Benjamin Laufer ’19


Operations Research and Financial Engineering

Project Title

Using Machine Learning to Ensure Clean Air Act Compliance: Optimizing EPA’s Inspection Strategy

Presentation Link

View Benjamin's Presentation

I worked with the EPA’s data-management team on using statistics and machine-learning methods to help the air-compliance branch with inspection targets. Currently, EPA inspectors use personal discretion in deciding which facilities to check for illegal air pollution-emission practices. Using an array of historical EPA compliance data, I aimed to devise a data-driven, optimal strategy for Clean Air Act inspections. With R programming, I implemented a Random Forest Machine Learning algorithm to classify suspect facilities nationwide. I hope my work will help the EPA identify and regulate sites that pose significant environmental risk to American citizens. My summer internship experience taught me a lot about American environmental regulations, and helped me hone my data-analytics and programming skills. Back at Princeton, I am continuing coursework related to mathematical modeling, big data and environmental policy. Additionally, I am continuing this project under the guidance of Robert Vanderbei, Professor of Operations Research and Financial Engineering.

Internship Year


Project Category

Resilient Cities


U.S. Environmental Protection Agency, New York


Daniel Teitelbaum, Environmental Policy and Data Analyst, EPA