public policy

Reg2Vec: A Spatial Mapping of Economic Regulation

In this study, we use word and document embedding techniques from natural language processing to contextualize policymaking in U.S. federal agencies. We specifically look at a range of agencies responsible for economic regulation and train a model …

Amphan: Analyzing Experiences of Extreme Weather Events using Online Data

Data Science for Social Good, Solve for Good


Developing a word and document embedding model for speeches by the Federal Reserve

Improving Workplace Safety through Proactive Inspections

2018 Data Science for Social Good Fellowship

Natural Language Processing: An Application in Public Policy

Repository for PyCon Ireland 2018 session