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Matt is a data analyst with expertise in data management and causal inference. He uses software such as Amazon Web Services, SQL, and R to handle datasets with millions to hundreds of millions of datapoints. He is also proficient at machine learning in Python and many statistical models in R, such as synthetic control. Matt often uses these tools to answer questions pertaining to education, housing, transportation, and other policy areas.

At Westat Insight, Matt works on projects for the U.S. Department of Labor and the National Science Foundation. This work involves web scraping and classifying search results using Python.

Earlier, Matt attended Georgetown University’s McCourt School of Public Policy, where he was a Massive Data Institute Scholar. He worked on topics ranging from helping the Bureau of Labor Statistics detect outliers in research import and export indexes to assessing the causal relationship between zoning changes and neighborhood change. Matt also interned at the Brookings Institution’s Metropolitan Policy Program, where he worked on the relationship between housing and transportation access in the DC area.