Machine learning algorithms to predict development indicators

Seth Goodman

AidData Research Scientist

Seth is a Research Scientist at AidData. In this role, he designs and develops tools and data infrastructure that improve AidData's capacity to provide and analyze data. He specializes in building data processing and management systems, designing and operationalizing novel research methods, and developing applications that utilize William and Mary's High Performance Computing cluster. Seth developed GeoQuery, AidData’s free spatial data platform, which enables individuals and organizations without significant computing power or data science expertise to freely find and aggregate satellite, economic, health, conflict, and other geospatial data into a single, simple-to-use spreadsheet file. He also supports AidData’s work using machine learning methods to predict development indicators based on satellite and survey observations. Seth previously served as a Data Engineer at AidData. Seth completed his BS and MS in Electrical Engineering at Villanova University and a Ph.D. in Computational Geography at William & Mary.

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The GeoField community of practice  is a space for impact evaluators, remote sensing experts, and field organizations to brainstorm and problem solve about how Earth observation can lead to program insights that solve real world, long-running challenges of climate adaptation and agricultural development.
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