Dr. Chris Li Zhang

Dr. Chris Li Zhang, a senior research associate at IMPAQ International, is a data scientist and applied microeconomist. He applies machine learning, advanced mathematics, and artificial intelligence to answer policy questions in workforce and health. He is currently leading the development of a large-scale microsimulation tool for U.S. worker paid leave for the U.S. Department of Labor (DOL) and the development and implementation of analytical solutions to inform policies for the Office of Compliance and Enforcement at the Food and Drug Administration (FDA) Center for Tobacco Products.


Dr, Zhang’s analytical expertise includes machine learning, statistical and econometric modeling, natural language processing, simulation, complex system modeling, geospatial analysis, and high-performance computing. The application of these methods in Dr. Zhang’s work includes development of large-scale simulation models, development of an optimal tobacco retailer inspection scheme based on Bayesian hierarchical risk models, automation of document management systems based on web scraping, automation of PDF content extraction from survey forms, development of real-time media monitoring platforms, and implementation of crime forecasting algorithms based on self-exciting point process models.


Dr. Zhang has worked closely as data scientist and project manager with the Office of Science and the Office of Compliance and Enforcement at the FDA Center for Tobacco Products, and the DOL Chief Evaluation Office. He has extensive experience in project management, technical working group coordination, product prototype user testing, material dissemination, and presenting to a nontechnical audience.

Prior Experience

Prior to joining IMPAQ, Dr. Zhang was a visiting researcher at the U.S. Bureau of Labor Statistics and worked at Hong Kong Mortgage Corporation, an agency that promotes homeownership in Hong Kong.


Dr. Zhang earned his Ph.D. in economics from the University of Virginia in 2016. He received his M.A. in economics from the University of Toronto in 2008 and his B.Sc. in mathematics and economics from the University of Toronto in 2007.

Featured IMPAQ Publications and Presentations

Zhang, C. (2017, November). The Effect of Casinos on the Non-gambling Economy: Evidence from Nationwide Household Spending Data. Presentation at the Southern Economic Association, Tampa, FL

IMPAQ Publications and Presentations

Zhang, C.L. (2020). Impact of paid leave programs on low-wage Workers, evidence from the Worker PLUS model [Conference presentation]. Association for Public Policy Analysis and Management Fall Research Conference, Washington, DC.

Zhang, C.L. (2019, December). USDOL worker leave simulation model beta version demonstration. A webinar for invited stakeholders [Presentation]. U.S. Department of Labor, Washington, DC.

Zhang, C.L. (2019, November). Using machine learning to predict physician program participation in the presence of class imbalance [Presentation]. U.S. Food and Drug Administration Innovate Today Summit Tech Talkx Event. Washington, DC.

Heuser, A., Huynh, M., & Zhang, C.L. (2018, July). A system dynamics model for tobacco research [Presentation]. Joint Statistical Meeting, Vancouver, BC.

Nanda, N., Corea, C., Patterson, L., Poe-Yamagata, E, Mian, P., & Zhang, C.L (2018). Feasibility study and evaluation of non-traditional occupation demonstrations: Final evaluation report. Washington, DC: U.S. Department of Labor, Employment & Training Administration.

Previous Publications and Presentations

Zhang, C.L. (2015, November). Household consumption smoothing between monthly housing payments [Presentation]. Southern Economic Association Conference, New Orleans, LA.

Zhang, C.L. (2015, September). Household consumption smoothing between monthly housing payments [Presentation]. Bankard Public Economics Workshop, University of Virginia, Charlottesville, VA.

Zhang, C.L. (2014, July). The effect of casinos on household consumption [Presentation]. Consumer Expenditure Survey Microdata Users’ Workshop, Washington, DC.