work on physics-informed and hardware-aware models for wind forecasting, grid reliability, and urban heat-risk prediction.

My current project extends this work to 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.

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.