Song Gao
Professor Of GIScience at International Journal of Geographical Information Science
Santa Barbara, California, United States
Summary
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Senior🎓
Top SchoolSong Gao is a Professor of GIScience with a decade of experience specializing in GeoAI, spatial data science, and geospatial big data analytics, currently based in Santa Barbara. He progressed through academia at the University of Wisconsin–Madison from assistant to full professor, blending rigorous research in spatio-temporal analysis, trajectory mining, and human mobility with applied work in spatial databases and semantics. An active journal editor and long-time peer reviewer for PNAS, he bridges theoretical spatial modeling and practical GIS platforms like ArcGIS, informed by early industry internships at Esri and Apple Maps. Song is also engaged in the international GIS community as a board member of CPGIS, reflecting a commitment to mentoring and global collaboration beyond his lab. Unusually for an academic, he maintains a hands-on developer mentality—his GitHub identity “GIS Guy” underscores ongoing engagement with geospatial tooling and prototype-driven R&D.
11 years of coding experience
9 years of employment as a software developer
B.S, Geographic Information Systems and Remote Sensing, B.S, Geographic Information Systems and Remote Sensing at Beijing Normal UniversityM.S., Cartography and Geographic Information Systems, M.S., Cartography and Geographic Information Systems at Peking UniversityDoctor of Philosophy (Ph.D.), Department of Geography, emphasis on Geographic Information Science, Doctor of Philosophy (Ph.D.), Department of Geography, emphasis on Geographic Information Science at UC Santa Barbara
Chinese, English
Stackoverflow
11reputation
3kreached
1answer
0questions
Github Skills (20)
crowd9
mobility9
lstm8
datasets8
reproducible-research8
deep-learning6
gis6
postgis6
community-detection6
arcgis6
hadoop6
clustering5
machine-learning5
graph3
geospatial3
Programming languages (3)
Github contributions (5)
gissong/ADCN
Feb 2020 - Jan 2021
Contributions:34 commits, 33 pushes, 2 branches in 10 months
GeoDS/IntraCounty-Mobility-SEIR
Jan 2021 - Nov 2022
A human mobility flow-augmented stochastic SEIR-style epidemic modeling framework is developed, which combines with data assimilation and machine learning to reconstruct the historical growth trajectories of COVID-19 infection within a county.
Contributions:47 commits, 47 pushes, 1 branch in 1 year 10 months
machine-learning
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