As a research professional with experience at the Environmental Modeling Lab at UPenn and in the private sector, I specialize in integrating spatial data, machine learning, and GIS to analyze human, urban, and natural systems under extreme climate events.
Over the past three years, I have conducted advanced statistical modeling in R to simulate climate vulnerability, leading regional climate action plans, and advanced environmental justice studies.
My current work involves using spatial data and machine learning to understand urban resilience and health challenges in the face of global change, including heat extremes and pandemics.
My expertise lies in integrating machine learning (Geo-AI), remote sensing and GIS techniques to explore the complex relationships between human, urban and natural systems under climate change, which aims to provide both scientific and practical frameworks for designing more resilient and sustainable communities.
My goal is to promote urban climate equity through urban planning.
How do health and social inequalities shape urban heat exposure in New Orleans?
This study examines disparities in urban heat exposure across New Orleans, identifying vulnerable populations and analyzing the health impacts. It leverages geospatial and socioeconomic data to propose equitable strategies for mitigating heat-related risks and improving community resilience.
This paper examines the relationship between land surface temperature (LST), land use, and socioeconomic variables, highlighting disparities in heat exposure. It identifies impervious surfaces and vegetation as primary drivers and explores equity-focused strategies to mitigate urban heat impacts.
Are urban greening projects making our city greener? This project applies remote sensing approaches to measure the change of green in Philadelphia Navy Yard.