Mr. Luke Patterson

Luke Patterson, M.P.P., is a research associate in IMPAQ International’s Data Science and Artificial Intelligence team. Mr. Patterson is a seasoned data scientist with extensive experience employing a number of machine learning, artificial intelligence, and statistical analytic techniques for economic analyses, program evaluations, forecasting models, and simulation engines.


Mr. Patterson has applied a number of advanced data collection and extraction techniques such as web scraping, natural language processing, and network analysis to construct data sets for task automation and intensive data mining projects. Mr. Patterson is proficient with data analysis using multiple programming languages and statistical packages, including R, Stata, SAS, SPSS, Python, Visual Basic/VBA, C++, and Java. His portfolio of work includes the successful management of several highly technical projects requiring these methods and tools.


Mr. Patterson has eight years of experience working with a wide range of clients including the Agency for Healthcare Research and Quality, the Food and Drug Administration, the U.S. Department of Labor, and the Centers for Medicare and Medicaid Services.

Prior Experience

Prior to joining IMPAQ, Mr. Patterson was a policy assistant at the Consumer Financial Protection Bureau, a research intern at the Economic Security Index at Yale University, and a research intern in the U.S. Department of Labor’s Occupational Safety and Health Administration.


Mr. Patterson earned his Master of Public Policy from the University of Maryland College Park in 2017, and his Bachelor of Science from Cornell University in Industrial and Labor Relations in 2013.

Featured IMPAQ Publications and Presentations

Patterson, L. (2014, August). Impact of PRWORA and TANF on the wellbeing of low-income Americans in the 21st Century [Conference presentation]. National Association for Welfare Research and Statistics Conference. Providence, RI.

IMPAQ Presentations

Patterson, L. (2020, January). Applying machine learning methods to predict nursing home abuse [Conference presentation]. Health Datapalooza Conference.