Song Gao

Song Gao uses spatial statistics and machine learning to increase understanding of human mobility patterns and human-place interactions. He develops and applies geospatial data science methods for better sensing of human behavior, socioeconomic characteristics, and urban dynamics. Many domains such as business intelligence, transportation, urban planning, public health, and environmental studies rely on spatial analysis and geographic information systems (GIS). Song is passionate about solving real-world challenges by connecting theoretical data science research to advancements in the GIS industry.

With the increasing availability of mobile devices and popularity of mobile apps, users in social networking platforms actively share rich information about the places they go and the activities they engage in. Those location-based profiles provide an invaluable source of information. However, mobility data is among the most sensitive data being collected by mobile apps, and users increasingly raise privacy concerns. Song recently received an award from the American Family Funding Initiative to develop a deep learning architecture that will protect users’ location privacy while keeping the capability for location-based business recommendations.

Song especially values his collaborations with other scientists, scholars, and students on multidisciplinary and interdisciplinary projects, and learning from each other.

“Geospatial data science helps reveal underlying spatial patterns and insights, and supports knowledge discovery and decision making.”