Jiayong Liang is the Senior Remote Sensing Scientist of Cloud to Street, where she develops multi-sensor, multi-modal data fusion algorithms to enhance flood mapping. She is a Ph.D. candidate of Geography at the Ohio State University, and received her Master degree from College of Environmental Science and Forestry, State University of New York, and Bachelor degree from Sun Yat-sen University. Trained as a geographer, she has accumulated expertise in remote sensing and geospatial analytics, utilizing which she strives to pursue her passion for flood loss mitigation. She grew up in Guangzhou, China, one of the most vulnerable places to flood damage, according to a 2013 World Bank report. Realizing how such a report helped her hometown manage flooding, she decided to devote herself to producing more science-based decision-support information to help vulnerable populations facing similar challenges across the world. Before joining Cloud to Street, she has been working with multi-modal data to map flooding in an NSF funded project, and now she is developing new data fusion algorithms to integrate in situ measurements, flood simulations, and remote sensing data (see publications).